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TAUNTON — Chief Edward J. Walsh reports that the Taunton Police Department is investigating after a 12-year-old boy was seriously injured after being struck by car on Friday night.
About 6:30 p.m. on Friday, the Taunton Police and Fire Departments responded to the area of 43 Plain St. for a report of a motor vehicle crash involving a pedestrian.
Upon arrival, officers located the boy, who was suffering from serious injuries. The boy was transported by Brewster Ambulance to Hasbro Children’s Hospital in Providence.
The initial investigation indicates that the boy was crossing the street with a family member when he slipped and fell backward into the path of an oncoming vehicle. The boy was struck by a 2006 Nissan Sentra traveling westbound The driver remained at the scene and was cooperative with police. The Taunton Police and Fire Departments were assisted at the scene by the Taunton Police Accident Reconstruction Team, which is continuing to investigate. No charges have been filed, and speed is not believed to be a factor in the crash.
The post 12-year-old seriously injured after being struck by car appeared first on Shore News Network.
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I know it's been a while since I wrote part 1. You have probably read lots of resources about ASP.NET MVC by now. Thus for now I will skip the how to do part and concentrate more on why. Also as we go along this series, I will demonstrate some exciting new features that are available to ASP.NET MVC. Please remember wherever in this post I mention MVC, it refers to MVC 4. In this post, we will will limit our discussion to Model. Why model is used, what approach to take while working with models, and validating them.
MVC on ASP.NET
Sounds a bit old and an out of scope topic, but even now I see lots of discussion on how web form developers are planning to migrate to MVC. It's been a hot topic, hence I like to share some insights in this regard. For new developers MVC is a charmer, but for those who are considering to migrate from webform to MVC, the primary concern is familiarity with features that they are used to over the years. Most of webform developers' concerns is the ASP.NET webform framework has a lot to offer in the context of:
Security
Debugging
Validations
State management
MasterPage/UserControl
RAD Controls
Familiar syntax
Now the common question is does ASP.NET MVC fulfill these needs? Does it?
Humm, let's look at what ASP.NET MVC has to offer. Even though their architectural approaches are quite different, ASP.NET MVC and Web Forms actually have a lot in common.
Concerns
ASP.NET MVC
Web Form
Security Forms/Windows Authentication, MemberShip Forms/Windows Authentication, MemberShip
Validation Data Annotations Server Controls for validation
State Management Session and profile state View State/Cookies/Query Strings
Cross Page Data Transfer controller to view/controller to request ViewData, ViewBag, and TempData Session/Query String
Health monitoring Health monitoring Health monitoring
Caching output and data caching output and data caching
Master Page Layout Page Master Page
UserControl Partial View UserControl
RAD Control HTML helper Server Controls
Syntax Razor View Engines ASPX View Engines
Bingo, as you can see a Web Forms developer looking to learn ASP.NET MVC already is further ahead than he thinks. Productivity wise, ASP.NET web forms and MVC do not differ much, we can obviously raise the issue of RAD controls. I agree RAD controls speed up development but think how UX designers can play with views without the developer's involvement. Developers do not have to give their effort to implement cool UX utilizing RAD controls. Combining these final development efforts, the difference is very thin between webforms and MVC. Moreover there are a lot of good frameworks like angularJs/backbone js available these days to ease development effort. Productivity is the one of the major concerns for developers who wish to move from web forms to mvc.
This is no surprise like Web Forms, ASP.NET MVC is built on top of the ASP.NET platform. So both frameworks rely on .NET languages C# and Visual Basic .NET to interact with the .NET Framework, and can access compiled assemblies developed with any other language that the .NET Framework supports. Even interesting is while developing with MVC you will see the very familiar web.config and Global.asax. If you Google, you will probably get thousands of resources on these comparisons.
Working with Model
Now you can ask the obvious question, DB, Model or Code first? What are all these and how are they related to MVC? Fair enough, but to answer that let's step a back for a while to Part 1 which discusses the model part of MVC. Let's refresh our memory:
Models are those parts of the MVC application that play with the application's data domain and retrieve/store model state in a database.
So model is the heart of your application (from a business perspective) that deals with the single most important element: "data". Model represents your data structure and will contain functions that will help you to do CRUD (Create/Retrieve/Update/Delete) operations. Now depending upon the architecture, application type/scope, risk analysis, we need different approaches to implement the model entity. This is where EF (Entity Framework) comes in play. With EF we have the flexibility to work with models as we want to.
Entity Framework provides three ways to define the model of your entities. Using the database first workflow, with the model first workflow, and last but not least, code first approach. Code first approach of designing the model is becoming a popular choice due to complete control over the classes being written or implemented. But that doesn't mean DB first or model first are not cool to be used. The trick is to know when to use what, if you can analyze your application scenario/scope/risks and choose the one prefect for your scenario, in my opinion that is cool. Let's dive into our main topics.
Why Database First
Consider a scenario where an existing enterprise database tends to have many tables, each with many columns. They are not normalized as you prefer but can't complain as it's an existing database. No one will allow you to break and restructure the DB when there is a business in stack. In this situation the entity model of your application must be compatible to the existing database; you can’t bend the database to suit your preferred object model, in other words your hands are tied. Database First is popular in a scenario like this, also called a “brown field” scenario, with developers who build applications that access existing production databases holding large numbers of tables and columns.
When to adopt DB first approach is a different opinion over forums, groups, user meet-ups. Without reinventing the wheel I like to summarize those and here is the list; Legacy systems built upon existing DBs designed by DBAs, developed separately, or if you have an existing DB.
You will let EF create entities for you and after modification of mapping, you will generate POCO entities.
If you want additional features in POCO entities, you must either T4 modify template or use partial classes.
Database defines your domain model.
A fair amount of business logic resides in the database end for better maintainability.
Now the question is, is it bad to use DB first? Many experts have their own view, my opinion is despite the non-comforting object model if the application is serving as it is supposed to be, then there is no reason to be unhappy about it. Every approach we are discussing here has its own pros and cons.
Advantages
RAD development approach, EF will include all the usual metadata of the existing database, CSDL, SSDL, and the mapping between them just as model-first does.
Can concentrate on DB design first in case of dealing with big volume of data.
Database is not just a storage, can utilize it to implement business logic also.
Disadvantages
No control over EF generated code.
EF generated code is too complicated to customize.
Less control over the EF generated entities.
Imposing custom Entity level validation will require expertise on T4 template
Why Model First
Though DB first approach was the successor, changed development scenarios demanded Domain Driven Development, and model first brings the essence of DDD but not truly. Model first design came into prevalence where the Database Schema was based upon the Model. Though conceptually database and model first approaches are different, from an implementation perspective there in not much difference. Both end up with generating lots of auto generated code.
Just like in DB first, when to adopt this approach has varied answers, and my summarized version is,
IMHO popular if you are designer fan (= you don't like writing code or SQL).
You will "draw" your model and let the workflow generate your database script and T4 template generate your POCO entities. You will lose part of control on both your entities and database but for small easy projects you will be very productive.
If you want additional features in POCO entities you must either T4 modify template or use partial classes.
Manual changes to database will most probably be lost because your model defines the database. This works better if you have database generation power pack installed. It will allow you to update the database schema (instead of recreating) or update database projects in VS.
Advantages
Database is not just a storage, can utilize to implement business logic also.
Flexibility of creating entities that make sense in your business domain and their relationships.
Minimize the "ripple" effect caused by changes in the object model (code) generated or the database.
Disadvantage
The contents of the database will be generated each time, causing data loss as it regenerates everything every time you create the database from the model.
Once the database schema is published database can be changed directly in many occasions and thus the model becomes out of date and must be updated from the database in a database-first style.
Why Code First
In the Code First approach, you avoid working with the Visual Model Designer (EDMX) completely. You write your POCO classes first and then create the database from these POCO classes. This is an ideal candidate for Domain-Driven Design (DDD). Code First uses classes and properties to identify tables and columns, respectively. Code First has a number of benefits. First, developers are no longer delayed by not having a database. You can now create the database structure and start coding, with everything maintained in the same solution. Second, there's no auto-generated code; a developer has full control of each of the classes. Lastly, everything is kept simple in the data access layer because there's no .EDMX model to update or maintain.
Advantages
Can integrate model validation easily.
EF modeling often has weird errors, such as when you try to rename an association property, it needs to match the underlying meta-data - very inflexible.
Contrast back to Code First and you have complete control without all the hidden complexities and unknowns to deal with.
Enable migrations can keep track of schema changes, so can seamlessly deploy upgrades/downgrades while model changes.
I am a fan of Code First so I guess my opinion on disadvantages is biased.
Disadvantage
No visual model designer (EDMX) support.
No auto generated code.
Manual changes to database will most probably be lost because your code defines the database.
There is no hard and fast rule on when to use Code First, but in general you can use Code First because,
Full control over the code (no auto generated code which is hard to modify).
Ideal for applications with complex business needs.
Very popular because hardcore programmers don't like designers, and defining mapping in EDMX XML is too complex.
If you are expecting that the role of the database in your application is only storage. EF will handle creation and you don't want to know how it does the job.
Model Validation
As we discussed in the above three approaches, model objects deal with data and perform business logic on that data. Models are application specific and hence the ASP.NET framework imposes no restrictions on the construction of model objects. But we can impose custom validation on models. The most common and convenient approach to add such validation is Code First because this approach ensures complete control over the model and two popular methods of validation are DataAnnotation and FluentValidation. DataAnnotation and FluentValidation both serve a similar purpose. They ensure that the values that have been assigned to the object properties satisfy the business rules that are supposed to be applied.
DataAnnotation
FluentValidation
Validation support built into the .NET Framework and can integrate with minimal code. Advanced validation support that is not possible with data annotations.
Not intended to work with DB/Model First, but with some workaround (buddy class) support. Supports only Code First approach.
Does that mean Database First or Model First approach does not need any model validation? While using the Database First or Model First, the EDMX file has the same purpose - it contains all the details and mapping definitions of your model. Whatever restriction (or constraint) you define, your database/database model is translated by EDMX mapping definitions, so you do not have to put extra effort to define your own. Fluent API or data annotations and conventions replace the EDMX file only when using Code-First.
Testing Models
One of the major focus of mvc is always been testing. Thus this post is incomplete to with out discussing a bit how to conduct unit testing on models. Just a heads up I will try to show positive and negative testing both. Hope to do discuss in details future.For example purpose lets take a very simple class with DataAnnotation, and only do the property set ruls testing. FYI, many experts recommends to test properties that are derived instead of testing each properties.
C#
Copy Code
public class User
{
public string FName { private get; set; }
public string LName { private get; set; }
[Required]
[StringLength(10)]
public string Name { get { return string.Format("{0} {1}", this.FName, this.LName); } }
}
Now, finally for the testing, for the sack of simplicity I am just going to test the Name rule, I have written two tests. one positive and one negative .
Positive test: is to make sure name length satisfy allowed length
C#
Copy Code
[TestMethod]
public void Name_Should_have_valid_length()
{
User _userToTest = new User( );
_userToTest.LName = "Sh."; //3 character
_userToTest.FName = "Iqbal"; //5 character
Assert.IsTrue(_userToTest.Name.Length <= 10);
}
Negative test: is to make sure throw error when name exceeds the allowed length
C#
Copy Code
[TestMethod]
public void Name_Should_Fail_for_invalid_length()
{
User _userToTest = new User();
_userToTest.LName = "Shahriar"; //8 character
_userToTest.FName = "Iqbal"; //5 character
Assert.IsTrue(_userToTest.Name.Length <= 10);
}
For effective testing both - Positive and Negative testing should be utilized. I know the example is very small and basic but good for beginners, and hope to show more details testing techniques in upcoming posts.
References and Good Reads
ASP.NET MVC Framework Unleashed
Database First Walkthrough
Model First Walkthrough
Code First Walkthrough
Validate Model Walkthrough
Fluent Validation Walkthrough
Data Annotations Walkthrough
Series
Why(s) and How(s) of ASP.NET MVC Part 1 - MVC Basics.
History
Version 1.0.
License
This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)
Share
About the Author
Shahriar Iqbal Chowdhury/Galib
Chief Technology Officer
Bangladesh
I am a Software Engineer and Microsoft .NET technology enthusiast. Professionally I worked on several business domains and on diverse platforms. I love to learn and share new .net technology and my experience I gather in my engineering career. You can find me from here
Personal Site
Personal Blog
FB MS enthusiasts group
About Me
Comments and Discussions
First PrevNext
Pictures
Nelek9-Mar-16 2:41
Nelek 9-Mar-16 2:41
You should consider to remove the pictures of the animation film, since you are not the copyright owner and I suppose you don't have explicit allowance from the owner
M.D.V.
If something has a solution... Why do we have to worry about?. If it has no solution... For what reason do we have to worry about?
Help me to understand what I'm saying, and I'll explain it better to you
Rating helpful answers is nice, but saying thanks can be even nicer.
Nice Article
Santhakumar Munuswamy @ Chennai23-May-15 9:24
Santhakumar Munuswamy @ Chennai 23-May-15 9:24
Thanks for nice article...
Cross Page Data Transfer
Master.Man198026-Jul-13 6:42
Master.Man1980 26-Jul-13 6:42
Query string is applied to Asp.net MVC as well.
Re: Cross Page Data Transfer
Shahriar Iqbal Chowdhury/Galib27-Jul-13 6:15
Shahriar Iqbal Chowdhury/Galib 27-Jul-13 6:15
good point, I will update that part, thanks for reading
My vote of 5
Antariksh Verma26-Jul-13 3:29
Antariksh Verma 26-Jul-13 3:29
very clear and lucid explaination!!
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib26-Jul-13 5:45
Shahriar Iqbal Chowdhury/Galib 26-Jul-13 5:45
thanks Antariksh, glad you like it
My vote of 5
Thanks787225-Jul-13 0:12
Thanks7872 25-Jul-13 0:12
Request you to continue the series in order to contribute to such a wonderful place. Great one.
modified 2-Aug-13 2:33am.
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib25-Jul-13 2:35
Shahriar Iqbal Chowdhury/Galib 25-Jul-13 2:35
thanks Rohan for appreciation
My vote of 5
Jasmine250124-Jul-13 6:58
Jasmine2501 24-Jul-13 6:58
Nicely done! I'd love to see a sequel discussing the merits of MVC and when it should be used and when it should not be used. I've been instructed to use MVC when it was totally inappropriate. AND, we can implement the MVC design pattern with WebForms, and many people have been doing that for years. Talk about when it's appropriate to switch frameworks, and how to prepare for that. Very well written!
There is a typo in the table at the top where it should say "ViewBag" but it says "VieBag"
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib24-Jul-13 7:42
Shahriar Iqbal Chowdhury/Galib 24-Jul-13 7:42
Thanks jasmine, glad you like it. I discussed a bit about when to use MVC in part 1, but I will try to explain more about switching frameworks in upcoming one, thanks for the typo correction
My vote of 5
Mohammed Hameed23-Jul-13 21:14
Mohammed Hameed 23-Jul-13 21:14
As usual, excellent article!!!
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib23-Jul-13 22:47
Shahriar Iqbal Chowdhury/Galib 23-Jul-13 22:47
Thanks Hammed , glad you like it
My vote of 5
Fenil Desai18-Jul-13 3:51
Fenil Desai 18-Jul-13 3:51
Excellent article..
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib18-Jul-13 10:40
Shahriar Iqbal Chowdhury/Galib 18-Jul-13 10:40
thanks Fenil, glad you like it
My vote of 5
Аslam Iqbal16-Jul-13 22:46
Аslam Iqbal 16-Jul-13 22:46
good article.
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib16-Jul-13 22:55
Shahriar Iqbal Chowdhury/Galib 16-Jul-13 22:55
thanks aslam, glad you like it
Re: My vote of 5
Аslam Iqbal20-Jul-13 20:49
Аslam Iqbal 20-Jul-13 20:49
You are welcome.
My vote of 5
Maksud Saifullah Pulak16-Jul-13 22:38
Maksud Saifullah Pulak 16-Jul-13 22:38
Very nice.
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib16-Jul-13 22:56
Shahriar Iqbal Chowdhury/Galib 16-Jul-13 22:56
thanks pulak
Re: My vote of 5
Maksud Saifullah Pulak17-Jul-13 8:22
Maksud Saifullah Pulak 17-Jul-13 8:22
You are most welcome.
About MVC
sudarsan dash16-Jul-13 22:25
sudarsan dash 16-Jul-13 22:25
Can't MVC Support Aspx Engine, Just make it correct with the diffrence between the two.
Re: About MVC
Shahriar Iqbal Chowdhury/Galib16-Jul-13 22:58
Shahriar Iqbal Chowdhury/Galib 16-Jul-13 22:58
Good point, will make the change, thx
My vote of 5
DK0916-Jul-13 15:10
DK09 16-Jul-13 15:10
Well done. =)
Cheers
Re: My vote of 5
Shahriar Iqbal Chowdhury/Galib16-Jul-13 22:56
Shahriar Iqbal Chowdhury/Galib 16-Jul-13 22:56
thanks glad you like it
My vote of 5
Sk. Tajbir16-Jul-13 11:43
Sk. Tajbir 16-Jul-13 11:43
Awesome article
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Inside: Show your kids’ teachers appreciation year round with this Teacher’s Favorite Coffee or Tea Drink Printable.
When I taught third grade, I had a room partner who was super generous and thoughtful. Randomly, she would bring me an orange juice or a bagel or a coffee when she got one.
And it always came with a smile and a reminder that I deserved the treats because teaching is one of the hardest jobs out there.
That little treat always made my day so much better. It put me in a better mood. And it made me feel appreciated.
And now that I’m a parent and no longer in the classroom, I make sure to return the favor by paying it forward to my kids’ teachers.
I encourage my kids to take the time to appreciate their teacher and thank them each day…because teaching is often a thankless job. They get paid too little to do too much with too few resources. And that’s in a normal teaching year. Teaching in the middle of a pandemic proved even more how valuable our teachers are.
But teachers don’t often feel valued.
So a simple way to let our teachers know they are valued and appreciated is to take the time to value and appreciate them.
One of our favorite ways to do that is to drop off their favorite coffee drink on a random school day to brighten their day and start them off on the right foot.
Other Ways to Show Teacher Appreciation
Official Teacher Appreciation is the first week in May every year.
And Room Moms and PTAs go big during this week…which is great. Teachers get flowers and cards and parent-sponsored lunches.
But it’s also important to let teachers know they’re appreciated way sooner than May.
We love these 22 Sweet Ways to Show Teachers Appreciation.
We also love sending them this Teacher’s Favorites Sheet for them to fill out so we know what they love and can give more personalized gifts.
Here’s how we appreciate teachers during a pandemic while social distancing.
And this is the Number One Gift All Teachers Prefer.
But we also love randomly dropping off their favorite drinks to them throughout the year.
I tend to drop off drinks on the day after Halloween, which is the worst day to teach.
But before you can send a drink to school, you have to know what their favorite drink is.
One year, I asked my son’s teacher what she wanted and she gave me her Starbucks order. I assumed it would be a similar Starbucks drink for my daughter’s teacher. When I asked him, he shyly said, “Actually, I don’t drink coffee. But I love Diet Coke from McDonald’s.”
So we made two stops the next morning on our way to school…the Starbucks drive-through and the McDonald’s drive-through.
But our thoughtful gift of appreciation would have fallen flat for my daughter’s teacher if I hadn’t asked what he wanted to drink.
How to Use the Favorite Drink Order Printables:
1. Since we don’t know what our kids’ teachers like until we ask, download and print out the Favorite Drink Order Printable (download it below).
2. Cut out the order form and send one with each of your kids (or if you have a middle or high schooler, have them choose their favorite teacher). You can use the Starbucks order form or if you have a local coffee shop you prefer or another coffee chain, send in that order form.
3. Once you get the drink order forms back, choose a morning when you have a little extra time and can get through the drive-through line before school drop off.
4. Encourage your kids to fill out a little notecard to go with the drink. There are coffee ones and tea ones, depending on their teacher’s drink of choice.
5. Drop off the kids, the coffee/tea and the notecard when you can.
6. Next month, fill their drink order again or send in a drink order form to a school staff member who rarely gets appreciated or thanked: a teacher aide or the school nurse or the school custodian.
7. Know that a $4 coffee and the time you took to get it will make a teacher feel appreciated. There’s no need to wait till May’s official Teacher Appreciation Week.
The first time my kids climbed out of the car with their backpacks and their teachers’ coffee and McDonald’s Diet Coke, I knew their teachers’ days would be slightly better…just like mine was years ago when I got my orange juice and bagel.
Download the Teachers’ Favorite Drink Order Printable here.
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Coffee and Carpool
Welcome to Coffee and Carpool for busy parents on the go.
I’m Nicole and I am a recovering elementary school teacher, a mom to three super busy kids, and I mostly survive on strong coffee.
I share my best parenting tips and tricks for intentionally raising kind kids, creating a stronger family connection, and bullying prevention.
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Advertiser Disclosure: Some of the products & offers that appear on this page are from companies which Coin Clarity receives compensation including affiliate compensation. Any change in display ranking will be clearly marked as “featured” or “sponsored.” For more info see how we make money.
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On the 7th of May at 12:00 AM UTC, CoinDogg officially kicked off their initial liquidity offering (ILO) on decentralized exchange SakeSwap. Depending on your timezone and sleeping schedule you’d be forgiven for missing the whole thing, with the entire process being sold out and over in less than four hours!
The sale raised a cool 20 ETH for the project and comes off the back of a successful month just being completed on the ExMarket’s exchange launchpad both initiatives aimed to help realize the vision of supporting independent content creators across the globe and accelerating the adoption of crypto by the mainstream.
SakeSwap launched in 2020 and has already been making waves in the crypto space with its novel ILO crowdfunding platform and AMM liquidity pools, which are designed to give access to top-quality projects for all investors ranging right down from whales to shrimps.
How an ILO works?
ILO raises on the SakeSwap platform are conducted on an application basis with the process involving approval by core members of the SakeSwap team and community voting before projects are accepted onto the platform.
Project founders provide details outlining the project’s vision and token info including pool cap Info, investor access, and contribution limits, dates & Token Lock Period. The intention of the lock period is to give the project stability and the community more confidence.
Once approved, projects submit their tokens to SakeSwaps ILO smart contract pending the ILO opening. Assuming the ILO is well received and gains investor interest, investors also contribute funds to the smart contract with half of the funds being deposited converted to the project token and half remaining unchanged.
Investors receive LP tokens to prove their contribution to the pool whereas the project receives half the funds deposited by investors, equivalent to the value of project tokens deposited.
For a more detailed understanding of how the ILO process works and to find out about potential future projects launching on the SakeSwap exchange check out their information page here.
For more information on the CoinDogg project, development, and future fundraising rounds follow us on Twitter, join our Telegram community, or visit us at www.coindogg.com
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Blockchain in Shenzhen: More than 2,000 new companies are added a year, small and micro enterprises are the main force
At 9 o’clock in the morning, the sun had just risen, and on the sidewalk in the east, west, north and south sides of the Dasha River in Shenzhen, there were hurried young faces flowing. Here is the Shenzhen Bay Technology ecological park, is one of the gathering block chain enterprises in Shenzhen City, here are the head of corporate block chain micro-public banks, but also hundreds of small and medium micro enterprises block chain. At 10 o’clock in the morning, Xiao Wang, dressed in casual clothes and fluffy hair, walked into…
News 2021-11-26
The “Crown” and “Load-bearing” of Goose Factory Game
Heavy is the head who wears the crown. 1. Crown and load-bearing These are the two key words of this year’s Goose Factory game summarized by the author. This “crown” has been worn by the goose factory for many years, and there is no possibility of it being picked in the short term. Still krypton gold: According to Tencent’s 2021 Q3 financial report, game revenue in the local market was 33.6 billion yuan, an increase of 5% year-on-year and quarter-on-quarter; game revenue in the international market was RMB 11.3 billion, a year-on-year increase of 20% (calculated at a constant exchange rate,…
News 2021-11-26
Ethereum News
The Ethereum EIP-4488 proposal will make Layer 2 gas fees 5 times cheaper
Ethereum developers are considering more mid- to long-term solutions, and Ethereum rollups will become the standard for L2 scaling solutions. Ethereum co-founder Vitalik Buterin and ConsenSys R&D engineer Ansgar Dietrichs proposed a new Ethereum improvement proposal EIP-4488 on November 23. Looking at it now, they seem to make rollups 5 times cheaper than before. The title of the Ethereum rollups enhancement EIP-4488 is “Transaction calldata gas cost reduction with total calldata limit”, which is described in the official proposal as “Gas that greatly reduces transaction call data.” At the same…
2021-11-26
News
Vitalik: A step-by-step roadmap for expanding Rollups
For Ethereum , Rollups is the short to medium term, and may be the only trustless scalability solution in the long term . The transaction fees on Ethereum L1 have been high for several months, and now it is more urgent to take all necessary actions to help promote the transfer of the entire ecosystem to Rollups. Rollups has greatly reduced the cost of many Ethereum users: l2fees.info website often shows that the cost of Optimism and Arbitrum networks is about 3-8 times lower than the Ethereum base layer itself , and zk-Rollups has better data compression. And it can avoid including signatures, so its cost is about 40-100 times lower than the Ethereum…
2021-11-26
What is Web 3.0?
Many people do not understand what Web 3.0 is. This article tries to explain my understanding of this problem in the most concise language. 1. Internet paradigm iteration Web 1.0, the first generation of the Internet, is a static Internet, and its main application is network media. Various online media hire a large number of editors to publish content with pictures and texts into web pages. Readers visit websites and browse digital content, but they can only read but not write, and cannot participate in content creation. The entire Web 1.0 media is equivalent to the…
News 2021-11-26
News
Dialogue with the White Hat Alliance: Where are the players in the chain game in full swing?
contents Blockchain game data Dialogue with the leader of the Chinese branch “Master” Advantages of the White Hat Alliance Looking Forward to the White Hat Alliance About the White Hat Alliance Blockchain game data Judging from the global “GameFi” trend chart of Google Trends in the past year, the popularity of GameFi began in July this year, and the related heat reached its peak in just a few months. Google Trends Global? “GameFi” Trend Chart in the Past 1 Year DappRadar shows that in the past 24 hours, the top…
2021-11-26
News
DAO: The most effective way to collaborate online on a global scale
Compared with the common forms of organization and management, DAO has obvious characteristics of decentralization. However, centralization and decentralization are not all-or-nothing. Many DAO organizations rely more or less on centralization to start or maintain, and they have not achieved absolute decentralization. Therefore, it is more accurate to understand from the perspective of degrees of freedom, that is, to show an organization with a clear decentralized scale. Generally, DAO is regarded as a decentralized company. Of course, it is more business and practical to understand DAO from the company level, but we…
2021-11-26
What is the status quo of the side chain and Layer 2 solution?
Polygon POS Side chains like Ronin are for application-specific (Axie Infinity), while other side chains are suitable for more general applications. Currently, Polygon’s proof-of-stake (POS) sidechain is the leader of the track, and its value is close to US$5B. More than 100 DeFi and gaming applications have been deployed, including projects we are familiar with, such as Aave and Sushiswap, and A clone version of Uniswap called Quickswap. Similarly, Polygon POS and EVM-compatible Layer 1 does not seem to be any different. But it was built as an extension of Ethereum, not a competing…
2021-11-26 • News
Understand BSN in one article
The blockchain service network (hereinafter referred to as “service network” or “BSN”) is a global infrastructure network that is used to deploy and run various blockchain applications across cloud services, across portals, and across underlying frameworks. The service network is committed to changing the current high cost of blockchain application development and deployment, and provides developers with a public blockchain resource environment based on the Internet concept, which greatly reduces the development, deployment, operation and maintenance, interoperability and supervision of blockchain applications Cost, so that the blockchain technology has been…
News 2021-11-26
Reputation Our foundation in the Web3.0 world
In “NTNFT: When the Wallet Can Record Your Life”, the Rhythm Research Institute introduced various use cases that the non-transferable non-fungible tokens (Non-Transferable Non-Fungible Token) that cannot be transferred have experienced off-chain and on-chain. Although the standard has not yet received widespread attention from the encryption community, its function of proving the unique attributes of personal identity in the Web3.0 world will surely be widely used in the future. People say that the Internet has memories. It is undeniable that centralized databases do give the Internet a brain that can be manipulated. Sometimes,…
News 2021-11-26
Interview with QitChain CEO Shoaib Hayat: Distributed search will serve as a web3.0 infrastructure to support the ecological development of the meta universe
With the rapid development of Internet technology, emerging technologies such as artificial intelligence, Internet of Things, cloud computing, and 5G have made people’s lives more and more convenient, and the speed of information circulation in society has continued to increase. Huge data resources have opened up the process of data revolution in various fields, the value of data has become more obvious, and the awareness of data security is gradually increasing. In the face of centralized storage solutions with frequent problems, distributed technology has been given high hopes from all walks of…
News 2021-11-26
Central bank officials: Establish a virtual asset transaction traceability and scenario tracking system
The concept of virtual assets has become broader, but its risks cannot always be ignored. On November 26, Gou Wenjun, director of the Anti-Money Laundering Monitoring and Analysis Center of the People’s Bank of China, stated at the first Lujiazui National Financial Security Summit and the 11th China Anti-Money Laundering Summit Forum in 2021 that virtual assets can satisfy some people’s privacy, currency trust, and wealth appreciation. At the same time as demand, its decentralization, anonymity, and borderless characteristics, coupled with the capital control and anti-money laundering compliance requirements faced…
News 2021-11-26
The economic mechanism design of web3 game
web2 games and web3 games The web2 game is a free-to-play model, which largely makes money from the items in the game. The web3 game is a play-and-earning model. Users gain income by participating in the game ecology, and the items are NFTs, which really belong to the participants, not the company. Of course, the current web3 games are not pure enough, leaving a lot of traces of web2 games. In the future, native web3 games will truly change the game industry. It is only a matter of time before the game field…
News 2021-11-26
zkTube and WAFI Group reached a cooperation: to establish an equipment sales shop in the UAE to promote the globalization of zkTube
zkTube is an Ethernet Square 2 expansion layer solution, using a zero-knowledge proof to solve the problem of network congestion, high gas costs and low transaction rate. The company is headquartered in Australia and is committed to providing the best user experience and various opportunities. The company is expanding its network to other countries. According to foreign media news, zkTube Dubai headquarters was established. zkTube and WAFI Group jointly established the world’s first retail store for mining machinery sales in the UAE. zkTube is also one of the sponsors of the DCentral Miami Conference 2021, the…
News 2021-11-26
Digital RMB pilot shows “full bloom”
The number of personal wallets opened exceeds 100 million Beijing Winter Olympics pilot scene construction enters the sprint stage Digital RMB pilot shows “full bloom” Regardless of the coverage area, application scenarios, or the number of wallets, the digital renminbi pilot is currently showing a “full bloom” trend. The latest data shows that the cumulative number of digital renminbi personal wallets has exceeded 100 million; the construction of the digital renminbi Beijing Winter Olympics pilot scene has entered the sprint stage: the construction of the acceptance environment in Beijing is accelerating;…
News 2021-11-26
Threshold digital signature
Digital signature is a basic concept of blockchain and cryptocurrency. Modern blockchains use digital signatures to ensure billions of dollars in value. Digital signatures use a so-called key pair, which is a pair of seemingly random values, one of which is a “private key” and the other is a “public key”. With digital signatures, anyone with a “private key” can “sign” transactions and use digital currency. Therefore, it is very important to protect the “private key”. Some tech-savvy blockchain users choose to protect this key themselves and accept the risk of theft or loss of…
News 2021-11-26
DappRadar Announces Issuing Token RADAR
On November 26, Dapp data tracking company DappRadar announced that it will issue Token RADAR. Details will be announced within a few weeks. The token has not yet been launched on any trading platform. According to reports, RADAR represents an opportunity to directly contribute, manage and shape the future of Dapp stores in the world. Whether it is managing lists, making recommendations or product decisions, in the spirit of Web3.0, DappRadar hopes to fully accept decentralization, and calls on the community to help expand DappRadar and the dapp space to new…
News 2021-11-26
Can accurately predict the industry wind direction every time? The founder of the well-known research institute Messari “has two brushes”
Few people in the blockchain industry know Messari. The company has won a large number of fans with its detailed data and forward-looking vision beyond the limits of the industry. As the founder of Messari, Ryan Selkis (Ryan Selkis) is more famous in the industry for his unbelievable words, but every one of his predictions is extremely accurate. Therefore, the exploration of the views of Messari founder Ryan Selkis has also become a required course for many start-up projects. Then, as a “mysterious man” similar to “Satoshi Nakamoto” who can hardly…
News 2021-11-26
Ethereum and 7 challengers: Can ETH 2.0 stop them?
We investigated seven projects that tried to overthrow Ethereum, we introduced each of these seven, and briefly discussed their technology and how they stole the crown from Ethereum. Obviously, some projects have made significant progress in terms of transaction speed, cost and other factors. Now, we take this analysis one step further. Here, we will study these seven projects in depth to evaluate factors including ease of use and developer interest, degree of decentralization, governance framework, security settings, functions, etc. Among these projects, which one can overthrow the giant Ethereum and become David…
2021-11-26 • Ethereum News
Create a new consumer payment model for farmers
Recently, at the 9th China Public Welfare and Charity Project Exchange Exhibition (hereinafter referred to as the “City Exhibition”), Mr. Wang was browsing the 3D exhibition hall of the China Construction Bank of the Charity Exhibition Cloud Exhibition and clicked on the agricultural aid commodity Xinjiang Grey Date suspended on the cloud platform The picture consumes 36 yuan digital renminbi to purchase goods, of which 28 yuan arrives in the wallet of the farmer in real time. This is a new payment model in the field of consumer assistance created…
News 2021-11-26
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Tanzania’s central bank plans to launch CBDC
2021-11-26
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According to news on November 26, out of “FOMO” sentiment, more and more governments are allowing the introduction of central bank digital currency (CBDC) as the national currency. Tanzanian central bank governor Florens Luoga said at a financial conference held in the capital Dodoma on Thursday: “In order to ensure that our country does not lag behind in adopting central bank digital currencies, the Central Bank of Tanzania has begun to prepare to build its own CBDC.” Luoga compared Tanzania’s plan with eNaira. Nigeria launched eNaira in October with the aim of reducing transaction costs and increasing people’s participation in the formal financial system. eNaira will supplement the physical naira. Despite the central bank’s efforts to stabilize its exchange rate, the physical naira has weakened this year. Luoga said that as part of the preparatory work, Tanzania is strengthening the capabilities of central bank officials in digital currency and expanding research. He said that Tanzania remains vigilant about cryptocurrencies and continues to advise the public to be cautious. (Bloomberg)
Russian parliament establishes cryptocurrency supervision working group
2021-11-26
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On November 26, the Russian State Duma Parliament is now forming a working group that will work to resolve outstanding issues related to cryptocurrency regulation. Anatoly Aksakov, chairman of the Parliamentary Financial Market Committee, revealed that the working group will hold its first meeting in the near future. He believes that there are “grey areas” in the issuance, circulation, taxation and operation of cryptocurrencies. All of these need to be discussed, analyzed, legislated, and regulated. The decision to establish a working group is correct. He also said that he would participate in the work of the reform team, hoping to find a solution. (Bitcoin news)
List of important news in the evening of November 26
2021-11-26
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12:00-21:00 Keywords: Upstox, European Central Bank, Uzbekistan 1. Indian fintech company Upstox received 25 million US dollars investment from Tiger Global; 2. Singapore fund management company Fintonia Group launched two regulated Bitcoin funds; 3 .Affected by the South African epidemic, the overall market value of cryptocurrencies fell by US$200 billion; 4. South Australian police seized cryptocurrencies worth 700,000 Australian dollars in dark web anti-drug cases; 5. The European Central Bank expanded the PISA framework to cover digital currencies and stablecoins; 6 Gou Wenjun, the central bank: Establish a virtual asset transaction traceability and scene tracking system; 7. Uzbekistan allows residents to trade cryptocurrencies on local exchanges; 8. The “Rug Pull” of the Meme project Snowdog is suspected to have a “Rug Pull” internal staff made a profit of $17 million;
Affected by the South Africa epidemic, the overall market value of cryptocurrencies fell by 200 billion U.S. dollars
2021-11-26
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The newly discovered variant of the new coronavirus in South Africa has affected all financial markets. As the world’s most important stock index futures contract plummeted, the cryptocurrency field has also undergone a large-scale adjustment. Bitcoin fell to a six-week low, while the prices of some altcoins saw double-digit declines, and the overall market value of cryptocurrencies fell by $200 billion. Specifically, Bitcoin fell by $5,000 in a few hours to an intraday low of $54,300, which is the lowest price point since October 13. The price collapse coincided with similar developments in global stock markets. Fearing that the new coronavirus variants in some African countries will cause the epidemic to expand, the futures contracts of popular stock indexes such as the Dow Jones, S&P 500 and Nasdaq plummeted. Dow Jones index futures fell more than 2%, the S&P 500 index fell nearly 1.7%, and oil prices fell even more. US crude oil futures fell more than 5.5%. (CryptoPotato)
Central Bank Gou Wenjun: Establishing a virtual asset transaction traceability and scene tracking system
2021-11-26
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On November 26, Gou Wenjun, director of the Anti-Money Laundering Monitoring and Analysis Center of the People’s Bank of China, stated at the first Lujiazui National Financial Security Summit and the 11th China Anti-Money Laundering Summit Forum in 2021 that virtual assets can satisfy some people’s privacy, currency trust, and wealth appreciation. At the same time as demand, its decentralization, anonymity, and borderless characteristics, coupled with the capital control and anti-money laundering compliance requirements faced by cross-border payments, are widely used in extortion, drug trafficking, gambling, money laundering, terrorist financing, and tax evasion. , Cross-border transfer of funds and other illegal transactions. In Gou Wenjun’s view, we should pay attention to the risk management of virtual assets from four aspects. First, clarify the non-financial attributes of virtual assets and improve the regulatory policies for emerging virtual assets. Second, strengthen the monitoring and analysis of virtual asset transactions to penetrate and identify the essence of virtual asset transactions. Third, strengthen the innovative application of new technologies and establish a virtual asset transaction traceability and scene tracking system. Fourth, strengthen information sharing and cooperation with overseas financial intelligence agencies to form an international joint force against crimes using virtual assets. (The Paper)
Uzbekistan allows residents to trade cryptocurrencies on local exchanges
2021-11-26
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On November 26, the National Project Management Agency of Uzbekistan (NAPM) recently revised the rules related to local cryptocurrency transactions. Forklog quoted a decree issued by the agency’s head and reported that under the new regulations, Uzbek residents can Domestic purchase, sale and exchange of encrypted assets in exchange for domestic currency. It is also possible to exchange cryptocurrency for foreign legal tender. (Bitcoin News)
State Council: Support Beijing to accelerate the promotion of legal digital currency pilot
2021-11-26
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On November 26, the State Council issued its opinions on supporting the high-quality development of Beijing’s urban sub-center. Opinions put forward support for the innovation and development of financial technology, accelerate the promotion of legal digital currency pilots, do a good job in the implementation of financial technology innovation regulatory tools, support large banks and other legally established digital renminbi operating entities, support qualified banks to participate in direct banking pilots, and explore the construction of digital Asset trading venues. (Official website of the State Council)
JPMorgan Chase considers acquiring digital bank Viva Wallet
2021-11-26
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On November 26, Bloomberg quoted sources as reporting that JPMorgan Chase is considering acquiring or investing in Greek digital bank and payment company Viva Wallet to get involved in more areas of financial disruption. According to sources, the valuation of Viva Wallet in this round of transactions will reach more than 1.5 billion euros ($1.7 billion), and the transaction has not yet been finalized. JPMorgan Chase and Viva Wallet both declined to comment. Viva Wallet provides services such as debit cards, credit cards and small business payment solutions in 23 European countries. It is also one of the certified payment companies for London’s iconic black taxis. In April of this year, the company received US$80 million in financing through investors such as Tencent, the European Bank for Reconstruction and Development, and Breyer Capital. (Bloomberg)
Official of the Central Bank of Russia: There are risks in Russian cryptocurrency investment
2021-11-25
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Officials of the Central Bank of Russia stated that there are risks in Russian cryptocurrency investment. (Golden Ten)
Governor of the Bank of England: Bitcoin as a national currency is too volatile
2021-11-25
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The Governor of the Bank of England Bailey said that I am worried about a country’s choice of Bitcoin as its national currency. Bitcoin as its national currency is too volatile. The currency used for payment should be stable. If leverage is used, the cryptocurrency market may become a financial stability issue. (Golden Ten)
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Copyright © 2021 CoinYuppie. Blockchain is a belief and Cryptocurrency is a consensus that they will eventually change the planet and humanity.
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The Copernicus programme is a cooperation of the European Union and the European Space Agency (ESA) for Earth observation. ESA coordinates the acquisition and delivery of Earth observation data from space. Starting with the successful launch of Sentinel-1A in 2014, the series of Sentinel satellites will deliver a wide range of data for environmental and civil security purposes.
Under the ESA – European Community GMES Agreement of 28 February 2008, as amended subsequently, the agency is charged with managing the GMES Space Component (GSC) Programme. Under the Copernicus Agreement signed by ESA and the European Union (EU) on 28 October 2014, consistent with the EU Copernicus Regulation, ESA continues the activities related to the Copernicus Space Component.
2. Data supply
The Copernicus Data Hubs (Open Access Hub, Collaborative Data Hub, International Access Hub, Copernicus Services Data Hub) are ESA online portals, which make Sentinel data available to individuals and entities worldwide. The portals allow the searching for Sentinel data and the download of Sentinel data, selected by the user.
3. Copyrights
Any Sentinel data available through the Sentinel Data Hub will be governed by the Legal Notice on the use of Copernicus Sentinel Data and Service Information.
Any other contents of ESA's Sentinel Data Hub websites are intended for non-commercial use. ESA grants permission to users to visit the site, and to download and copy information, images, documents and materials from the website for non-commercial use. ESA does not grant the right to resell or redistribute any information, documents, images or material from its website or to compile or create derivative works from material on its website. Use of material on the website is subject to the terms and conditions outlined below.
All material published on the ESA Sentinel Online websites is protected by copyright and owned or controlled by ESA or the party credited as the provider of the content, software or other material. Copyright in the material must be recognised by an appropriate on-screen credit in a form such as "European Space Agency - ESA". If a remote sensing product is being used, the credit "produced from ESA remote sensing data" must be added, plus, if applicable, "image processed by (name of the institution as indicated on the Internet Server)".
4. User registration
To access Sentinel data at the Sentinel Data Hub portals, individuals and entities need to register as a user and provide information to ESA. Through the registration, the user will be granted a password to access Sentinel data.
5. Personal data privacy
When the user accesses ESA's Sentinel Data Hub websites, registers as a user or downloads content, certain information about users, such as internet protocol (IP) addresses, data downloaded, pages visited, software used and time spent, as well as other similar information, will be stored on ESA servers.
The collected information will be used by ESA only for the traffic analysis of the webpage, as well as for monitoring, reporting and communication regarding the distribution and use of Sentinel data towards ESA internal bodies, ESA Member States, the European Union and the general public. Whenever sharing user information with individuals or entities other than ESA, the data will be dealt with anonymously.
6. Use of cookies
Cookies are pieces of information that a website transfers to an individual hard drive for record-keeping purposes; passwords are not saved in cookies. Cookies help ESA to know which pages are visited most often and helps ESA to ensure that its website responds to users' needs.
If you do not wish to receive a cookie, or if you wish your browser to notify you when you receive a cookie, you may use the option on your web browser to disable cookies. Click on the 'help' section of your browser to learn how to change cookie preferences. However, please note that if you disable all cookies you may not be able to take advantage of all the features available on this website.
ESA does not use personal identification details transferred through cookies either for promotion or marketing purposes, nor will such information be shared with a third party.
7. Availability of the Service
ESA will make best efforts to provide the Sentinel data access service through the Sentinel Data Hubs. It is understood and recognised by all individuals and entities having accepted these Data Supply Conditions, that ESA is not in a position to guarantee the availability of Sentinel data through the Sentinel Data Hubs.
In particular, ESA reserves the right to review, modify, suspend or terminate the provision of Sentinel data through the Sentinel Data Hubs at any time in the event:
of technical capacity constraints due to the number of user requests
of lack of sufficient funding for sustaining Sentinel missions, and associated facilities and activities
that satellite or ground system failure occurs
that planned activities of related Sentinel systems and facilities are suspended or cancelled in order for ESA to carry out activities considered of a higher priority.
The operating entity, i.e. ESA, may modify the Sentinels’ operations plans at any time, should it consider such corrective action necessary for ensuring the missions safety and success.
8. Waiver of Liability
With regard to the service offered by ESA through the Sentinel Data Hub portals, the user shall have no claim against ESA, its employees, related entities or employees of such entities, with respect to any injury to, or death of its own employees or employees of related entities or for damage to or loss of its own property or that of its related entities, whether such injury, death, damage or loss arise through negligence or otherwise, except in the case of gross negligence or wilful misconduct.
9. Use of the Sentinel Data Hub
By registering at the Sentinel Data Hub portals, accessing and\or downloading available Sentinel data, the user shall act in good faith and shall not misuse or interfere the service of the portals.
The Sentinel Data Hub portals use map layers as reference information for assisting data selection, ESA and its suppliers offer no guarantees of any kind in connection to their accuracy or completeness.
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These units are the minimum requirement for those who will work in the capacity of Supervisor in the Resource and Infrastructure sectors.
The units of competency include:
These 3 units are the minimum requirement for those who will work in the capacity of Supervisor in the Resource and Infrastructure sectors.
The units of competency include:
RIIRIS301E– Apply risk management processes
This unit describes the skills and knowledge required to apply risk management processes in the mining, drilling and civil infrastructure industry.
This unit applies to individuals who contribute to applying risk management processes as part of their responsibilities. This could be an employer, line manager, supervisor, safety representative or employee. This unit applies to those who carry out or assist in carrying out risk assessment in the workplace
RIIWHS301E - Conduct safety and health investigations
This unit describes the skills and knowledge required to apply risk management processes in the mining, drilling and civil infrastructure industry.
This unit applies to individuals who contribute to applying risk management processes as part of their responsibilities. This could be an employer, line manager, supervisor, safety representative or employee. This unit applies to those who carry out or assist in carrying out risk assessment in the workplace
RIICOM301E – Communicate information
This unit describes the skills and knowledge required to communicate information to complete work activities within the mining, drilling and civil infrastructure industry. It includes planning and preparing for and conducting communication via a number of different methods to ensure work activities are understood.
This unit applies to individuals who are working in an operational, supervisory or team leader capacity.
RIIRIS402E Carry out the risk management process.
This unit describes the skills and knowledge required to carry out risk management processes, including planning, identifying hazards and assessing risk, and developing and implementing risk controls.
This unit it appropriate for those working in supervisory roles overseeing and auditing the risk assessment process.
Cost: $610.00
Time and Duration: 4 Days
< Back: All Courses
Course Requirements
Things to bring with you:
Drivers Licence or another form of legal identification. E.G. Passport, High Risk Work Licence
USI Code
All students will be required to create or obtain their personal USI. Certificates cannot be issued without this code.
USI link: https://www.usi.gov.au/
Appropriate clothing and enclosed footwear must be worn when attending courses.
Venue/Parking:
Our office front is located at 12 Fursden Street, Glenella, 4740.
Please be advise if attending a course, you cannot park in Fursden Street.
Free off-street parking is available at 7 Brickworks Court, Glenella, 4740 located off Glenella Connection Road. Once you have secured a park the entrance to our building is locate between the blue and cream coloured building follow the signage through to reception for check in.
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We welcome feedback. The web database is not a complete record of the Museum’s botanical holdings, and documentation for specimens will vary due to when and how they were collected as well as how recently they were acquired. While efforts are made to ensure the accuracy of the information available on this website, some content may contain errors. We work with communities and stakeholders around the world to interpret the collections in order to promote a greater understanding of global heritage and, through consultation, will revise or remove information that is inaccurate or inappropriate. We encourage and welcome members of communities, scholars, and others to contact us to confirm or clarify data found here.
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R. M. Schuster
Current Determination: Calyptrecolea parssonii R. M. Schust.
Family: Pterobryaceae
Det. by: R. M. Schuster
Type Status: Isotype
Coordinates: -44.7724 168.117
Elevation: 914
Collector(s): R. M. Schuster
Collection Date: 11 May 1962
Catalog Subset: Bryophytes
Catalog Project: vTypes
EMu IRN: 3698362
OccurenceID: 5839400e-72d6-4e5a-b718-98cec269b61e
Disclaimer: The Field Museum's online Botanical Collections Database may contain specimens and historical records that are culturally sensitive. Some records may also include offensive language. These records do not reflect the Field Museum’s current viewpoint but rather the social attitudes and circumstances of the time period when specimens were collected or cataloged.
We welcome feedback. The web database is not a complete record of the Museum’s botanical holdings, and documentation for specimens will vary due to when and how they were collected as well as how recently they were acquired. While efforts are made to ensure the accuracy of the information available on this website, some content may contain errors. We work with communities and stakeholders around the world to interpret the collections in order to promote a greater understanding of global heritage and, through consultation, will revise or remove information that is inaccurate or inappropriate. We encourage and welcome members of communities, scholars, and others to contact us to confirm or clarify data found here.
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We welcome feedback. We are continually working with our geological records to ensure the accuracy and appropriateness of these data. As we work to promote a greater understanding of the global heritage embodied by our collections, we actively seek consultation and will revise or remove information that is inaccurate or inappropriate. We encourage and welcome help from minorities and other people historically-underrepresented in museum communities, scholars, and others to improve the data in our geological records.
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Braxton Burmeister Dino Babers Andre Garrett Shrader Tommy DeVito Jim Brown Michael Vick Justin Fuente Josh Black Sean Tucker Andre Szmyt Sports Football Coaching College sports College football
More From
Syracuse ACC Virginia Tech Clemson South Florida
Virginia Tech, Syracuse both looking for much-needed victory
By HANK KURZ Jr. - Oct. 21, 2021 11:13 AM EDT
Syracuse running back Sean Tucker (34) rushes during the second quarter of the team's NCAA football game against Clemson in Syracuse, N.Y., Friday, Oct. 15, 2021. (AP Photo/Joshua Bessex)
View All (2)
Virginia Tech coach Justin Fuente sounds envious when he talks about the challenge his struggling Hokies will face on Saturday against Syracuse.
“This is a team that’s found their identity,” Fuente said of the Orange (3-4, 0-3 Atlantic Coast Conference). “... They have really found, in my estimation, ... a quarterback, and certainly found their tailback."
Those are things the Hokies (3-3, 1-1) can't say. After taking No. 13 Notre Dame to the wire before losing 32-29 at home, Virginia Tech floundered offensively last weekend in a 28-7 Coastal Division loss to No. 23 Pittsburgh. Quarterback Braxton Burmeister was 11 for 32 for 134 yards vs. Pitt and the running game totaled 90 yards on 28 carries, 3.2 yards per attempt.
The Orange enter the game trending in the opposite direction.
Syracuse has the nation's second-most productive running back in Sean Tucker, who averages 135.4 yards and has scored 11 touchdowns. Only nine of those came rushing; he also leads Syracuse with 224 yards on 14 catches and two more TDs. His five straight 100-yard games match the program record, not bad for a school that's featured the Hall of Fame trio of Jim Brown, 1962 Heisman Trophy winner Ernie Davis, and Floyd Little.
“Sean is different. He’s something that doesn’t come along very often,” Orange coach Dino Babers said. “We need to use that, and that’s what we’re doing.”
The Orange also appear to have settled on a quarterback in Mississippi State transfer Garrett Shrader, who has started the past four games, leading to the exit this week of two-year starter Tommy DeVito to the transfer portal.
Shrader’s running ability has helped jump-start the Orange offense. The 6-foot-4, 230-pound Shrader totaled 315 rushing yards and four TDs in last-play losses to Florida State and then-No. 19 Wake Forest, but was held to just six yards on seven carries in a 17-14 loss at home last Friday night to Clemson.
Some other things to watch when Syracuse visits Virginia Tech:
SPUTTERING
Fuente arrived in Blacksburg with a reputation as an offense-minded coach, but things have not panned out this year behind quarter Braxton Burmeister and the absence of an effective running back. Burmeister leads the team in rushing with 212 yards and is completing less than 54% of his passes.
Against Pitt, “Ultimately the execution left plenty to be desired,” Fuente said.
CARDIAC 'CUSE
Syracuse has lost all three of its ACC games, the first two on the last play and last week 17-14 in the final minute against Clemson when place-kicker Andre Szmyt missed a 48-yard field goal attempt that would have tied it at 17-all.
"We’re getting tired the way that we’re losing,” redshirt senior defensive lineman Josh Black said. “We’re one play away these last three games. That’s what makes it so frustrating to us. It almost seems like it’s rigged.”
PACK YOUR BAGS
After playing six of their first seven games at home, the Hokies will play away from Blacksburg on four of their next five weekends. Their stops include Georgia Tech and longtime rivals Boston College, Miami and Virginia.
DON'T I KNOW YOU?
These long-time Big East rivals have met just twice as members of the ACC, the last time in 2016, a 31-17 Orange victory. Syracuse leads the all-time series 10-8. The losses include a 62-0 beatdown for the 16th-ranked Orange in 1999, the year the fourth-ranked Hokies played for the national championship.
In that game, Michael Vick threw for just 135 yards and a touchdown, but “Beamerball” scored twice on fumble returns and once on an interception.
ENTER SANDMAN
The Metallica song has given the Hokies one of the best entrances in college football, but struggles on the field have led to boos raining down on Fuente. He says it's part of coaching and hopes his players can ignore it, but after meeting with Baylor last offseason, only positive results will call off the dogs.
___
AP sports writer John Kekis contributed from New York.
___
Follow Hank on Twitter: www.twitter.com/hankkurzjr
___
More AP college football: https://apnews.com/hub/college-football and https://twitter.com/AP_Top25. Sign up for the AP’s college football newsletter: https://apnews.com/cfbtop25
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Minister of International Trade Malik Samarawickrama told Parliament that Sri Lanka needs to look at new markets as the traditional markets have not helped Sri Lankan exports grow.
As a result, the Minister said that Sri Lanka will look towards China, Singapore, Japan and Korea to improve Sri Lankan exports.
Sri Lanka is to sign Free Trade Agreements with China and Singapore and discussions on the deals are expected to be finalised this year.
Samarawickrama said that Sri Lanka will also sign a trade deal with Pakistan and an economic and technological agreement with India.
“This is the only way forward,” the Minister said as parliament discussed Sri Lanka’s exports.
Sri Lanka hopes its exports to Europe will improve once the country obtains the GSP plus trade concession from the EU.
The EU is to decide on Sri Lanka’s application for GSP by May 15.
The EU is Sri Lanka’s biggest export market, accounting for nearly one-third of Sri Lanka’s global exports. The EU’s Special Incentive Arrangement for Sustainable Development and Good Governance, GSP+, is part of EU’s unilateral tariff preferences in favour of developing countries.
The GSP+ scheme is designed to help developing countries by granting full removal of tariffs on over 66% of tariff lines covering a very wide array of products including, for example, textiles and fisheries. (Colombo Gazette)
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bugsbunny
1 COMMENT
Nimal March 1, 2017 At 11:08 pm
I would suggest to sell the government own tea estates to foreigners on condition that they use our tea in the brands and distribute world wide. Our tea propaganda board operating in foreign countries is a joke, waste of taxpayers’ money.
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Switch your present monitoring service to CSI and SAVE. Receive THREE MONTHS FREE with the signing of a new CSI Twelve Month Agreement.
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When severe weather or other natural disasters hit, your Xfinity or Comcast Business services could be impacted. Comcast is prepared and ready, always making the safety of our customers and employees our top priority.
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All content �2000,2021 by Kevin Morgan, except where noted. Please do not use any material from this site without permission. Thank You.
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An antipodist spinning a parasol with a pair of puppets on her feet. The puppets’ expressions of excitement and frustration were controlled from the jugglers toes. A cow girl on a pogo stick whose lasso doubled as her jump rope.
One minute the ringmaster is coaxing cupcakes and cookies from children in the front row, and the next she accepts a beer from the crowd (“Can you keep it closed for now? I’m still working on this one.” she tells them.)
Posted on 2016-03-06 Categories UncategorizedTags Chicago, Circus, JugglingLeave a comment on The Occasional Inspiration
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Insight 106: “The oft-asked question in response to this intense focus on one’s immediate geography is, “What about the ends of the earth?” or “How do we take responsibility for our own COA (Circle of Accountability) while also ensuring that the gospel gets to places where there is not a known expression of the local church?” We might posit the answer to this question using the image of a telescope. A telescope takes something far away and brings it into focus, allowing you to see and make observations about some distant object. A local church can telescope its efforts as well.” (Gospel Saturation Primer, p. 63)
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The long-awaited return of Becky Lynch finally happened at SummerSlam, and she replaced Sasha Banks in a surprising victory over Bianca Belair for the WWE SmackDown Women's Championship. One of the biggest questions in WWE revolved around Lynch's return, more specifically when she would make her return to the ring. Lynch has had some fun trolling fans with teases of being backstage at various events, but she has yet to actually appear on television, and sometimes she wasn't even at the event. Some thought that might be different coming into SummerSlam, as it would make for one hell of a return and buzzworthy moment for WWE's second-biggest show of the year. That said, some also thought the mysterious absence of Sasha Banks from SmackDown might speed up Lynch's return if she couldn't make the show, and while Carmella came out, it was Becky Lynch who came out to dave the day and take her spot.
The crowd was on their feet for the reveal, and so happy to see Lynch back in WWE. Belair was shocked as well, but extremely happy, while Carmella looked visibly upset. Belair was ready though, but the match did not go the way the Champion hoped. Belair and Lynch would lock up after the bell rang, but soon after Lynch would hit the ManHandle Slam and pin Belair clean for the win, and with it, she became once again SmackDown Women's Champion.
Belair looked visibly upset and disappointed, but she lifted her chin and looked like she will be looking for a rematch very soon.
Previously Banks and Bianca Belair had been pulled from a live show for undisclosed reasons, and that's when fans started worrying about an injury or health issue keeping them out of SummerSlam. They were confirmed for the show by WWE later, but then Banks was not at the SmackDown before SummerSlam, though Belair was.
As for Lynch, she instantly brings new energy and star power to whichever brand she ends up calling home, though the assumption would be that she joins Raw since she was previously Raw Women's Champion and never lost the title, as she relinquished it to Asuka after announcing her pregnancy. That said, she was also SmackDown Women's Champion before that and held both titles at the same time, so her options are plentiful.
Her possible opponents are also plentiful, as there are a number of names that come to mind in both brands. Rhea Ripley, Nikki A.S.H., Sasha Banks, Bianca Belair, Toni Storm, Zelina Vega, Carmella, Alexa Bliss, Shotzi Blackheart, and Tegan Nox could all use a new opponent, so we'll have to wait and see what WWE decides to do.
Here's the current full card for this weekend's big event:
Roman Reigns (C) vs John Cena (Universal Championship)
Bobby Lashley (C) vs Goldberg (WWE Championship)
Bianca Belair (C) vs Sasha Banks (SmackDown Women's Championship)
The Usos (C) vs The Mysterios (SmackDown Tag Team Championships)
AJ Styles and Omos (C) vs Randy Orton and Riddle (Raw Tag Team Championships)
Sheamus (C) vs Damian Priest (United States Championship)
Edge vs Seth Rollins
Alexa Bliss vs Eva Marie
0comments
Drew McIntyre vs Jinder Mahal
What did you think of Becky's big return? Let us know in the comments or as always you can talk all things WWE with me on Twitter @MattAguilarCB!
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Writer: Brian Azzarello Artist: Jim Lee Publisher: DC Comics Release Date: September 29, 2004 Critic Reviews: 1 User Reviews: 3
8.0Critic Rating
7.7User Rating
Pushed to his mental limits, Superman must face the Earth, itself, when a magical attack awakens the spirits of Gaia. Is The Man of Steel willing to literally fight the world he's sworn to protect? Find out in this halfway-point issue of ''For Tomorrow''!
Get a copy: AmazonComixology
Critic Reviews (1)
User Reviews (3)
Rate / Write A Review
8.0
Comics Bulletin - Judson Miers Oct 20, 2004
As far as Im concerned, this storyline is as good as anything Marvels published in quite a while. (Some of you remember my Marvel bias.) This may be due to the fact that DCs just kicked it up a notch, or Ive still got the bad taste of ASM 512 in my mouth, but either way, keep up the good work! Ill at least finish this storyline, and who knows, maybe continue to pick up Superman into the future Read Full Review
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Newcastle United 2 - 2 Reading FC: Hand of Ba Rescues Newcastle From Abysmal Effort - Coming Home Newcastle
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Newcastle United 2 - 2 Reading FC: Hand of Ba Rescues Newcastle From Abysmal Effort
New, comments
Given that Yohan Cabaye's absence was always going to be a big miss, Newcastle United did just what you thought they'd do. They took the pitch at the Madejski Stadium feeling that their name would win the match for them.
By Jim McMeachin Sep 29, 2012, 4:58pm BST
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Alex Livesey - Getty Images
With Yohan Cabaye out due to an infection related to having his wisdom teeth out, Alan Pardew started a midfield combination of Gael Bigirimana and Cheik Tiote. Otherwise, in terms of the outfield players, it was close to a first-choice save for James Perch's inclusion as Danny Simpson is not yet recovered from his injury. As such, the person at large would have thought Newcastle United still had enough quality in their side to take a result from a newly promoted side with one earned on the season. The problem is, someone told the team that.
As I mentioned in the preview, the search for consistency has not turned up much, but some encouraging form at times v. Norwich and v. Manchester United midweek, it seemed like we were on the verge of finding our best form from last year. Two things happened at Reading that were disappointing, however:
The partnership of Bigirimana and Tiote is not ready for prime time. While they managed to play well defensively for large sections of the match, neither one of them was ready or willing to hold or play the ball within the offense in the middle of the pitch. Granted Tiote managed a great through ball for Ba's first goal, but we were dominated through the middle of the pitch. Bigi is going to be great eventually... Tiote still seems a little off... but this was the beginning of the breakdown for Newcastle United. Well... not the beginning.
Newcastle United took the pitch at the Madejski and felt that their name would win the match for them. While no club EVER earns that right (not even Manchester United, Barcelona or any other club), Newcastle are the least club that would have earned it if they could. Since our promotion to the Premier League, we have struggled every. year. to newly promoted sides. If there was a match that we didn't think we could walk in and win without any effort, it should have been this one.
This effort was abysmal, and Reading will rightly feel harshly done by to not have earned the 3 points from this match as they were the better team for all 90 minutes and Ba's equalizer was mildly illegal and all... If Newcastle United truly have Top-8 aspirations, they're going to have to get this sorted and sorted soon. We have been unlucky with injury for sure, but there is a general lack of attitude and effort that is troubling thus far this season.
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Thanks for choosing to setup a Fuel Cards account with us. You’re just a few steps away from fixed prices, additional security and up to 14 days interest free credit.
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Ford Fuels Ltd – Fuelcard Terms & Conditions1.DefinitionsIn these Terms and Conditions the following words, terms or expressions shall have the following meanings:-“Ford Fuels” means the company Ford Fuels Ltd who registered office is at The Oil Depot, Farrington Fields Trading Estate, Farrington Gurney, Bristol, BS39 6UU.“Authorised Cardholder” means a person to whom the Principle Cardholder has provided a card and who is authorised by the Principle Cardholder to use a card.“Card” means all cards issued to the Principle Cardholder by Ford Fuels.“Cardholder” means the Principle Cardholder and, where applicable, any Authorised Cardholder.“Diesel” means diesel engine road fuel.“Directory” means the list of Sites issued from time to time by Ford Fuels showing where the card can be used to obtain fuel.“Fuel” means Diesel, Gas Oil, Petrol and Lubricants.“Gas Oil” means fuel for use in any industrial or agricultural diesel engine and as a burner fuel.“Lubricants” means oil or grease intended for lubricating.“Petrol” means petrol engine road fuel.“PIN” means Personal Identification Number which shall be issued to every Cardholder for use with a card.“Principle Cardholder” means the partnership, limited liability partnership, firm, corporate body or other person to which cards are issued.“Site” means any services station or depot at which the Card may be used.“Vehicle” means the Principle Cardholder’s or Authorised Cardholder’s vehicle for which fuel is obtained details of which have been supplied to Ford Fuels.“Working Day” means Monday to Friday 9.00am to 5.00pm excluding public holidays2.Acceptance of Terms and ConditionsSignature of and / or use of a Card by the Principle Cardholder or an Authorised Cardholder constitutes acceptance of these Terms and Conditions3.Card Issue3.1The Card remains the property of Ford Fuels at all times and shall be returned by the PrincipleCardholder promptly at Ford Fuels request. Ford Fuels shall have the right to collect the card.3.2The Principle Cardholder shall be liable to Ford Fuels for the proper use of the card by the Principle Cardholder or any Authorised Cardholder prior to the return of a Card in accordance with condition 11.3.3The Authorised Cardholder shall be deemed to have been given authority to use the Card as a duly authorised representative of the Principle Cardholder and the Principle Cardholder shall ensure that the Authorised Cardholder complies with all these Terms and Conditions.3.4The card may contain the registration number of a Vehicle in which case it shall be used only to obtain Fuel for that Vehicle or where it does not contain a registration number it may be used for any Vehicle.3.5The Principle Cardholder shall return unexpired Cards that are no longer required, or where the Vehicle registration has changed to Ford Fuels requesting cancellation in writing.3.6Unless advised otherwise, Cards will automatically be replaced by Ford Fuels prior to expiry3.7The Principle Cardholder shall use its best endeavours to ensure that the Card is kept safe and undamaged. If damaged, the card must immediately be returned to Ford Fuels by the Principle Cardholder for replacement by Ford Fuels.4.Card Use.1A card shall not be valid unless the signature strip on the reverse of the Card has been completed in accordance with the instructions issued by Ford Fuels from time to time.2A card may only be used:(i)if it is a current Card which has not expired, been cancelled or which does not appear on the Ford Fuels Stop List;(ii)by a Cardholder to obtain Fuel up to the applicable facility credit limit issued in accordance with condition 6.(iii)to obtain Fuel up to the maximum value per transaction as specified by Ford Fuels from time to time;(iv)if the card has not been reported lost or stolen;(v)in the case of a staffed site if the Principle Cardholder or the Authorised Cardholder presents or shows the Card to the Site prior to obtaining Fuel;(vi)by any cardholder to obtain Fuel for the Vehicle identified on the Card (where applicable).4.3When using a Card;(i)to obtain fuel at an automated site, the Cardholder shall use the Card with its unique PIN (where applicable) in the designated machine. Each transaction will be recorded on a delivery voucher which will be dispensed to the Cardholder on completion of each transaction;(ii)to obtain Fuel at a staffed site, the cardholder shall use the card alone and the transaction will be recorded on a delivery voucher a duplicate of which will be dispensed to the cardholder on completion of each transaction.4.4It is the obligation of the Principle Cardholder or Authorised Cardholder to collect and retain any delivery voucher (duplicate or otherwise) issued at the time Fuel is obtained4.5When obtaining fuel the Principle Cardholder and any Authorised Cardholder shall comply with these Terms and Conditions and all procedural requirements of the Site in respect of the relevant card transaction and shall ensure that the Card is only used in accordance with condition 4.2.4.6If any procedural requirements are not complied with or the Card is used other than in accordance with these Terms & Conditions, the Principle Cardholder shall nevertheless remain liable to pay Ford Fuels for all amounts due to Ford Fuels under the relevant Card transaction.4.7Cards that identify Vehicles are issued to the Principle Cardholder as a management information tool to be used correctly by the Principle and Authorised Cardholders. Such cards do not provide additional security. The Principle Cardholder shall be liable to pay Ford Fuels for all amounts due to Ford Fuels under the relevant Card transaction, including where the transaction is in respect of a Vehicle other than that identified on the Card.15.PIN.1The Principle Cardholder and the Authorised Cardholder shall use the PIN if so required at an automated Site.2The PIN shall only be used by the Principle Cardholder and the Authorised Cardholder to whom it is supplied and shall not be disclosed directly or indirectly to any other person..3The PIN shall not be written on any card or delivery voucher or on anything kept with the Card.6.Account LimitFord Fuels shall impose and notify to the Principle Cardholder an overall facility credit limit and (where applicable and not in addition to) a daily credit limit on the use of the Cards and if any applicable financial limit is exceeded then, without prejudice to Ford Fuels right to recover all amounts due from the Principle Cardholder, Ford Fuels may terminate this agreement immediately on notification to the Principle Cardholder.7.InvoicingInvoices will be sent to the Principle Cardholder at the billing period intervals determined by Ford Fuels from time to time detailing Card transactions and any additional charges for the relevant billing period.8.Payment.1Unless indicated otherwise by Ford Fuels, payment shall be made by direct debit seven days following invoice.2If any amounts are not paid by the due date then (without prejudice to Ford Fuels right to terminate this agreement)(i)interest shall be payable on the outstanding balance at the rate set under s6 of the Late Payment of Commercial Debts (Interest) Act 1998.(a)calculated (on a daily basis) from the date of our invoice until payment(b)compounded on the first day of each month; and(c)before and after any judgment (unless a court order specifies otherwise);(ii)Ford Fuels may claim fixed sum compensation from you under s5A of that Act to cover our credit control overhead; and(iii)Ford Fuels may recover the cost of taking legal action to make you pay..3Any dispute relating to any delivery voucher or invoice issued by Ford Fuels must be notified to Ford Fuels in writing within 14 days of receipt..4Any payments made by credit card will be subject to a 2% processing charge which may be varied from time to time.9.No Set Off.1All payments made by the Principle Cardholder or any credits or refunds due to the Principle Cardholder shall be applied first in settlement of any interest due and secondly by Ford Fuels in its absolute discretion in reduction of any amount due to Ford Fuels on any account whatsoever..2No set off or counter claim against Ford Fuels shall be made in respect of any claim by the Principle Cardholder or any Authorised Cardholder.10.Charges.1Ford Fuels may charge the Principle Cardholder a fee on the issue of the Card and/or on the renewal or replacement of the Card and/or for administration each year commencing on the date of issue of a Card and/or on each use of the Card and/or for each purchase of Gas Oil, Petrol and Lubricants and/or on each use of the Card “Help Line”..2Such fees have been notified to the Principle Cardholder and can be increased by Ford Fuels on giving 30 days notice in writing to the Principle Cardholder.11.Cancellation of Cards.1Ford Fuels will cancel a Card if the Principle Cardholder requests cancellation in writing and encloses the relevant Card cut in half.2Ford Fuels may request the return of all/any Cards or cancel or suspend all/any Cards at any time without notice or refuse to reissue replace or renew any Card.3A request for return or cancellation or suspension is without prejudice to the Principle Cardholder’s liability in respect of use of any cards prior to the actual return of the relevant card to Ford Fuels cut in half.12.Lost or Stolen Cards.1If a Card is lost or stolen or remains in the possession of a person who has ceased to be an Authorised Cardholder the Principle Cardholder must immediately notify Ford Fuels at The Oil Depot, Farrington Gurney, Bristol, BS39 6UU or at any other address, email address, fax or telephone number as Ford Fuels may specify from time to time and also notify the police of the loss or theft and obtain a crime reference number or lost property reference number..2If notification of loss or theft of a Card is given verbally it must be confirmed in writing within one Working Day. Such notification must include the card number and the cardholders name and Vehicle details. Provided such notice is given before 1300 hours on a Working day, the Principle Cardholder shall be liable in respect of any delivery vouchers issued against the lost or stolen Card until one Working Day after notice in respect of automated sites capable of accepting “Stop List” updates via electronic data transfer or in the case of staffed sites 3 Working Days after such notice. If no such notice is given the Principle Cardholder shall remain fully liable..3The Principle Cardholder shall be liable in respect of any use of a Card by any Person before notification in accordance with condition 12.1. After Ford Fuels has been properly notified and provided that this condition 12 has been complied with in full, the Principle Cardholder shall have no further liability for Card transactions made with that Card other than for Card transactions made by the Principle Cardholder or by a person who has ceased to be an Authorised Cardholder where the Principle Cardholder has allowed the Card to remain in the possession of that person..4The Principle Cardholder shall ensure that no Card remains in the possession of any person who has ceased to been an Authorised Cardholder..5The Principle Cardholder shall give Ford Fuels all the information in its possession as to the circumstances of the loss or theft and take all reasonable steps to assist Ford Fuels to recover any missing or stolen card.13.Information2.1Information supplied by a Cardholder and/or which relates to a Principle Cardholders account(s) will be held and processed by computer or otherwise by Ford Fuels to operate the Principle Cardholders account(s); to confirm, update and enhance Ford Fuels customer records and for statistical analysis to establish and Cardholder’s identity (where required); and to assess the Principle Cardholder’s credit status (for this purpose credit scoring techniques will be employed and checks may be carried out with a credit reference agency and a fraud detection system, who will maintain a record of any such enquiry).2Ford Fuels may disclose data relating to a Cardholder and/or the Principle Cardholder’s accounts to (1) a credit reference agency where it may be accessed by other financial institutions to assist assessment of any application of credit made by Ford Fuels and for occasional debt tracing and fraud prevention; (2) to any agent or sub-contractor of Ford Fuels; (3) to any person to whom Ford Fuels propose to transfer any of its rights and/or duties under the Agreement; (4) to any guarantor or person providing security in relation to the Cardholder’s obligations under any agreement with Ford Fuels; (5) to insurance companies for purposes connected with insurance products that relate or might relate to the Principle Cardholder’s account(s); (6) to any organisation which endorses the Card; (7) as otherwise required or permitted by law or any regulatory authority and in each case persons may be located in countries outside the United Kingdom that do not have laws to protect you information Details of the countries involved will be provided on written request by the Cardholder..3Ford Fuels may use information about any applicant or Cardholder and its account(s) to send the Cardholder information about other goods and/or services offered by Ford Fuels and/or third parties which Ford Fuels believes may be of interest to the cardholder. Cardholders may be contacted about other goods and services by any means (including telephone and email). Any Cardholder that does not wish to receive this information should write to Ford Fuels at The Oil Depot, Farrington Gurney, Bristol, BS39 6UU..4The principle Cardholder shall immediately notify Ford Fuels of any changes in the Principle Cardholders address or bank details or any other relevant details. Ford Fuels reserves the right to renew or take up bank references from time to time and the Principle Cardholder shall authorise the bank to provide such references upon request..5Without prejudice to any other provisions for termination contained in these Terms and Conditions all monies due and owing by the Principle Cardholder to Ford Fuels shall become due and payable forthwith if Ford Fuels discovers that any information provided by the Principle cardholder to Ford Fuels is materially inaccurate.14.TerminationThis agreement may be terminated(i)by either party, at any time, by giving not less than one month’s prior written notice to the other party;(ii)by Ford Fuels with immediate effect and without notice if:a.the Principle Cardholder is in breach of any of these Terms and Conditions or any other provisions of the agreement;b.the Principle Cardholder (as applicable) goes into or in the reasonable opinion of Ford Fuels is likely to enter into receivership, administrative receivership, administration, bankruptcy or liquidation or has similar proceedings taken against it or any event occurs which in the opinion of Ford Fuels may affect the ability of the Principle Cardholder to comply with any or all of its obligations or meet any of its liabilities under the Agreement;c.any other circumstance arises which gives Ford Fuels a termination right under these Terms and Conditions.(iii)By Ford Fuels with effect upon notification to the Principle Cardholder if Ford Fuels receives a credit reference (which the Principle Cardholder hereby agrees Ford Fuels may carry out from time to time on the Principle Cardholder) which in the opinion of Ford Fuels is unsatisfactory.15.Effect of TerminationOn termination of this agreement the whole outstanding balance of the Principle Cardholder’s account shall become due and payable in full to Ford Fuels and the right of the Principle Cardholder and any Authorised Cardholders to use a card shall cease (but without prejudice to the Principle Cardholder’s liability for use of Cards after termination or to the rights of Ford Fuels already accrued at the date of termination).16.Liability.1Ford Fuels gives no warranty, express or implied and whether arising by common law or statute, in relation to any Fuel sold to the Principle Cardholder. Ford Fuels shall not be liable for any loss or damage suffered by the Principle Cardholder or any Authorised Cardholder or by any third party in connection with any Fuel or the use of any Card save to the extent that such liability cannot by law be limited or excluded..2Ford Fuels (or any subcontractor or agent of Ford Fuels) shall not be liable to the Principle Cardholder in respect of any inaccurate information on obtaining Fuel made by use of a Card when such inaccuracy is caused by incorrect or incomplete information provided by the Principle Cardholder, any Authorised cardholder or a site, whether on a deliver voucher or otherwise;.3Ford Fuels gives no representation or warranty, express or implied and whether arising by common law or statute that any computer software or data (which shall include without limitation disks, tapes, hard drives or any other form of programme or file delivery, tangible or intangible and whether delivered to the Principle Cardholder electronically via a communications network or otherwise) (“Data”) supplied to the Principle Cardholder is free from any computer virus or other malicious programming or is accurate or is confidential or is compatible with the Principle Cardholders computer systems or that there is no functionality included which is incompatible with the Principle Cardholders computer systems. Ford Fuels shall not be liable for any disruption in the Data transmission, errors or omissions in the content of the Data, or loss or damage which may arise by the delivery of the Data, save to the extent that such liability cannot be limited or excluded..4Ford Fuels shall not be liable in any way if a Site refuses to provide Fuel for any reason..5Ford Fuels shall not be liable in any way to the Principle Cardholder or any Authorised Cardholder or any third party for the fraud, negligence, act, default, omission or wilful misconduct of Ford Fuels, any Site or its or their representative employees contractors and agents, save to the extent that such liability cannot be limited or excluded..317.PricesPrices for Diesel are Ford Fuels prices less any rebates that it has agreed to give the Principle Cardholder as at the date that Ford Fuels receives its copy of the delivery voucher. Prices for Gas Oil, Petrol and Lubricants are the full re-sale value at the site plus the prevailing fee levied by Ford Fuels on these products. Ford Fuels shall not be liable to the Principle Cardholder for any delay in providing a duplicate of the delivery voucher which is due to a matter beyond its reasonable control. Prices are subject to any increases which are equivalent to new or increased taxes, duties or any other sum which the Government imposes on the sale of Fuel by Ford Fuels up to the time of receipt of the delivery voucher by Ford Fuels.Price of Fuel drawn at Premium Sites may incur a surcharge. A list of these sites is available upon request.Price of Fuel drawn at Tesco Branded sites will be charged at the full poll sign price plus 3.50 pence per litre18.Variations.1Ford Fuels may vary, add or delete any of these Terms and Conditions (including without limitation any terms contained in written correspondence) and impose new terms and conditions for any other card scheme which is essentially the same as and replaces the current card scheme (whether or not such new card scheme is operated by Ford Fuels or by a third party on behalf of Ford Fuels)..2The use of any card after notification of any variation to these Terms and Conditions or the imposition of new terms and conditions shall be deemed acceptance of the varied or new terms and conditions by the Principle Cardholder.19.Transfers.1This agreement is personal to the Principle Cardholder and the Principle Cardholder shall not be entitled to assign, transfer, mortgage or charge all or any of its rights interests or obligations hereunder except by way of a floating charge created in the ordinary course of business..2Ford Fuels shall be entitled in its absolute discretion without consent of the Principle Cardholder to assign, transfer, mortgage or charge all or any of its rights, interests or obligations hereunder including, without limitation, condition 8.20.Joint and Several LiabilityWhere the Principle Cardholder consists of two or more persons the obligations of the Principle Cardholder shall be joint and several21.HeadingsThe headings used in these Terms and Conditions are for convenience and shall not affect their interpretation.22.Notices Subject to the express requirements of these Terms and Conditions and to the requirements of any other statute or other regulation, notifications by such means as Ford Fuels may select shall constitute effective notice under these Terms and Conditions.23.Force MajeureFord Fuels shall be under no liability to the Principle Cardholder for failure to perform its obligations under the Agreement due to circumstances that are beyond the reasonable control of Ford Fuels, its agents or contractors.24.Waiver Any neglect forbearance or indulgence on the part of Ford Fuels relating to its strict rights under these Terms and Conditions shall in no way be deemed to be a waiver, implied or otherwise, to those rights.25.Third Party RightsThe Agreement, including these Terms and Conditions, confers benefit to Ford Fuels. The provisions of the Agreement as a whole are intended to be enforceable by Ford Fuels by virtue of the Contracts (Rights of Third Parties) Act 1999 (the “Act”). Save as described above, the parties do not intend that any terms of the Agreement should be enforceable by virtue of the Act, by a person who is not a party to the Agreement26.Law and JurisdictionThis Agreement shall be governed by English law and shall be subject to the exclusive jurisdiction of the English Courts.4
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RH10: Index and TOC don't appear in Chrome on Mac
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RH10: Index and TOC don't appear in Chrome on Mac
RH10: Index and TOC don't appear in Chrome on Mac
pgcalc
Explorer ,
/t5/robohelp-discussions/rh10-index-and-toc-don-t-appear-in-chrome-on-mac/td-p/10644263 Oct 01, 2019 Oct 01, 2019
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I have been using RH10 for several years now to generate WebHelp for two of our products. We have noticed that when we open Help while running these products in Chrome on a Mac, the Index and TOC are empty. Blank. This was not always the case, but I cannot pinpoint exactly when the problem started. The Index and TOC render fine in Safari on the Mac and in Chrome on Windows 10.
Has anyone else encountered this issue? Can anyone suggest a solution? Might upgrading to RH 2019 be the answer?
Thanks in advance for any help you can offer!
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Jeff_Coatsworth
Adobe Community Professional ,
/t5/robohelp-discussions/rh10-index-and-toc-don-t-appear-in-chrome-on-mac/m-p/10644283#M126591 Oct 01, 2019 Oct 01, 2019
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There's been a number of browser-security "fixes" that have messed up WebHelp in older versions (recently it was a FireFox Quantum update that knocked off all locally-hosted WebHelp) - moving to RH2019 will help you get on top of those changes, but it won't be with WebHelp - it's not on offer anymore - replaced by the new shiny HTML5! I suggest you download a copy of RH2019 as a trial on a non-production machine, update it (it doesn't come with the 9 patches already released) & test out a copy of your project on it to see how it behaves.
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Peter Grainge
Adobe Community Professional ,
/t5/robohelp-discussions/rh10-index-and-toc-don-t-appear-in-chrome-on-mac/m-p/10644326#M126592 Oct 01, 2019 Oct 01, 2019
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I hope Jeff won't mind me clarifying his point about webhelp not being available.That only applies to the new UI version. There is also a Classic version where it is still available and where the UI is similar to the version you are using. Upgrading gives you both versions.
The only difference between webhelp and responsive help apart from skin design is that the responsive help changes according to screen size whereas webhelp does not. In the new UI version, if you don't want the layout to change according to screen size, that can be turned off. Effectively you would still have webhelp.
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pgcalc AUTHOR
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/t5/robohelp-discussions/rh10-index-and-toc-don-t-appear-in-chrome-on-mac/m-p/10656923#M126710 Oct 08, 2019 Oct 08, 2019
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Thank you for both responses. I will give RH2019 a try on a copy of my project and see how it goes. Fingers crossed!
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https://community.chocolatey.org/packages/dotnetcore-runtime.install/1.0.15/ContactOwners
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community.chocolatey.org
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2,608,056 |
To protect the wiki against automated account creation, we kindly ask you to answer the question that appears below (more info):
Finish this phrase with a number:
Falcon School District
Username
Password
Confirm password
Email address (optional)
Real name (optional)
Real name is optional. If you choose to provide it, this will be used for giving you attribution for your work.
|
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"warc_headers": {
"warc-record-id": "<urn:uuid:180421b9-0a30-4ff9-bf1a-0b4f0a8e3514>",
"warc-date": "2021-11-27T06:33:56Z",
"content-type": "text/plain",
"content-length": 379,
"warc-type": "conversion",
"warc-identified-content-language": "eng",
"warc-refers-to": "<urn:uuid:5341b303-8f26-42a2-b2a2-9cc87a815312>",
"warc-target-uri": "https://community.d49.org/index.php?title=Special:UserLogin&returnto=Special%3ARecentChangesLinked&returntoquery=days%3D3%26from%3D%26hidemyself%3D1%26limit%3D250%26target%3DTemplate%253AOmbox%252Fcore&type=signup&printable=yes",
"warc-block-digest": "sha1:2S36FWS23JHFZJKSQ2WYZZQKYE7EUJD7"
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| 676.7 | 379 |
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2,608,057 |
If this is your first visit, be sure to check out the FAQ by clicking the link above. You may have to register before you can post: click the register link above to proceed. To start viewing messages, select the forum that you want to visit from the selection below.
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#1
cannot get any crests after third city addition
01-01-1970, 12:13 AM
Ever since the third city was added in my world I can't get any more crests from pict camps. I have attacked dozens of pict camps during several days and I haven't received not even one crest. Before this addition I was getting crest and I already had 11. What is going on Kabam? I am getting ready to quit this game.
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darius94
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Join Date: Jul 2015
Posts: 1
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#2
07-02-2013, 08:24 PM
@Elicabeth85: Thank you for reporting this issue. In which world are you playing in?
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#3
07-05-2013, 03:05 AM
I'm not sure if my crest hunting problem is the same as Elizabeth85's but I attacked 11 level 7 pict camps and 4 level 6 pict camps without a single crest drop. These 15 attacks happened July 4th. I'm in world 76 and username is FelixTheCat.
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#4
07-06-2013, 08:00 AM
Originally posted by Jennifer View Post
@Elicabeth85: Thank you for reporting this issue. In which world are you playing in?
I am playing in pellinore71 and two alliance members in the same world are having the same issue with the crests.
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#5
07-08-2013, 09:06 PM
Same thing is happening in 407!! This is complete crap. It started back in april, then when kabam introduced all the new cards...it seemed to get better for a few days. But since July 4th I have noticed it happening again. Today i have hit HUNDREDS of dark forests and have received 6 freaking cards!!!!!!!! So I tried a level 10 castle next to me, after 2 hours and approximately 500 attacks. NOT ONE SINGLE CARD!!! Seriously if you guys don't get this fixed, i'm done. You don't stand a chance in the game without a good TR...how can you have a good TR if you never have any cards to work on??? Come on Kabam...if your sooooo busy that you can't fix huge bugs like this..then either hire more people or figure something out. I have sent numerous "support tickets" and the only stupid respone i get is,
Hello,
Thank you for taking the time to contact Kingdoms of Camelot support. We sincerely apologize for not being able to answer you in a timely fashion.
As we have recently encountered a surge of player requests, we are unfortunately unable to provide your ticket the attention it deserves. While this does not make your request any less important, each ticket must be answered in the order it is received.
Though it may not directly address your concerns, our game team has been hard at work on resolving many of the major in-game issues that players like yourself have reported. We do hope that any changes made have improved your gaming experience.
If you are still having this issue, however, please let us know by either re-submitting your ticket or directing your concerns to our forums. You may even find the solution to your inquiry posted here. If not, rest assured our Developers pay close attention to the issues that surface here, and can work to see them resolved immediately.
You can access our forums through the following link:
http://community.kabam.com/forums/fo...oms-of-Camelot
We apologize again for any inconvenience that you've experienced. Thank you so much for your patience and understanding.
Kingdoms of Camelot Support
Not so helpful!!!!!!!!!!!!!! I have report it 5 times...all 5 times...I get this message back.
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bapatrickmnr9884
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#6
07-11-2013, 10:27 PM
I'm sorry to feel so callous (really I am...I've invested a lot of time in KoC...and money) but I doubt this is a tech support glitch.
Kabam has the ability, and exercises it, to rejigger many features of the game when it suits their purpose (monetizing the game, often). I believe they made a business decision to force people to buy the fourth city deeds, and they did so by making the earning of a deed via crests nigh-on impossible to achieve.
They've paid lip service to this issue but I believe it is facetious if not outright deceptive. Perhaps the biz managers there have kept their support reps in the dark, so the reps are being authentic in asking for trouble tickets, etc. But come on...every world, ever since Fourth City launched, has been ratcheted down as far a crest drops. The crest yield has never exceeded 10% of what it was for 2nd and 3rd City, imho.
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This email is about my college decision, status regarding the Baclofen pump, and my feelings concerning how being disabled influences your views about death. Take care and be strong...God is always with me, I know. **cross posted**
Hi Dr. J.
My meeting at Daemen yesterday went well. The campus and apartments are reasonably accessible, and between CASA, VESID, and my parents, we should be able to cover 24 hour care. Daemen even offered to give my aid a room in my apartment at night, which should be covered under my room and bored deposit. If I decide to go and after a few weeks, we decide the dorms aren't working out, I will get my deposit back and my spot will be filled very quickly. On the other hand, if I end up at Edinboro and decide to come back, I will be able to dorm then as well. With a good chance that we can achieve satisfactory accommodations during the actual school day, it's now up to me to make a decision based on my comfort level and career goals. I told Daemen that I would give them a tentative answer on Monday, but a final after my meeting at Edinboro on the 23rd. Right now, after talking to my Dad this morning, I'm learning toward Edinboro for at least a semester; I'll know what to expect as far as accommodations and personal care if decide to come home. It's a lot to consider and a hard decision of course, but that's beside the point; it has to be done soon.
I just don't know if I'm mentally and emotionally ready to grow up go away. Why do you think I'm ready now, as opposed to last year for instance? This issue is exactly why I think I might benefit from some type of psychoanalytic therapy; so I can go back to my childhood, and maybe let this anger, resentment, and whatnot go. Even though it's extremely hard to write, let alone talk about what I remember, I think doing so is the only way I'll be able to let go and grow up. No to mention be comfortable with the physical part of my disability, as well as when I'm alone in my room at night too.
As for my appointment with Lynn at Dr. Kinkel's office, all my lab work came back normal. She thinks the point at the bottom of the pump is bothering me because I'm so thin...other people usually don't feel it because there is more muscle and tissue to help cushion it. If it's ok with Dr. Moreland and the x-rays look normal, Lynn is going to try to move the pump back into place manually next time I see her. If it doesn't stay in place, we might have to go back in and re-stitch it. I am now on 50 micrograms daily, which means I'm able to go into "flex" next time. I will be getting more Baclofen at night, during the hours I have the most pain...I'm keeping a chart.
That's about it, except that my mom's great aunt did pass away this afternoon. The funeral arrangements are being made tomorrow morning, so the small mass will be this week. I think it's bothering me more because I'm afraid of death myself. I have come close to death many times due to the CP, so I can't seem to shake this feeling of fear.
Take care! Talk to you soon!
Holly
Tags: college plans, emotions, faith, holly-mod, medical, seeking support, therapy
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I finally managed to finish Max Payne 3. High budget, lovely graphics, fun game, good shooting. A lot of cutscenes, but I believe these were used to help mask loading times between areas (and helps to explain why the game is a whopping 30GB which I'll be glad to get rid of). I tried a bout of the multiplayer but it felt pretty bad, lacked pacing and seemed to take ages between respawn timer finishing, and then actually respawning in the game.
Max Payne 3 article is pretty much complete. I would like to do some more documentation with Rockstar Social Club by comparing notes with GTAIV and LA Noire, or if someone wonderful can do this that would great :). Need to test controller support, and then I can delete.
Next game.. I may try Batman: Arkham Asylum. I did play a bit of it on release but was too annoyed by GFWL implementation. Of course those previous savegames are now lost to the ether. I wonder if savegames are Mac/Windows cross-compatible, will try to test this.
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Nicereddy
August 17, 2013
Team Fortress 2 - Per usual, this is my game of choice. I'm nearing 800 hours in the game and still don't have an unusual hat. I main soldier and have been experimenting with different loadouts since
Andytizer
August 17, 2013
That's awesome Nicereddy. I also wanted to say I've also been getting into Dota 2 as well, particularly with playing co-op bot matches and watching bits and pieces from The International 3, which has
CheighLee
December 9, 2013
Spec Ops: The Line is collecting "dust" for some time in my library and I finally sat down to play it. Good game for now :D
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Newmansan 30
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Posted November 21, 2013
Titan Quest, it's adequate.
Gravity Rush. I'm enjoying it.
Killzone Shadowfall: Boring and inconsequential but pretty
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Andytizer 268
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Posted November 21, 2013
Titan Quest, it's adequate.
Gravity Rush. I'm enjoying it.
Killzone Shadowfall: Boring and inconsequential but pretty
In a world of Diablo 2, Diablo 3, Torchlight II and Path of Exile there's hardly a reason to play Titan Quest anymore. I certainly found it not nearly as good as Diablo 2 which it tried to emulate.
How's the PS4?
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Pridit 39
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Posted November 21, 2013
Been getting back into Supreme Commander recently. Huge fan of the RTS genre - one aspect I hate is that if you just spam units you automatically win - this is applicable for most RTS but it really should be rectified, it's near impossible to win against normal AI just playing strategically/tactically as they will eventually send every unit they have over to you and wipe you out instantaneously. If you somehow manage to defend then they'll keep sending twice as many as they previously sent meaning that you can't hope to fully defend yourself solely via structures.
I gave Planetary Annihilation a bit of a whirl but I hate how you were always exposed in every direction, unless you fortified the entirety of your base you would get flanked where there is no defense. Meaning again that spam units = win. Maybe it's not like that as I haven't given it enough of a chance but I intend to dive back into it at a later stage when it's a bit more stable as I'm having performance issues with the beta.
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Newmansan 30
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Posted November 21, 2013
How's the PS4?
All things considered I'm enjoying it. Killzone is a good tech demo, but a boring game. Knack is OK. (It feels a bit like going back and playing Crash Bandicoot 1 after Playing Crash Bandicoot 3. More difficult and a bit less fun than perhaps I was expecting). Assassin's Creed 4 plays exactly like you'd expect and looks good (I don't have the last gen version to compare it to though) Need for Speed Rivals is an absolute blast the very little I've played of it. Resogun is fun, and Contrast looks intriguging.
Not a knockout set of launch games, but they do fine. I can't wait for Infamous.
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CheighLee 1
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Posted December 9, 2013
Spec Ops: The Line is collecting "dust" for some time in my library and I finally sat down to play it. Good game for now :D
IAmWeasel
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IAmWeasel 0
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Posted March 26, 2014
Heroes of Might & Magic III - whatever happens I always get back to this game. It's just the closest thing to the perfect game.
Trine 2 - Still fun in coop, although I know all maps by heart.
Spectraball - Steam Roulett choose this one. Nice, little, short.
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Andytizer 268
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Posted May 9, 2014
I've been playing tons of FTL lately, and I've finally managed to beat the game on Hard mode using The Shrike ship (one of the new Advanced Edition race ships). I love how that ship allows you to start with a teleport/mind control combo at the beginning as it makes for a killer raiding party combination. The key to winning on the Shrike is to pick up a Drone Control with a defense drone to shoot down incoming missiles, and then you are basically invincible.
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Jamieb 0
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Posted July 12, 2014
Right now I'm playing Dead Island: Riptide and BattleBlock Theatre
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Andytizer 268
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Posted November 18, 2019
Resurrecting this thread! What I've been playing:
Katana Zero
Really enjoyed this game. Reminded me a lot of Hotline Miami, both in terms of the story and gameplay. Plot-wise, it was an interesting psychological experience with some predictable twists and turns. Gameplay is also similar to Hotline Miami in that it is one-hit kill action that you can repeat instantly as soon as you make a mistake. Similar as well is how frantic it gets - you can clear a whole level except for the last enemy, who might behave unpredictably and then get a lucky hit and you have to restart the entire section again. Great game!
Apex Legends
I've been playing this game since launch, and it's the only real Battle Royale game I actually like (compared to Fornite/PUBG which I bounced right off). Was fairly addicted to this game and would spend significant amounts of time outside of gaming watching players like Shroud play the game on YouTube. However I haven't really touched it for the last few weeks so it's definitely lost some allure for me and it reminds me that competitive multiplayer games can be quite a timesink and you don't necesarily take that much away from them when you are done. Reminds me to complete more of my single-player backlog!
The Outer Worlds
I wasn't initially going to try this but after consuming all the reviews promising a Fallout: New Vegas-type experience I had to try it. For me it's more similar to Mass Effect than anything else. I'm around halfway through the game and enjoying the experience. The game feels extremely polished and I haven't experienced a single bug. Presentation, graphics and sound are fantastic. Combat feels janky especially the enemy AI and animations and the combat is really easy and boring so far, even though I didn't spec into a heavy combat character. I hate how in these games the NPCs are all posed naturally but as soon as combat starts they're all running around in really unnatural and game-y ways and there's no strategy, intelligence or naturality to the characters.
Another problem I think is the pacing which is way too fast for some things. For example there's a character who you can use dialogue checks to convince them to give up their leadership position in lieu of killing them. It just goes from 'I'm going to kill you' to 'I better give up everything without a fight' within a single dialogue check which is way too sudden. Similarly there is a romance between a companion and an NPC which just feels like it happens way too fast, it feels jarring.
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TheSteelStallion 3
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Posted November 18, 2019
Gaming just hasn't been interesting me lately. I resorted to playing Dead Rising after all these years of being frustrated with the timer when I was younger. I played it somewhat casually while listening to music and finally getting all the survivors with the best ending. I succeeded and am now back at square one.
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Posted November 18, 2019
Been playing NieR RepliCant and finished the 1st part. Up until now, it's been a solid experience. I'm still curious where the story ends up in but it has an amazing soundtrack, stereotypical Japanese characters and the game-play is not fantastic but serves its purpose well.
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Posted November 18, 2019
8 hours ago, TheSteelStallion said:
Gaming just hasn't been interesting me lately. I resorted to playing Dead Rising after all these years of being frustrated with the timer when I was younger. I played it somewhat casually while listening to music and finally getting all the survivors with the best ending. I succeeded and am now back at square one.
This happens to a lot of gamers, it sounds like you need to take a break from gaming for a bit and explore other hobbies. You may find that you enjoy games more when you have some time apart.
6 hours ago, Unrul3r said:
Been playing NieR RepliCant and finished the 1st part. Up until now, it's been a solid experience. I'm still curious where the story ends up in but it has an amazing soundtrack, stereotypical Japanese characters and the game-play is not fantastic but serves its purpose well.
Do you intend to play the slightly broken PC sequel or play it on console?
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Posted November 18, 2019
1 hour ago, Andytizer said:
Do you intend to play the slightly broken PC sequel on play it on console?
Played it this year on PC actually! Enjoyed it so much that it prompted me to try out RepliCant on RPCS3.
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Posted January 17
Felt like going back to the more grounded GTA IV, as badly optimised as it may be. Still having a blast.
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What are your PC gaming predictions for the next decade?
By Andytizer
I would like to invite community to predict what major PC gaming changes will happen over the next decade, please comment below if you have any thoughts. My own predictions are rather dour, I can see the 'end of PC gaming' as we currently know it will happen at some point in the future, but will it happen in this decade or in 50 years from now? Current year 2020: Microsoft releases Xbox Series X and xCloud. Sony releases PlayStation 5. Future: Microsoft Xbox sales higher than Sony PlayStation spelling the beginning of the end of Sony’s gaming division. Xbox Game Pass and xCloud merge to become thede facto game subscription system on their unified console/PC platform. Steam release Steam Cloud, a desktop streaming service where you can play all your Steam games streamed directly from their content delivery network ala Stadia. It becomes a popular option for PC gamers that competes with Xbox Game Pass. Stadia shuts down in 2023 due to lack of consumer interest which is overshadowed by Xbox Game Pass and Steam Cloud. Microsoft release Xbox Series X Desktop, a new hybrid console and light PC that runs Windows X, a stripped down Windows OS that can only play Xbox games and run Microsoft Store apps and OneDrive/Office 365 and is designed to be used with keyboard, mouse and monitor. The next Xbox ‘console’ will run Word and Excel and kids will get them to do homework on. At the end of the decade, light PCs emerge as the dominant new product range in general - with locked down operating systems and heavy cloud integration but with good enough gaming capabilities. Think Chromebooks but with passable GPUs and frictionless access to game streaming services and cloud services. PC hardware begins to become relegated to enterprise and hobbyist pursuits. DRM like Denuvo becomes less relevant as games and consumers move to convenient subscriptions or streaming services. Game ownership question becomes far less important to gamers (see what happened to ‘music ownership’ once Spotify, Amazon Music, YouTube became popular). PC games become far more consumable, one-time use products and general users stop caring as much about game preservation. Ultrawide resolutions rise from <2% to 25% adoption rate due to popular use in enterprise, replacing the 16:9 aspect ratio. 4K adoption languishes as computing performance begins to plateau and convenience of streaming takes over where 4K streams are heavily compressed. Oculus releases another all-in-one successor Oculus Quest 2 and discontinue all other product lines ie Oculus Rift. Overall sales of VR games still do not dominate. VR games will only peak at up to 20% of the market, and it will be focused on the all-in-one product line and not PC VR. Despite the short term success of Half-Life: Alyx, Valve Index sales slump and Valve step out of production of VR hardware due to low sales ala Steam Controller but still push SteamVR as the platform neutral choice. Third parties still compete on low cost VR headsets but Oculus Quest 2's Android platform will be the de facto place for VR gaming.
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Nicereddy
August 17, 2013
Team Fortress 2 - Per usual, this is my game of choice. I'm nearing 800 hours in the game and still don't have an unusual hat. I main soldier and have been experimenting with different loadouts since
Andytizer
August 17, 2013
That's awesome Nicereddy. I also wanted to say I've also been getting into Dota 2 as well, particularly with playing co-op bot matches and watching bits and pieces from The International 3, which has
CheighLee
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Spec Ops: The Line is collecting "dust" for some time in my library and I finally sat down to play it. Good game for now :D
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2,608,060 |
Can't get my Sprint Bill, which is linked to my Q, to stay associated with a bill reminder in Q, and also will not update to current bill cycle...showing billing from two months previously. I have unlinked it from my reminder, but I can find no way to delete the linked bill from my B & I page....just sits at the top of the screen and says "Your bill has been successfully added, but your next amount due is not yet available from you biller." I have waited 2 cycles, and this has not cleared. I would like to know how to delete the linked bill completely, and then start all over and link my reminder with a refreshed link to Sprint.
Can anybody help me with this, please?
Thanks,
Richard Roberts
0
Best Answer
Bob_L SuperUser ✭✭✭✭✭
August 2019 edited August 2019 Accepted Answer
Have you tried clicking the gear icon at the top right of the online biller page? Chose manage online billers to get to the page with remove options.
Quicken Premier Subscription, Windows 10 Home
5
Answers
Bob_L SuperUser ✭✭✭✭✭
August 2019 edited August 2019 Accepted Answer
Have you tried clicking the gear icon at the top right of the online biller page? Chose manage online billers to get to the page with remove options.
Quicken Premier Subscription, Windows 10 Home
5
Richard Roberts Member ✭✭✭
August 2019
Bob_L,
Thank you. I was able to use that method to remove the Sprint bill. I also lost the linked bill reminder. I have reconnected to Sprint, and associated with my Sprint bill reminder. Currently, shows the same comment: "Your bill has been successfully added, but your next amount due is not yet available from you biller.", but hope it will update, and go back to normal with this next billing cycle.
Thanks for your help.
Richard
0
Bob_L SuperUser ✭✭✭✭✭
August 2019 edited August 2019
My experience has also been that you are better off creating an online bill, and then letting it also create a new reminder rather than linking to an existing one. Then delete the existing one.
Quicken Premier Subscription, Windows 10 Home
0
Richard Roberts Member ✭✭✭
August 2019
Thanks for your advice. I will do that going forward.
0
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The Dia de Los Muertos altar at the Utah State Capitol was decorated with candles, fruit, sugar skulls and bright orange and purple flowers and yarn pom-poms, as vivid as the fall leaves outside. The memorial also featured poignant photographs of loved ones to honor the memories of the more than 3,200 Utahns who died in the past year due …
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UTAH.GOV HOME • UTAH.GOV TERMS OF USE • UTAH.GOV PRIVACY POLICY • UTAH.GOV ACCESSIBILITY POLICY • TRANSLATE UTAH.GOV
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Tag Archives: Johnathan Vaugh Strebly
Play / Pause : Digital Design Talks, with Johnathon Strebly, Kim Oxlund, Nicholas Ferreira & Dominik Grejc
Sholeh Atash
Digital Design Program Manager
July 17, 2012
It’s that time of the term again: Digital Design Talks is here and the theme this time is Play / Pause — with a focus on Motion Design.
Our speakers include the VP Education from the Society of Graphic Designers of Canada (GDC BC), Johnathon Vaughn Strebly as well as Digital Design alumnus, Kim Oxlund and current Digital Design students, Nicholas Maykot Ferreira and Dominik Grejc
Here are few more details about their respective backgrounds:
Johnathon Vaughn Strebly brings a diverse range of experience and knowledge from Photography, Design and Education. As head of Electronic Media Design, Digital Media, and Communication Arts at Langara College, he is deeply involved in the rich visual media available today. As principal and creative director of his own studio, he extends his professional business experiences and industry awareness to the academic environment. Having served two terms as President of CAPIC Vancouver and VP of Business Practices, CAPIC National, he is proud to continue his association with professional organizations as VP Education for the GDC/BC Executive Board.
Kim Oxlund grew up in Copenhagen, Denmark. He started playing piano at 10, guitar at 13, and by 15 he was sneaking into the computer room at school to sit and draw circles and squares in Paintbrush. His talent made him one of the most interesting students to watch during his time of study at VFS. His design approach is simple: it’s all about emotion. He wants to make people feel something, whether it’s a website, a motion graphic piece or a piece of music. This is his design philosophy: “If my work can make you stop and forget about your daily routine, even if it’s just for a moment, my mission is accomplished.”
Dominik Grejc is from Krakow, Poland. His passion for film and storytelling first led him to become a Video Editor. But he also started making short movies and doing other kinds of freelance work. Then he decided to take an art course to help develop his creative skills further. After a few years, his arts eduction evolved into a fascination with Motion Graphics, which brought him to VFS, where he is currently a senior student in the Digital Design Program. In his free time, Dominik enjoys swimming, traveling and learning about new cultures.
Everything started when Nicholas Maykot Ferreira got his first camera and began to record videos of himself and his friends skateboarding. Making those short clips got him so excited about filmmaking and video editing that he decided to study Cinema at the University of Senac in São Paulo. While studying and actively looking for work in different areas of the Film Industry, he one day came across Boca (Marcos Ceravolo), a Motion Designer and VFS alumnus. After gaining more experience in the industry, he finally got a chance to work with Boca. At that time, Boca was the Art Director at New Content, a production company and publisher of customized content. Nicholas quotes: “Boca’s work inspired me so much and made me motivated to not stop when I am stuck and work on my creativity even more. I enjoyed and learned so much working with him. I believe that Motion Design is a powerful environment to convey a message, it gives us a chance to make our lives more interesting. This was one of the main reasons that made me to decided to come to VFS, to keep extending my knowledge in Motion Design, especially the conceptual idea behind each project.”
Come listen to these interesting and engaging guest speakers at our Digital Design Talks night on Wednesday, July 18, 2012 at 4:30pm in the Main Theatre at 420 Homer Street. We’re looking forward to seeing lots of designers there!
Posted in Motion Design Tagged digital design talks, Dominik Grejc, GDC BC, Johnathan Vaugh Strebly, Kim Oxlund, Nicholas Maykot Ferreira, Paintbrush, photography
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] | true |
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ivo Administrator last edited by ivo
Hi, I am Ivo, founder of Windy.com.
The times we are living in are not normal. We, pilots, kiters, sailors, and all the outdoor people value freedom the most. Yet we are being told to stay home.
Most of us, including me, have no fear of death, and some of us, also including me, challenged life-threatening situations a few times. Yes, death is an integral part of life, and we all have to die someday. Yet we are witnessing the panic and hysteria that is not the equal danger we are facing.
At Windy, we are working from home, using teleconferences to see each other, so everything is running as usual, and we have no service outages at all. But making innovations in this environment is a little bit difficult. And four weeks of lockdown, took its toll on our spirits.
As you know, in 2019, we have collected almost 500.000,- EUR/USD in voluntary donations from 55.000 donors, and we are grateful for that. Some of the donors were so generous that they send us a few thousand dollars. Thank you very much for this help.
But in order not to loose cash as hell, we need at least to double that amount. Windy.com is becoming more and more popular and offers more and more services. We have to pay an enormous amount for infrastructure to serve millions of users we have, and the amount doubles every year.
Windy Premium
Unfortunately, the coronavirus crisis stopped the economy. It broke our plans to find an income in the B2B segment, so we have decided to launch a Windy Premium subscription (details). It will offer something more for just the price of one lunch.
Bear in mind that Windy.com remains free of charge, fast as hell, and without any advertisement or tracking cookies. And everything, what was for free in 2019, remains free in 2020. Our subscription covers only new functionality that was not present in Windy.com in the past. And of course, our generous donors will get one year of subscription for free (details here).
So far, Windy Premium offers a 1h forecast. We also plan to add a 6h update time for the ECMWF model.
Windy Premium is available on desktop and in our mobile apps version 24.0.0, that are being updated right now.
We need your help
But running the company from our bedrooms and developing this functionality is very difficult. These times are not typical, so please be patient and tolerant of the mistakes we make. We have difficulty in pairing some of the payments with your user account. Some of the problems are caused by not so reliable third party services that collect these payments for us. We are working hard to fix these issues.
Dear Windy.com friend.
The times we are living in are not normal. We are staying at home, and the only risk we are taking now is overeating and gaining more weight. But all the pandemics have ended someday.
I am sure that we will see each other personally sooner that we think. We will see each other on kiting beach, airport, marina or in the mountains and we will have many perfect sessions, flights or sails together.
Stay safe, keep social distancing, and do not catch the coronavirus.
Ivo
K E 16 Replies Last reply Reply Quote 1413
MeteoAnim @ivo last edited by
@ivo As a StormChaser I really appreciate that, windy has become more than just daily forecasting and it's really helpful, I also donated already. (I just had to). Stay safe, and always look up to the clouds!!
1 Reply Last reply Reply Quote 9
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vkrejcir | Premium last edited by
@ivo I upgraded to Premium, not only for new functionality, but for support one of the best app i use. Stay safe and healthy!
1 Reply Last reply Reply Quote 7
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Liam.pankhurst | Premium last edited by
@ivo i do local forecasts for my area for some small businesses, friends and family. Windy is fantastic. It was already very accurate but now windy premium is even better. So good!
1 Reply Last reply Reply Quote 2
Korina Administrator @vkrejcir last edited by
@vkrejcir @MeteoAnim Thank you! With your help, we can make a difference . Take care and stay safe! ~ Windy
Korina
1 Reply Last reply Reply Quote 1
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saileakim | Premium last edited by
Thanks for a for a great app - just started using it for my snorkling/spearfishing and it's been great at getting me out on the waves in these times. Thanks again, I'll definitely sign up for a premium membership!
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Blito | Premium last edited by Blito
Hola. Muchas gracias a todos por esta excelente aplicación. Yo me he suscrito a Premium hace unos días, pero no se me ha cobrado la cantidad que tengo que pagar, ni puedo ver el pronóstico de 1 hora. Me imagino que es por los errores de terceros. Estoy a la espera, pero muchísimas gracias por esta excelente web.
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bcouto last edited by
You guys are amazing! incredible platform and friendly interaction! this is a humble simple appreciation msg! thank you so much for changing my days for the good!
1 Reply Last reply Reply Quote 4
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jbruzz | Premium last edited by
Thank you bro the best article I read since this thing started, especially the second paragraph, that’s what we need. Not all the other nonsense.👍🏻
1 Reply Last reply Reply Quote 1
mmmmmichael Weather Journalists last edited by
@ivo That is a great thing you did! Thank you for it!
1
1 Reply Last reply Reply Quote 2
T
Toone | Premium last edited by
As a windsurfer and weather addict I use the app daily. I like the personal interaction between the users and the developers. I will take premium because the whole thing seems the right thing to do. Thanks a lot for the wonderful windsurf sessions Windy gave me.
1 Reply Last reply Reply Quote 4
A
Ahmed A.AS last edited by
GREAT app and GREAT management nothing strange.
A.A.A
1 Reply Last reply Reply Quote 1
K
Kopansky @ivo | Premium last edited by
@ivo Dobry den. Kdyby vše mnelo být fér tak každému kdo dobrovolně přispěl měla by být udělena roční licencemi zdarma. Vždyť diki těm lidem jste zde.
2 Replies Last reply Reply Quote 0
J
jdebe | Premium last edited by
We will continue to support you as you have been supporting us for a long time. Let us know, we will give!! 👏🏼
1 Reply Last reply Reply Quote 2
ivo Administrator @Kopansky last edited by
@Kopansky Kvuli zachovani anonymity byly prispevky ANONYMNI, tj my je musime parovat rucne. To je dost pracne a proto je tam limit 50 EUR. A musime doufat ze ty zadosti budou prichazet pomalu, abychom to zvladli.
K 1 Reply Last reply Reply Quote 0
F
foxtrotuniform | Premium last edited by
Appreciate it.
How do we clear out the announcement once it has been read? I keep seeing it every time I launch the app.
1 Reply Last reply Reply Quote 1
TonyTeaBags @ivo | Premium last edited by
@ivo I am a tired airline pilot and an avid amateur astronomer. I just subscribed to the premium service, and am proud to be a subscriber. Thank you for a great service.
1 Reply Last reply Reply Quote 4
K
Kopansky @ivo | Premium last edited by
@ivo Dobrý den,jak mužů pořádat o roční licenci zdarma z nižším příspěvkem než 50€? Děkuji.
1 Reply Last reply Reply Quote 0
ivo Administrator @Kopansky last edited by ivo
@Kopansky Vas prispevek se vztahoval k provozu v roce 2019. Vybrali jsme zhruba polovinu nasich nakladu. V roce 2020 mame naklady vyssi. Pokud mate Windy radi predpadte si Windy Premium.
1 Reply Last reply Reply Quote 0
ivo Administrator @Kopansky last edited by
@Kopansky Neni pravda, ze jsme tady diky prispevovatelum. Bohuzel v roce 2019 jsme vybrali pouze POLOVINU nasich nakladu. Je to sice velika pomoc, ale Windy je zde kvuli investorovi, ktery jeho provoz dlouhodobe dotuje.
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TRAVERSE CITY, Mich. – A major health care provider in northern Michigan said it is emphasizing COVID-19 care and cutting other services after a spike in the region.
Munson Healthcare, based in Traverse City, said it has moved its pandemic response to a “red” stage in the northwestern Lower Peninsula.
“It’s the first time in Munson Healthcare’s history that we’ve made that decision,” medical director Christine Nefcy said Tuesday. “Of course, this is also the first pandemic that we’ve all faced in 100 years.”
More data: Tracking Trends in Michigan Hospitalization Data
Munson had a test positivity of 22 percent on Sunday. About 100 people with COVID-19 were in Munson’s hospitals, including 56 in Traverse City, the Record-Eagle reported.
Advertisement
At least 24 people have died since Oct. 26, Nefcy said.
The Leland School District, 25 miles north of Traverse City, said schools would be closed Friday. It encouraged staff and students to get vaccinated.
Chief Inspector Stephanie Long said the sudden decision to close before the weekend was necessary “given the recent surge in positive cases impacting our school and the local community.”
Copyright 2021 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed without permission.
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Let’s say you go get a coffee at the place by the EatWell supermarket and the Walgreens on Broadway. If you chance on a seat by the wall, don’t be alarmed by the stylized playroom next door or the 20-odd cats watching you get your caffeine fix.
No, those aren’t the patrons; those are the feline residents of Papa’s Cat Café, currently the only cat café in Mid-Missouri. The cats are provided by Boone County Animal Care, the coffee is provided by Fretboard, and the human interaction is from local couple Ryan Kennedy and MacKenzie Everett-Kennedy.
“We wanted to put together something different,” Ryan says of their decision to open the café. “A place where cats of all different types can have a chance to get adopted, including disabled cats. Those have actually been some of our fondest success stories.”
The concept is simple (and cute). Anyone is welcome to purchase coffee and baked goods from the café, but a reservation in the adjacent playroom gets you right in the middle of a hoard of cuddly, lovable, and adoptable cats.
With a grand opening on July 31, 2018, Ryan and MacKenzie had simple goals for their first year in business. They wanted to adopt out 100 cats by their one-year anniversary, which they managed with a week to spare. Things have changed since then, of course, and drastically. Papa’s Cat Café closed its doors for two months during the start of the COVID-19 pandemic, having to temporarily lay off its employees.
However, the extended time at home brought an unforeseen turn of events. The entire community spent more time inside, which meant the demand for a furry friend to keep them company in lockdown soared, and donations began to creep in regularly. According to Ryan, the café has adopted out around 150 cats since the beginning of the pandemic alone, bringing their total to 350 total adoptions since they’ve opened.
Ryan and MacKenzie usually try to keep at least 20 to 25 cats at any given time within the playroom. Papa’s Cat Café offers plenty of chances to get up close and personal with their cats, and their events packages allow renting out any combination of the café, cat playroom, and adjacent party room. According to Ryan, the Side Room Party offering is perfect for children’s parties, since a reservation in the cat’s playroom comes with a 30-minute slot in a private party room.
Another popular option includes reserving a party during the Children’s Cat Academy event that Papa’s hosts on the third Saturday of every month from 10 a.m. to 11:30 a.m. It includes a lesson on cats, story time, and a craft, and then the party can be moved to the private room after the event is over.
Some would rather volunteer than just visit, and that’s completely fine with the café. There is a special youth internship program, an eight-week, hands-on program for students ages 10 to 15 that teaches them in-depth animal training skills. The internship is unpaid, but it allows participants with an enthusiasm for animals and the desire to volunteer to become certified BCAC volunteers.
All in all, Ryan and MacKenzie are grateful that their café and all of its programs have gotten so much support from the community.
“Obviously, with the pandemic, things have been scaled down,” Ryan says. “But we’re still here, which I think speaks to how much the town likes us. The biggest surprise is the people that support us that have never been here before.”
All but two of the cats featured at Papa’s Cat Café are available for adoption, and potential cat owners can fill out an adoption interest form on the BCAC website.
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Shaped from the bones of the extinct Feroxodon pufferfish, Feroxic weaponry is highly poisonous owing to the deep saturation of potent toxins within the marrow.
With superior reach the pike is a common weapon on the battlefields of Hyboria. The most effective use of the pike is from behind the lines of battle, thrusting into the gaps in a shield wall. The thick haft and weighted end mean that the pike must be wielded in two hands.
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Limited Company registered in England and Wales, number Registered Charity in England and Wales, number Registered Charity in Scotland, number SC Brook Belfast now known as Common Youth.
By BBrook you agree to Brook house Belfast use of cookies. Brooks is a boutique about 12 kilometres 8 miles from Dublin Airport c.
Brook Belfast (now known as Common Youth)
Rental range The Rental range indicates the likely lower and upper rental value achievable for this home based on varying market conditions. Seymour Hill Primary Fhm Craigavon model Primary school 0.
Primary school. Brooks Hotel Dublin Site by Netaffinity. Belfsst estimate Close dialog. Christ the Redeemer Primary School Primary school 0.
Look inside: Cottage charm of Belfast home with bags of character
Save Home Saved Arrange a Viewing. The housd river bank provides colour all year round and the New Cheltenham beautiful women vegetable patch will keep Brook house Belfast stocked up with herbs, fruit and vegetables in the summer.
Semi-Detached Bungalow. Please try. No expense Brook house Belfast been spared in creating the charming family Bwlfast which has been finished with part wood panelled walls and features a standalone ball and claw roll top bath. Email Address. Converted from two cottages and part of a quaint cluster of period homes that formerly housed the servants of the Glenmachan Estate, Brook Cottage is a truly unique home that will charm anyone who steps inside.
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This room also has a tongue and groove ceiling. Detached Lava Corby hot pots Bedrooms 2 Receptions Brochure. ❶The property sits in extensive gardens which have been magically transformed by the current owners and there is a detached garage.
Filter New Homes. Delete My Account. Warning your profile will be permanently deleted. The Value range indicates the likely lower Ladyboy new Livingston upper values of this home based on available data and varying market Brook house Belfast.
Save Password. Thousands join Derry's Halloween celebrations.
Part tongue and groove panelled walls. Other Options Exclude Sale Agreed. No expense has been spared in creating the charming family bathroom which has been finished with part wood panelled walls and features a standalone ball and claw roll Brook house Belfast bath. Brrook Ireland chef Danni Barry in mourning as Circathe cottage is part of a number of neighbouring properties that were formerly the old Briok house, the coach house, labourer's cottages on the estate and this is what gives this area a village type 'feel'.
Brook Cottage, 87-89 Old Holywood Road, Belfast, County Antrim, BT4 2HL Belfast
There are two bedrooms, a walk in wardrobe, large study and bathroom on the first floor while downstairs there are two reception rooms, a kitchen open plan to another living area, a utility and a WC. New Homes The finest new homes from leading house builders across Northern Ireland.
Find the perfect hire Upload Job.|Forgot password? Previously two semi detached cottages which were reputedly occupied by the butler and Brook house Belfast chauffeur on the Glenmachan Estate, Brook Cottage is both a charming and unique property situated on the Book site on the Old Massage jobs Gloucester county Road.
Circa Brook house Belfast, the cottage is part of a number of neighbouring properties that were formerly the old farm house, the coach house, labourer's cottages on the estate and this Borok what gives this Brook house Belfast a village type 'feel'.
The cottage was sympathetically converted into a detached house in the 's Baby classes Reigate still retains many original features as well as its double digit address! The cottage enjoys three bedrooms Belfxst bedroom mezzaninewalk in wardrobe, and bathroom to the first floor.
The ground floor offers two reception rooms, kitchen open plan to a further living area and a utility and WC. Furthermore the Chaba Oxford massage Oxford Brook house Belfast from extensive and mature gardens and a detached garage.
The owner's Brook house Belfast was instrumental in helping create brick pathways, walls and hoyse flowerbeds but ultimately the garden has been a labour of love and has evolved from a passion for cottage type flowers and shrubs which have been collected from Mountstewart to Cornwall!
The terraced river bank provides colour all year round and the small vegetable patch provides a selection of herbs, fruit and vegetables in summer. Convenient location just a Massage pakenham Rotherham to Brook house Belfast Park and with the restaurant and cafe culture of both Ballyhackamore Village and the Belmont Road within 5 minutes drive.]This 2 Bed Semi-Detached Cottage For Sale Is Located At 'Brook Cottage', 12 Massey Avenue, Belfast.
Find Out More Inside. Converted from two cottages and part of a quaint cluster of period homes that formerly housed the servants of the Glenmachan Estate, Brook. Brooke House is an Ely Centre project, funded by the Chancellor using LIBOR Funds to improve physical and mental health and family wellbeing of its clients.
This new service seeks to support retired Royal Ulster Constabulary GC Officers, Reserve Officers, and their families.
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In giving your service to clients in your practice you may find your work is incredibly meaningful and rewarding as well as exhausting and sometimes draining. Wether you are an osteopath, chiropractor, kinesiologist, psychologist, counsellor, naturopath or other health provider, balance and self care are a necessary aspect of maintaining a healthy service to your clients. Besides the actual service you offer, if you are running your own practice there are also business and administration duties that may weigh you down! Stress and burnout are all too familiar in the health industry, so how we manage our well-being is vital to business success for any health practitioner.
Whatever your business circumstances, mindfulness is a fantastic practice to support balance and ease in a busy therapeutic practice. If you are like most health practitioners then you have probably at least heard of mindfulness. You may have even begun a regular mindfulness practice at home. Today’s focus however, is about prioritising the integration of mindfulness into your daily business routines in your health service.
Steps to Integrating Mindfulness into Your Health Practice
The first step to integrating mindfulness into your health practice is to recognise its benefit in the context of your health practice. Mindfulness can help refresh your mind and de-stress your body as you work with your clients or carry out administration work. While it is easy to become consumed in the business activities and duties, practicing mindfulness will enhance your service as well as lift your energy levels at work. You can become so entrenched in the ‘doing’ that the ‘being’ can be forgotten. So the practice of mindfulness is as important at work as it is outside of work.
The second point is to realise that mindfulness can be integrated into a range of tasks that you are already doing. You do not need to add another ‘thing’ to your already busy list. You can simply modify the way you are doing it. Rather than rush between booking a client, organising the bookkeeping and welcoming the next client into session… slow down and breathe. Mindfulness can be as simple as taking ten seconds to feel the air enter into your lungs, feel your rib cage expand and contract and feel the air be released. Or it could be taking thirty seconds to feel the texture of the paper underneath your finger tips, or to really take notice of the image on your business card. It does not matter what you are doing as much as creating the ‘space’ within your body and mind to feel and witness what is. The simple act of ‘witnessing’ in mindfulness has the powerful influence of calming a busy mind.
The third step is to create reminders to practice mindfulness while at work. At first it is easy to forget to practice mindfulness throughout the day and especially if you are working in a busy health service. So a simple strategy is to put prompts around you at work to remind you. For example, you may create a phone message system or blue-tack a card above your computer screen. Eventually you will not need these prompts but at first, they can really help to maintain your mindfulness practice.
So these are three simple steps to integrating mindfulness into your health service. In a nut shell:
Recognize the benefits of mindfulness at work
Integrate mindfulness into current activities at work
Set up reminders to practice mindfulness in your work place
These three steps may seem incredibly simple but they are very powerful as you regularly bring mindfulness into your everyday way of being at work.
Take The First Step Today To A Mindful Health Practitioner Business…
If you want a beginners guide to developing your ability to practice mindfulness, feel free to subscribe to my 10 days of mindfulness tips and audio mediations on this website.
Have your say below and contribute to future mindfulness products and services produced by Conscious Solutions.
Your Feedback Is Valuable!
Posted in Business, Mindfulness, Stress
6 Responses to “Mindfulness for Health Practitioners”
Suzanne Robertson:
December 11, 2011 at 7:53 am
Hi Vanessa
I often practice mindfulness but I do it more in brief bursts of time when I recognize I need it. It’s good that you highlighted that’s an ok way to do mindfulness. If I was to try and ‘schedule in’ mindfulness, I don’t think it would help me as it would be another think on my list to do. Mindful breathing works best for me and mindful walking or eating is great too! The great thing about mindfulness is you can do it anywhere at any time!
Regards
Suzanne
Reply
Vanessa Bushell:
December 11, 2011 at 10:03 am
Hi Suzanne, thank you for your comment. I agree… adding another thing to a busy agenda is the last thing we want. Integrating a practice into what we already do is definitely the way forward and mindfulness gives us this opportunity.
Have a great time embracing mindfulness
Vanessa
Reply
Annie Infinite:
December 11, 2011 at 10:56 am
This is great advice Vanessa and it can help so much to clear the mind and allow expansiveness that can lead to renewed energy and better focus – I love to run outside barefoot on my grass and feel the ‘heartbeat’ of the earth beneath my feet and allow any tension to release down through my feet. It makes a huge difference so nice to know you are helping health workers with this kind of advice, they all put so much of themselves into their work
Reply
Vanessa Bushell:
December 11, 2011 at 8:05 pm
Hi Annie, thank you for your comment.
I believe people can feel the freshness within someone who connects with nature regularly and really allows themselves to receive Earth’s energies. It’s beautiful that our social medial guru has such a wonderful grounded connection with mother Earth. I think when you work with a lot of ‘head stuff’ and the on-line cyber world it would be so important to run barefoot on the grass outside!
Lovely response, Annie
Vanessa
Reply
Kama:
December 14, 2011 at 7:43 am
Great post Vanessa. My whole business is built around mindfulness. I don’t know how I survived before mindfulness came in to my life, my mind must have been a complete mess. I practice mindfulness all day and I certainly know when I have let it slip because suddenly everything seems difficult. Such a powerful tool. I love our mindfulness chats x
Reply
Vanessa Bushell:
December 14, 2011 at 9:52 pm
Hi Kama, thank you for your lovely comment! I enjoy our conversations on mindfulness too. Practicing mindfulness has been incredibly helpful to me as a psychologist as well as my clients and I know it is the same for you. It’s fantastic to embrace mindfulness in our service with others!
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Conscious relationship is one of my passions because I love and honour love itself. While intimate relationship is only one type of relationship where love can be cultivated, it fascinates me because I believe there is so much potential in our intimate relationships, rather than misunderstanding, separation and heartbreak. My hope is that increased awareness of gender and relationship will lead to us healing ourselves and our hearts.
Learnings from my Personal Story
From a very early age I had a deep longing for love. My dad was a Vietnam veteran with PTSD and my mum was psychologically and emotionally shut down. So neither one of them were connected to love within themselves, their relationship or with their children. The only time the word love was even used in their household was when we were getting the strap … “I’m doing this because I love you!” So needless to say, my first introduction to so called love was abuse. There was a deep sense of “wrongness” for me as a child. I knew this was not love! And yet there was no affection, encouragement or half way positive messages about who I was or who I might become.
Perhaps what gave me the deepest sadness in my family of origin was that they were so emotionally shut down. Like androids, these people were not connected to themselves or each other. The aimless, depressed negativity drove me crazy! Later I was to know this as unconsciousness… deeply shutdown patterns that blocked them from the life force… blocked them from love! So my earliest desires to wake up, become conscious and embrace love originated from early childhood experiences.
Over the decades as an adult, I have accessed counselling, spiritual healing, kinesiology, body talk, holographic kinetics, and anything else I could get my hands on! Today I wake up feeling happy! There are times when challenging things happen in life, but my perceptions and beliefs are largely positive. Sometimes I witness my thoughts and their attached emotions, spiral into the old family patterns, but I see them for what they are… simply unconscious patterns that detract me from loving life.
So what does all this have to do with Conscious Relationship?
I learnt in a very real way the importance of healing myself before I can create a conscious, healing and loving intimate relationship. I cannot say I am fully healed… I know that is not true! But I am on my way!!! Becoming more aware of my unconscious patterns that are triggered in intimate relationship, I can take responsibility for my 50% of a relationship and learn from it. My dream for intimate relationships is where both people become consciously aware of their own psychological patterns and learn to create love rather than pain.
This involves understanding the differences between genders and honouring those differences. If women are generally emotionally based creatures while men are often physically based, then what can we learn from each other in regards to respecting each other? It is not so much about becoming like the other, but rather to understand what the other is about in how they approach life. The idea of opening to happiness through connection, honouring differences and complementing our partners in intimate relationships, is the cornerstone of conscious relationship. By observing and learning from our own reactions, we can learn to give better to our relationships. By learning how to communicate even when we speak different languages, we can create shared meaning and purpose in our relationships. By understanding and appreciating what the feminine and masculine give and HOW they give, we can increase gratitude and reduce resentment. This is essential if we are going to shift our intimate relationships from the average, shut down relationships that exist in the sludge of life.
How we connect to our intimate partner is a choice! If we struggle with resentment, criticism, guilt or condemnation towards our partner, then perhaps it is time to heal. First this process is in each individual self. Secondly, the relationship itself may need healing. Becoming conscious simply opens the door to healing and finding happiness in relationship. Conscious awareness is an on-going process that sometimes requires guidance through therapy, self-help avenues and other healing modalities. But it is worth it!!! For it is the difference between mastering ourselves and our relationships, and perpetuating the living hell of the unconscious relationships of the past. Without compassionate awareness towards ourselves, there cannot be love for another.
What are the important ingredients of Conscious Relationships in your experience?
Posted in Communication, Conscious Relationship, Emotions, Family Patterns, Intimate Relationships, Sadness, Unconscious, Valuing Self & Others
9 Responses to “Reflections on Conscious Relationship – One”
Andrew:
June 30, 2012 at 2:14 am
Once again, great writing. Thanks Vanessa. You really do have the best blog.
It’s so interesting when we look into our past. Everyone has something tucked away there that their parents did or didn’t do. I love the way you talk about this because you realise, it wasn’t their fault; under the circumstances, they did the very best job they could, so we can’t blame them or judge them. All we can do is work on ourselves.
The important thing is, and you put this so well, is where our conscious relationships come from and for most of us they begin in the home as a child.
I often see the example of this higher consciousness in some older women. They have a knowingness glow about them where they are totally in form with their consciousness and they are a pleasure to be with.
Only thing I’d add is that I think men are not so much physically based as practically based and I think this is because of the hormones that they have to live with each day. Subtle difference in points-of view? maybe?
Have a great day, Andrew
Reply
Vanessa Bushell:
June 30, 2012 at 11:08 pm
Hi Andrew, thank you for your great comment!!
I know what you mean by some older women who have that “glow”, they are beautiful! When a woman learns to value and honour herself and accept herself fully she is amazing… deeply wise. Some people can be confronted by her because she sees straight through things … We could say she is “the Crone” in Ancient Greek mythology.
You are also so right about not blaming those who came before us. They did do the best they could with what they knew. It is all about us each taking the self-responsibility to heal ourselves and create better in our lives and relationships.
Lots of love
Vanessa
Reply
Kama:
June 30, 2012 at 2:46 pm
I agree with so much of this post. I believe that all relationships need to be conscious relationships, this is how we grow, learn and understand the world. It is all about choices, respect and awareness of ourselves and the other. Yes it all starts with self awareness, which is such a precious gift.
Reply
Vanessa Bushell:
June 30, 2012 at 11:11 pm
Hi Kama, thank you for your comment. I agree, it is all about self awareness, and it is a precious gift!!! Watching yours and Wayne’s relationship is a great example of conscious relationship is action… I thank you both for your influence on me!
Lots of love
Vanessa
Reply
Kama:
July 1, 2012 at 9:00 am
Thank you that is so lovely xx
Reply
Veronica (Roni):
June 30, 2012 at 11:59 pm
Hi Vanessa….agreement all round with above comments. Great writing, great intimacy, great exposure, great understanding. I find this whole subject a life-long stimulation….growing, learning, accepting, honouring, sharing. No room for shame or blame.
I try to walk the talk, in saying that, I think when we are up close & personal, we can sometimes lose our way for a moment…. slip back into reaction rather than response. For those times we need loving mirrors, trusted friends, written reminders, visible cues, whatever jolts us back to being the loving soul we strive to be.
Reply
Vanessa Bushell:
July 2, 2012 at 12:00 am
Hi Roni, fantastic comment. It is so true, it is so easy to fall back into unconscious reaction but we do not need to blame ourselves (or anyone else), we can simply bring in awareness and become conscious of what is happening for us. And yes, there are many mirrors. My process of self awareness is very pro-active including therapy, energy work, great friends and a lot of reading. These days Ill pick it up quicker if I have gone into reaction or projection!
Thank you for your insights
Lots of love
Vanessa
Reply
Deb:
July 1, 2012 at 1:08 am
Beautiful Vanessa – I see being in a conscious relationship as an absolute gift to oneself, to the other and to the whole… One teacher shared with me, the purpose of real relationship is to heal, to comfort, to nurture and to bless…
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Anxiety is a tricky condition because it feeds itself. It is like a snow ball that rolls down a hill, collecting more snow and growing bigger as it rolls down. Peace of mind and relaxation are the opposite of anxiety. When we are relaxed we can be open hearted and open minded, and things seem to flow easily and freely. A peaceful mind can make it easier to respond to events in the world, rather than emotionally react.
When we are stressed and anxious on the other hand, we are more likely to make mistakes, things seem hard and we feel overwhelmed and exhausted. That is why it is said that love is the opposite to fear! Fear, stress and anxiety close our hearts, feed our defensiveness and put us into a state of fight/flight. So to decrease anxiety, the key is to starve the anxiety of the thoughts, feelings and actions that feed it.
Mindfulness helps us in this pursuit by teaching us how to witness the thoughts, feelings and actions that feed anxiety. It helps us do “dis-identify” from the roller-coaster of anxiety and its food.
The benefits of Mindfulness to reduce anxiety are twofold. Beside the process of witnessing, it also teaches us to breathe! The fight/flight response which is associated with anxiety is a physiological reaction that involves shortness of breath, increased heart rate, sweaty palms and nausea or an upset stomach. These physiological reactions occur because anxiety is a fear response where if we were faced with an immediate real threat, we would need to survive. So all the blood rushes to our hands and feet and away from our internal organs. We can help turn this physical anxiety response around however, we would be hard pushed to change our blood flow! So we focus on our breath! This is a physiological symptom of anxiety that we do have some control over. So mindfulness can help us to slow down our breath while witnessing our thoughts, feelings and actions.
There are several different forms of anxiety. There is generalised anxiety which is not consciously attached to any one cause or trigger. There is panic disorder and phobias, which is anxiety associated with a specific trigger. There is post traumatic stress disorder which results from experiencing a traumatic event. There is obsessive-compulsive disorder, which is an anxiety disorder that results in unusual ritual behaviour to avoid the anxiety and unusual believes. There is social phobia, which is a fear of social relating, and there is agoraphobia which is a fear of leaving the house. All of these conditions can be incredibly debilitating however, therapy can help make sense of the thoughts and feelings behind anxiety, and by using regular mindfulness practice, people can overcome these conditions.
Angie tended to feel sick in the stomach with anxiety every time she had to meet with her boss at work. Richard, her boss, was older than Angie and while he was a fair man, he had a self-confident and direct demeanour. After attending a meditation morning with her friend, Angie decided to begin a mindfulness practice to increase her awareness of her thoughts, emotions and physiological reactions, to reduce her stress. Most of the time throughout the day she gauged her anxiety and stress to be relatively calm. However, when she knew she had to speak to Richard, she noticed feeling sick in the stomach, her throat tighten, her palms were sweaty and her breathe was shallow. She also felt her heart quicken, her hands were slightly shaky and her mind became foggy. Before practicing mindfulness, Angie knew she was stressed when she had to see Richard, but she did not know how anxious she really felt.
As Angie allowed herself to witness her mind and body’s reactions to seeing Richard, she realised her own unique anxiety reaction. To turn it around and feel more confident, she slowed down her breathing and wriggled her fingers. She then challenged some of the thoughts that were entering her mind. She witnessed thoughts that told her she was inadequate and left her feeling like a small child. She knew she was a good worker and that she had nothing to hide and yet she felt like a “naughty girl”. As she watched her thoughts and emotions, she realised that she felt the same way when she was younger and got in trouble for things she did not do. So bit by bit she comforted her inner child within and learnt how to stay in her adult self while speaking to Richard.
The power of mindfulness is that in practicing it, we can increase our self-awareness if we allow ourselves to simply listen! Angie’s story is a great example of how over time we can benefit from increasing self-awareness and practicing mindfulness.
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Posted in Anxiety, Communication, Mindfulness, Stress, Valuing Self & Others
One Response to “Turning Anxiety into Peace of Mind with Mindfulness”
Racquel Tyer:
February 6, 2013 at 2:19 pm
Everyone is afraid of something. This most fundamental, critical rule of human existence may be among the oldest reasons for the human need to socially interact. To a certain extent, it is arguable that all society is based on the foundation that we are playing off each other’s fears. However, while it is normal for everyone to have fears, not everyone has a phobia. The phobia, which is essentially an unreasonable fear that is firmly rooted in a person’s psychology, can sometimes be difficult to spot. In general, they don’t so much affect a person’s social and professional standing as other disorders might. Yet, there are some people that must deal with the prospect of having to face a phobia at work on a daily basis.,
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The increased incidence of chronic illness provides time for older adults to express their wishes for medical treatment at the end of their lives and their estate settlement after their deaths, a process called end-of-life (EOL) planning, but significant gaps in rates of EOL planning completion remain. Using public use data from the 2003 wave of the Wisconsin Longitudinal Study (WLS), this study explores bereaved adult children's (<italic>n</italic> = 1,199) EOL planning in the context of a deceased parent's EOL planning. This study uses Rettig's family decision-making theory foundation and the dependent variable incorporates both medical and financial EOL planning measures. Hierarchical regression results indicate that household net worth, parent's completion of a living will before death, and adult children's avoidance of death ideation explain the greatest proportion of variance in adult children's EOL planning, among variables included in the study. Practitioners can use this information to close accessibility gaps due to net worth differences, advocate for a more unified approach to EOL planning, and shift the focus of discussions of death from the death itself to a life well lived.
Keywords
Adult Children
End-of-Life Planning
Family Decision-Making Theory
Living Wills
Wills
Appears in collections
Master's Theses (Plan A and Professional Engineering Design Projects) [4008]
Description
University of Minnesota M.A. thesis. May 2014. Major: Family Social Science. Advisor: Sharon M. Danes, PhD. 1 computer file (PDF); vii, 61 pages, appendices p. 51-61.
Suggested Citation
Woosley, Angela Lynn. (2014). The relationship between parents' end-of-life planning and adult children's end-of-life planning. Retrieved from the University of Minnesota Digital Conservancy, https://hdl.handle.net/11299/165647.
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.
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Home 2018 Elections Dem Congressional Candidate Just Said About Trump And Bin Laden Will Go...
2018 Elections
News
Opinion
Politics
Trump
Dem Congressional Candidate Just Said About Trump And Bin Laden Will Go Down As Day Left Lost USA
By
Howard Roark
-
August 31, 2018
0
372
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The left really is doing everything they can to lose the midterms. With so many tight races across the country, one would think they would appeal to the middle.
But not these geniuses. They are going more and more to the left every day and their rhetoric will most probably come back to haunt them.
As reported by Fox News: A Democratic candidate for Congress in Illinois is facing heavy criticism for comparing President Donald Trump to Usama bin Laden.
Sean Casten, who’s challenging incumbent Rep. Peter Roskam (R) in the state’s 6th Congressional District, made the remark in February, but audio of it just surfaced Thursday in a report by The Washington Free Beacon.
“In many ways — and I don’t mean to sound overly, I don’t know, hyperbolic on this –Trump and Usama bin Laden have a tremendous amount in common,” Casten said during a public meet and greet with voters in Wheaton, Illinois.
“They have both figured out how to use the bully pulpit to activate marginalized young men. It’s the same tool,” he continued. “Every demagogue has done this, right? Find a group of angry people and give them something to be angry at.”
Casten made the comparison during a response to a woman’s question about gun violence at schools.
Casten campaign spokesman Greg Bales responded Thursday, saying that “Sean’s words were poorly chosen, and he regrets them.”
“Even prior to being elected, Donald Trump’s politics of disparagement and division have been straining the bonds that hold our diverse nation together,” Bales wrote in an email. “And Sean believes that Donald Trump has gone out of his way to divide Americans for his own personal gain rather than bringing us together to solve issues like gun violence.”
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The Jews are one of the most ancient people groups in the world today. Their history has been extensively recorded in the Bible, by historians outside of the Bible, and with the discovery of archeological artefacts. We have more facts about their history then any other nation and these data provide a continuous account going back about 4000 years. To make the history of the Israelites (an earlier Old Testament term for Jews) easier to follow, I will build a series of timelines showing their history based on the Bible and facts from these other sources.
Abraham: Family Tree of the Jews begins
The timeline starts with Abraham, one of the most recognized characters in ancient history. He was given a promise of nations coming from him and had encounters with God culminating in a symbolic enactment of sacrificing his son Isaac. This sacrifice is still mysterious to many today but it was a sign of future events. The timeline continues in green when the Israelites were slaves in Egypt. This period of time started when Joseph, grandson of Isaac, led the Israelites to Egypt, but they subsequently became slaves there.
Living in Egypt as slaves of Pharoah
Moses: The Jews become a Nation under God
Moses led the Israelites out of Egypt by inaugurating the equally mysterious Passover Festival, allowing their Exodus from Egypt and their arrival at the land of Israel that had been promised to Abraham hundreds of years before. Moses pronounced Blessings and Curses on the Israelites at the end of his life as he finished the writing of the Torah – when the timeline goes from green to yellow. These Blessings & Curses have followed the Jews ever since – as one can see by learning their history.
Living in the Land – but no King in Jerusalem
For several hundred years the Israelites lived in this land but they did not have a King, nor did they have a capital city of Jerusalem – it belonged to other people in this time period. However, with David about 1000 BC this changed.
Living with Davidic Kings ruling from Jerusalem
David establishes a Royal Dynasty at Jerusalem
David conquered Jerusalem and made it his capital city. It was he who received the promise of a coming ‘Christ’ and the anticipation of this title started with him. His son Solomon ruled as his successor and built the First Jewish Temple in Jerusalem. The descendants of King David continued to rule for about 400 years and this period is shown in green-blue (1000 – 600 BC). This was the glory period for the Israelites – they started to see the Blessings promised. They were a world power, had an advanced society, culture, and their Temple. But the Old Testament also recounts their descent into social and religious corruption and idol worship during this time. Many of the Old Testament books written in this period were warnings that the Curses of Moses would come upon the Jews if they did not change. But these warnings were not heeded. A series of prophecies of a coming ‘Branch’ from David started in this era.
The First Jewish Exile to Babylon
So finally around 600 BC the Curses came true. Nebuchadnezzar, a powerful King from Babylon came – and just like Moses had predicted in his Curse when he wrote:
The Lord will bring a nation against you from far away … a fierce-looking nation without respect for the old or pity for the young. … They will besiege all the cities throughout the land. (Deuteronomy 28: 49-52)
Nebuchadnezzar conquered Jerusalem, burned it, and destroyed the Temple that Solomon had built. He then took the Israelites and deported the majority across his vast Babylonian Empire. Only the poor Israelites remained behind. This fulfilled the predictions of Moses that
You will be uprooted from the land you are entering to possess. Then the Lord will scatter you among all nations, from one end of the earth to the other. (Deuteronomy 28:63-64)
Conquered and exiled to Babylon
So for 70 years, the period shown in red, the Israelites lived as exiles outside the land promised to Abraham and his descendants.
Return from Exile under the Persians
After that, the Persian Emperor Cyrus conquered Babylon and Cyrus became the most powerful person in the world. He issued a decree that permitted the Israelites to return to this land.
Living in the Land as a part of Persian Empire
However they were no longer an independent country, they were now a province within the vast Persian Empire. This continued for 200 years and is shown in pink in the timeline. During this time the Jewish Temple in Jerusalem was rebuilt (known as the 2nd Temple). Also, the ‘Branch’ theme develops further by identifying the name of the coming Branch as ‘Jesus’.
The period of the Greeks
Then Alexander the Great conquered the Persian Empire and made the Israelites a province in the Greek Empires for a further 200 years. This is shown in dark blue.
Living in the Land as part of Greek Empires
The Period of the Romans
Then the Romans defeated the Greek Empires and they became the dominant world power. The Israelites again became a province in this Empire and it is shown in light yellow. This is the time when Jesus lived and this explains why there was a Roman Governor and Roman soldiers throughout the gospels – because the Romans ruled the Jews in the Land of Israel during the life of Jesus.
Living in the Land as part of Roman Empire
The Second Jewish exile under the Romans
From the time of the Babylonians (600 BC) the Israelites (or Jews as they were called now) had never been independent as they had been under the Kings of David. They were ruled by other governments of other people. The Jews resented this and after Jesus they revolted against Roman rule. In this war the Romans came and destroyed Jerusalem, burned down the 2nd Temple and deported the Jews as slaves across the Roman Empire. This was their second exile. Since this Empire was so vast the Jews were effectively dispersed across the whole world.
Jerusalem and Temple destroyed by Romans in 70 AD. Jews sent into world-wide exile
And this is how they lived for almost 2000 years: dispersed, fragmented, living in foreign lands and never accepted in these lands. As they lived in these different nations they periodically suffered great persecutions. This was the same period where Christianity spread and the persecution of the Jews was particularly true in Christian Europe. From Spain, in Western Europe, to the pogroms in Russia the Jews lived often in a precarious state in these Christian kingdoms. These specific pronouncements of Moses back in 1500 BC became vivid and accurate descriptions of how they lived.
… Among those nations you will find no repose, no resting place for the sole of your foot. There the Lord will give you an anxious mind, eyes weary with longing, and a despairing heart. (Deuteronomy 28:65)
The Curses against the Israelites were given to make peoples ask:
All the nations will ask: “Why has the Lord done this to this land? Why this fierce, burning anger?”
And the answer was:
“ … the Lord uprooted them from their land and thrust them into another land…” (Deuteronomy 29:24-25)
The timeline below shows this 2000 year period which follows after the history of the Jews from the time of the Bible. This period is shown in a long red bar.
Historical Timeline of the Jews – featuring their two periods of exile
You can see that through their history Jews went through two periods of exile but the second period of exile was much longer than the first period of exile (which was only from 600 – 530 BC).
The 20th Century Holocaust
Then the persecutions and pogroms against the Jews reached their peak. Hitler in World War II, through Nazi Germany tried to exterminate all the Jews living in Europe. And he almost succeeded by creating a mechanised system of exterminating them in gas ovens. However he was defeated and a remnant of Jews survived.
Modern Re-birth of Israel
And then in 1948 the Jews, through the United Nations, saw the remarkable re-birth of the modern state of Israel. It is remarkable just in the fact that there were people still around who identified themselves as ‘Jews’ after all these millenia without a homeland. But this reality allowed for the final words of Moses, written down 3500 years ago, to come true. There was a “Jewish’ people around to see this final prediction fulfilled in our time.
…then the Lord your God will restore your fortunes and have compassion on you and gather you again from all the nations where he scattered you. Even if you have been banished to the most distant land under the heavens, from there the Lord your God will gather you and bring you back. (Deuteronomy 30:3-4)
It was also remarkable in that this state was founded in the teeth of opposition. Most of the surrounding nations in that region waged war against Israel in 1948 … in 1956 … in 1967 and again in 1973. Israel, a very small nation, often found itself at war with five nations at the same time. Yet not only did they survive, but their territories increased. In the war of 1967 the Jews regained Jerusalem, their historic capital city David had founded 3000 years ago. The aftermath of the creation of the state of Israel, and the fallout from these wars has created one of the most intractable conflicts and political problems of our world today. Strange how Moses’ words so long ago echo still today even though very few are even aware of what he wrote.
The Eeriness of Moses’ Farewell Speech echoing in Global Headlines today
by Ragnar
16/02/2013 17/11/2021
31 Comments
The Blessings & Curses in Deuteronomy through history and into our day This past week has seen the attention of all major news websites and… Read More »The Eeriness of Moses’ Farewell Speech echoing in Global Headlines today
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The 2014 PALS Report includes all Aleph Subsystem Reports, news relating to PALS products and services, and recent activities of the various Task Forces and Office Teams. Highlights include updates to the PALS Support Center, new lending and replacement lending slips, Islandora growth, and Leech Lake Tribal College joining the Consortium.
Group
User Groups
Form
Report
Date
2014-10-23
Years
2010-2014
Title
2014-10 Cataloging User Group Meeting Minutes and Agenda
Description
Topics discussed include printing issues with Aleph version 22, the future of the RDA Task Force, headings in OCLC records, and elections: Dianne Narum (Bemidji State University) will represent State University Libraries, Mike Rybak (Inver Hills Community College) will represent Two-Year Colleges, and Sarah Quimby (Minnesota Historical Society) will continue to represent Special and State Government Libraries.
Group
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Form
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Date
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Years
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Start Advancing Your Knowledge
Contact PALS Request Demo
PALS
Minnesota State University, Mankato
3022 Memorial Library
Mankato, MN 56001
Phone: 507.389.2000
Toll Free: 1.877.466.5465
A Program of Minnesota State
Minnesota State Colleges and Universities (Minnesota State) is a system of two- and four-year colleges and universities throughout Minnesota. Minnesota State aims to enable people in Minnesota to succeed by providing the most accessible education in the nation.
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PG&E Wildfire-Linked Bankruptcy Sparks Warnings Of Higher Utility Bills, Harm To Fire Victims | Consumer Watchdog
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PG&E Wildfire-Linked Bankruptcy Sparks Warnings Of Higher Utility Bills, Harm To Fire Victims
PG&E lists $51.69 billion in debts, while warnings erupt of higher power bills due to bankruptcy case
By George Avalos, THE MERCURY NEWS
January 29, 2019
https://www.mercurynews.com/2019/01/29/pge-files-for-bankruptcy-facing-…
PG&E filed for a Chapter 11 bankruptcy on Tuesday, staggered by billions of dollars in debts and liabilities in the wake of a series of deadly wildfires that torched Northern California in 2017 and 2018, and a key state lawmaker said the focus must now be on protecting customers, fire victims and company workers.
“It’s now time for the state Legislature and the governor to step up and be counted,” state Sen. Jerry Hill, a Democrat who represents parts of Santa Clara County and San Mateo County, said Tuesday. “There has to be some backstop, some protection mechanism in place to guarantee that ratepayers, fire victims and employees are not harmed by the final restructuring of PG&E.”
San Francisco-based PG&E listed $51.69 billion in debts and $71.39 billion in assets, according to the filing with the U.S. Bankruptcy Court in Northern California.
“We are not ‘going out of business,’ and we expect that there will be no disruption to the services you expect from us as a result of the Chapter 11 process,” PG&E stated in a post on its website to explain the bankruptcy.
However, the bankruptcy filing could lead to higher monthly power bills for PG&E customers, depending on the plan for financial reorganization that is approved by the bankruptcy judge and eventually reviewed by the state Public Utilities Commission, according to a one-time member of the powerful state regulatory agency.
“We can’t have any confidence that the PUC will protect ratepayers from higher monthly bills as a result of this bankruptcy,” Loretta Lynch, a former PUC commissioner, told this news organization.
Plus, people who are victims of the infernos that torched the North Bay Wine Country and nearby regions in 2017, as well as those who were victimized by wildfires that roared through Butte County in 2018 will likely find that their liability claims against PG&E could take a back seat to the financing package that PG&E is seeking to help it operate during the bankruptcy.
PG&E filed a motion to gain bankruptcy court approval for $5.5 billion in a funding package known as debtor-in-possession financing, PG&E stated in a prepared release. That funding is being provided by a consortium of large banks.
“The banks providing the financing have first dibs on being paid back,” Lynch said. “That means the fire victims, the company’s workers and the ordinary creditors will be placed at a disadvantage to be paid back and compensated.”
BlueMountain Capital Management, a large investor in PG&E, said it was “deeply disappointed” PG&E’s board of directors had decided to pursue a “reckless and irresponsible” bankruptcy proceeding. BlueMountain added, “Today’s filing is the latest example of how the PG&E board continues to fail the company, wildfire victims, customers, employees, creditors, shareholders and the people of California.”
State fire investigators have determined that PG&E’s equipment was the cause of 17 destructive fires in 2017. However, PG&E was found not to be the cause of the lethal Tubbs Fire, which was one of the October 2017 infernos, state fire investigators ruled earlier this month.
PG&E has disclosed that it suffered equipment failures near the point of origin of the Butte County blaze in 2018, which killed at least 86 people and essentially destroyed the town of Paradise. That inferno, known as the Camp Fire, is deemed to be the most deadly and catastrophic wildfire in California history.
“We have heard the calls for change,” PG&E interim Chief Executive Officer John Simon said. “We are determined to take action throughout this process to build the energy system our customers want and deserve.”
PG&E’s bankruptcy is the latest in a series of calamities to bedevil PG&E and its customers over the last several years.
Even before the deadly wildfires linked to PG&E’s electricity system, PG&E became a convicted felon in 2016 for crimes it committed before and after a fatal gas explosion that killed eight and destroyed a San Bruno neighborhood in 2010.
Federal investigators determined the San Bruno disaster was caused by a deadly combination of PG&E’s flawed record keeping, shoddy maintenance and the PUC’s lazy supervision of the utility.
PG&E’s shares jumped 16 percent in mid-session trades, an indication that Wall Street players believe the bankruptcy case could help PG&E ward off wildfire liabilities and assure a steady revenue stream.
Also favorable for PG&E: a decision by the PUC on Monday to approve what skeptics call a $6.1 billion bailout of the company by agreeing to the financing package to enable PG&E to keep the lights on and the heat flowing during the bankruptcy case.
“The PUC voted to put the interests of PG&E over that of ratepayers, who will bear the cost of the billions in bonds without conditions, and wildfire victims, who will be in line behind banks and lawyers to get paid in bankruptcy,” said Jamie Court, president of Consumer Watchdog. The consumer group urged that the four PUC members that former Gov. Jerry Brown appointed be ousted and replaced.
PG&E monthly bills rose by an average of $1,300 to $1,700 over a 10-year period due to PUC decisions taken in connection with the settlement of PG&E’s prior bankruptcy in 2001. Lynch and Sen. Hill want to avoid a repeat.
“The whole thing will be a major reorganization of PG&E over the next couple of years,” Hill said. “Everyone will be jockeying for positions that suit their own interests. We have to be sure that customers, workers and fire victims are not ignored.”
George Avalos is a business reporter for the Bay Area News Group.
Follow George Avalos @georgeavalos
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Dalet is responding to the increasing flexibility needs of content producers and distributors, offering customers greater choice when it comes to cloud providers, advanced remote editing and archive options with expanded support for Adobe Premiere Pro, Microsoft Azure, and Spectra BlackPearl RioBroker.
Shifts in the global workplace over the past two years have led to more geographically dispersed remote employees that still need to connect to headquarters, colleagues, partners and customers worldwide. To achieve the flexibility needed to serve the new remote workforce, content-focused organisations are prioritising system interoperability. They are turning to open solutions like Dalet Flex, Dalet Galaxy five and Dalet AmberFin that offer easy integration, to create extensible remote workflows that can be customised with best of breed solutions from developers like Adobe, Microsoft and Spectra Logic.
“Dalet has always encouraged our customers and partners to leverage our rich set of APIs to expand their content supply chains while protecting existing investments,” said Bea Alonso, Chief Market Officer at Dalet. “Our goal is to help companies grow for sustained success at their own pace, building long-term relationships and real partnerships with our customers.”
Cloud-based infrastructures and services are becoming more practical for content access, storage and asset management. However, many organisations prefer the flexibility of multi-cloud platforms for optimal levels of security, performance and reliability, as well as business continuity and system redundancy.
Bea adds, “The company will continue to explore integrations with providers of complementary solutions, especially cloud infrastructures and services such as Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and most recently, Alibaba Cloud – to strengthen its position as a true multi-cloud support vendor. Dalet Flex was one of the earliest cloud content supply chain solutions, and works seamlessly with these four different cloud infrastructure vendors.”
Dalet FlexMAM seamlessly integrates with archive solutions such as Spectra BlackPearl
The newest integrations include:
Extended Adobe Premiere Pro integrations for Dalet Flex and Dalet Galaxy five to support a wider set of editing scenarios, especially remote editing. FlexXTEND, the Dalet Flex panel integrated within Adobe Premiere Pro now includes the ability to collaborate, store and archive Adobe Premiere Pro projects for centralised management.
Dalet Galaxy xCloud on Microsoft Azure to support cloud-based radio production workflows and extend Dalet Galaxy xCloud to a wider range of customers.
Dalet Flex and Dalet Galaxy five with Spectra Logic’s BlackPearl RioBroker Archive to enhance existing workflow orchestration with expanded archive management capabilities.
Other recent Dalet integrations include Dalet Flex with Online Video Platform provider Kinow, Dalet AmberFin Cloud Transcoder Service with InSync FrameFormer and increased device support for Dalet OnePlay including Telemetrics Robotics and Avid Maestro.
Open for Integration
Today, Dalet offers more than 300 active integrations across its product range, covering every customer requirement from third-party integrations, cloud services and more. Its systems are designed for seamless third-party compatibility, allowing customers, system integrators and other manufacturers to add new capabilities through open application programming interfaces (APIs), including B2B and B2C REST APIs in Dalet Flex, and software development kits. Dalet APIs for Dalet Flex, Dalet Media Cortex, Dalet AmberFin and Dalet Galaxy five are all public and available through your Dalet representative.
Some of the hundreds of partner integrations within the Dalet Flex ecosystem
The same open integration approach is applied to Dalet’s new offerings, such as Dalet Pyramid, the next-generation Unified News Operations solution from Dalet. To learn more about Dalet’s open ecosystem of integrations and partnerships, visit www.dalet.com/partners.
Visit https://www.dalet.com
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It’s been interesting to read “classic high fantasy,” written before the Dungeons & Dragons tipping point into genre: where the antecedents are folk tales not Tolkien; where the magic is ambiguous not formularised and disposable; and most of all where the prose is formal, poetic and epic.
Don’t get me wrong, I love Tolkien, and realistically it is the benchmark, but it cast a long elf/dwarf/dragon shadow over fantasy writing. Some of those cliches are easing off, but the return-of-the-ancient-bad-guy-in-the-east remains!
Funny how at a time when reading and publishing are meant to be waning, that there seem to be more books and authors. I have no theory for why franchise style series have taken off, whether the change is driven by risk averse publishing or readers seeking familiarity. It has certainly been entertaining to see the proliferation of vampire romance novels in the wake of the Twilight series, and now the sub-genre of bondage books inspired by the Fifty Shades series! Black covers and shiny details replacing the bodice ripping hunks of last century…
Even stranger how is how much “Robert Ludlum” titled work is being published despite him being dead. Surely you read a work because you like the author’s style, not because it’s a brand on the cover, but that probably seems a little quaint as a concept!
(One for the subscribers!)
D&D | dice | fantasy | 4 comments
4 Comments
thundersbeard says:
April 27, 2013 at 11:21 pm
How far into the series are you? I read Wizard of Earthsea the same year I read Lord of the Rings, Grade 6. Probably the first fantasy books I consciously read. I remember finding the second book pretty scary, and the third book pretty bleak and kind of depressing, in that I’m just gonna sit here and ruminate about this book I just read kind of fashion.
Reply
Control Fink says:
April 28, 2013 at 9:12 pm
I’ve read the first two, and avoided binging to save the last. Writing your main character back in halfway through, awesome.
(I always avoided it because the cover art made me think it was a children’s novel! “The Left Hand of Darkness” is one of the best scifi novels I’ve read…)
Reply
thundersbeard says:
May 4, 2013 at 10:15 am
Couple of days after you replied, I was moving some bookcases around downstairs and actually came across the Earthsea Quartet, an omnibus I bought Kylie a few years ago. I’ll have to finally read the last one. I actually don’t think I’ve read any Ursula science fiction beyond a short story that was more anthropology then science fiction, but I’ll have to give “Left Hand of Darkness” a try on your recommendation.
Reply
Control Fink says:
May 5, 2013 at 2:58 pm
I never knew there was a fourth!
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In this episode we chat about artificial intelligence, where we think it may lead, potential benefits and pitfalls, and how it may impact creative endeavors and knowledge work.
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Working in Innovation is more than coming up with creative and unique ideas. It is about how you present the ideas to your potential customers during the important phases of interviews and testing and of course, when you present them to your sponsors. If you have been to a design thinking workshop at our labs or elsewhere you know the emphasis we put on creating a prototype. And let’s be honest, knowing what tool to use and where to start is not an easy task.
Join this workshop to learn about the fundamental Design Tools and which ones suit your business needs, which are the most challenging and the easiest to use. Let our UX/UI designer walk you through the process of choosing the right tool.
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Tags: 2022, Cookhouse Labs, Design Tools, Innovation, Insurance Innovation, Masterclass
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CASL Acceptable Use
ANTI-SPAM COMMITMENT
msg global solutions Canada Inc., and each of its affiliates or programs (“Cookhouse Labs”), is committed to providing transparency to outside parties with respect to the compliance of the organization’s electronic communication with Canada’s Anti-Spam Legislation, in effect as of July 1st, 2014, and its relevant rules and regulations (hereinafter “CASL”). To that end, the Msg global solutions Anti-Spam Commitment is a formal statement of rights and obligations which is made available to outside parties. It is intended to inform outside parties of the type of responsible and transparent practices adopted by Msg global solutions when electronically communicating with outside parties, to inform outside parties about who they may contact at Msg global solutions for any concern pertaining to electronic communications, and to inform such outside parties of where and how they may unsubscribe to any electronic communications from msg global solutions. This document also includes a series of answers to questions about spam and msg global solutions’ practices that are frequently asked by outside parties that msg global solutions may communicate with.
1. APPLICATION AND SCOPE.
This Anti-Spam Commitment generally applies to any electronic communications sent by msg global solutions to outside parties and is protected by a range of business procedures, processes and policies to ensure that such communications are done in compliance with CASL. msg global solutions, in its electronic communications with outside parties, has to comply with the rules established by CASL and enforced by the Canadian Radio-television and Telecommunications Commission, the Competition Bureau and the Privacy Commissioner of Canada. CASL regulates all commercial electronic messages (“CEM”), which are messages that include among their purposes, the encouragement of participation in a commercial activity.
2. WHAT IS msg global solutions DOING TO COMPLY WITH CASL?
msg global solutions has undertaken various initiatives in order to make sure that it is compliant with CASL. It has adopted this Anti-Spam Commitment to provide transparency to outside parties with respect to the compliance of the organization’s electronic communication practices with CASL, as well as undertaken the following initiatives:
msg global solutions has implemented CASL compliant consent forms
The consent of outside parties is necessary in order for msg global solutions to send a CEM. This consent typically must be “express”, but in certain circumstances consent can be “implied” and in others, messages are specifically exempt from consent requirements. msg global solutions has modified certain consent forms in order to ensure that the express consent obtained from recipients is in compliance with CASL.
Your communication preferences can be updated at any time by visiting the Preference Centre, which can be accessed at any time via our website, and you will be notified via email when changes have been made. You can have your email address removed from our mailing lists at any time, although even if you unsubscribe to receiving CEM from Msg global solutions, you may still receive electronic messages from msg global solutions which relate to an ongoing business relationship or which are exempt under CASL.
msg global solutions has modified its email footers
msg global solutions has modified its email footers to manage CASL’s consent requirements. Under CASL, all CEM sent must include certain prescribed content. For example, msg global solutions must clearly identify itself as the party sending the CEM, provide a method whereby the recipient can readily contact msg global solutions, such as a mailing address and one of (i) a telephone number with active response voicemail; (ii) an email address; or (iii) a web address; and provide a working unsubscribe mechanism. CASL compliant email footers have been updated on all CEM sent from msg global solutions, in order to ensure compliance with CASL.
msg global solutions has incorporated Unsubscribe Mechanisms
msg global solutions has incorporated unsubscribe mechanisms into all CEM, in order to manage CASL’s consent requirements. Under CASL, each CEM must provide a working unsubscribe mechanism (functional for 60 days), which must be processed without delay, within a maximum of 10 business days. msg global solutions has set up a uniform process in order to ensure that all unsubscribe requests will be complied with. You may at any time unsubscribe from receiving CEM from Msg global solutions by following the process laid out in the electronic message you receive, or contact: [email protected]. However, even if you unsubscribe to receiving CEM from msg global solutions, you may still receive electronic messages from msg global solutions which relate to an ongoing business relationship or which are exempt under CASL.
3. WHY ARE YOU RECEIVING AN ELECTRONIC MESSAGE FROM msg global solutions?
The types of CEM msg global solutions may send from time to time could include the following:
CEM sent to msg global solutions’s current or potential clients (whether individuals or businesses), by msg global solutions’s marketing department or msg global solutions’s sales and business development team. For example, this may be for prospecting purposes or in order to answer a request for information or an inquiry;
CEM sent to msg global solutions’s current or potential service providers by various msg global solutions business units. For example, msg global solutions may outsource part of its activities to a third party (such as a maintenance company, a translation service provider, etc.) or, msg global solutions HR department may contact potential employees electronically for recruitment purposes.
CEM sent to msg global solutions’s current or potential business partners. For example, this may be for building or finding new ventures and partnerships with industry companies, groups and associations.
If you have received a CEM from msg global solutions and you believe that you should not have, please assess as to whether you have provided implied or express consent to receive CEMs from msg global solutions, or if an exemption applies.
Express Consent: You can verify that you have provided express consent to msg global solutions (or verify the status of your consent) by reviewing your preferences in the Preference Centre.
Implied Consent: msg global solutions may infer your implied consent if:
msg global solutions has an existing business relationship with you or has a former business relationship with you which terminated less than two years ago (for instance, you are a former client);
msg global solutions received an inquiry from you within the last 6 months;
You disclosed your electronic address to a msg global solutions employee (for example you provided your business card to a msg global solutions employee) or you conspicuously published your electronic address (for example, via a corporate website or in a brochure) and the CEM sent is in connection with your business role and function.
When is an electronic message exempt under CASL?
If you are receiving an electronic communications from msg global solutions, it may be a message that is exempt under CASL. This would be the case if the message is one the following:
you have a personal relationship with the msg global solutions employee who contacted you, meaning that you have previously had a direct, voluntary, two-way communication;
you have a family relationship with the msg global solutions employee who contacted you;
the CEM is sent within msg global solutions (between employees of msg global solutions);
the CEM is sent between msg global solutions and another business, where there is an ongoing relationship between msg global solutions and this other business;
the CEM was sent by msg global solutions to you, in order to respond to your request or inquiry; or
the CEM was sent by msg global solutions to you, in order to enforce a legal right or obligation (for instance, if you have an outstanding debt, or breached a contract that you have with msg global solutions, etc.).
In certain situations, your consent is not required for certain types of messages sent by msg global solutions, although you may still unsubscribe from future transmission of similar messages. The type of messages where msg global solutions does not need your consent is an electronic message that:
is sent once, following a referral by a current msg global solutions client, service provider, business partner or employee who also has a personal or business relationship with you;
only provides you with a request for a quote or an estimate;
only facilitates or confirms a transaction;
only provides msg global solutions warranty, product recall, safety or security information; or
only provides information about your ongoing use of msg global solutions services or products or ongoing purchases (including updates and upgrades).
If you have received a CEM, and you believe that you should not have, please contact us immediately at [email protected] and we will promptly remove your address from our list (within maximum 10 business days).
4. SOCIAL MEDIA.
You may be contacted by a msg global solutions employee via social media, such as LinkedIn, if you are connected on the social network with the msg global solutions employee, or if you have indicated through your preference settings the fact that you are open to receiving messages about new business opportunities or ventures. You may also be contacted if you have conspicuously published your electronic address (for instance, on a social media website), have not indicated that you did not wish to receive CEM, and the CEM sent is in connection with your business role and function.
5. WHAT TO DO IF YOU NO LONGER WISH TO RECEIVE CEM FROM msg global solutions.
At msg global solutions, we take the law very seriously. You may unsubscribe at any time from receiving CEM, by visiting the Preference Centre or clicking on the link in any CEM that you may receive from Msg global solutions, and we will remove you from our list within ten (10) business days.
6. AMENDMENT OF THIS ANTI-SPAM POLICY AND GUIDELINES.
From time to time, Msg global solutions will review and update this Anti-Spam Commitment as required to keep current with rules and regulations, new technologies, standards, our business practices and outside parties’ concerns. We will post any Anti-Spam Commitment changes on this page and, if the changes are significant, we will provide a more prominent notice (including, as the case may be, email notification of Anti-Spam Commitment changes).
7. QUESTIONS OR CONCERNS ABOUT THIS ANTI-SPAM COMMITMENT?
If you need further assistance, please contact us at: [email protected]
Revised November 11, 2019.
Accessibility for Ontarians with Disabilities
Documents Available Notification (AODA)
Dear Valued Clients and Visitors,
msg global solutions Canada Inc. has created policies and procedures to meet their obligations regarding customer service outlined in the Integrated Accessibility Standards under the Accessibility for Ontarians with Disabilities Act. 2005.
Our accessibility policies are available for your review in a number of formats. Should you wish access to these documents in another way, please notify: [email protected]
Statement of Organizational Commitment
msg global solutions is committed to ensuring equal access and participation for people with disabilities. We are committed to treating people with disabilities in a way that allows them to maintain their dignity and independence. We believe in integration and we are committed to meeting the needs of people with disabilities in a timely manner. We will do so by removing and preventing barriers to accessibility and meeting our accessibility requirements under the Accessibility for Ontarians with Disabilities Act and Ontario’s accessibility laws.
Statement of Commitment to Accessibility
msg global solutions is committed to providing a barrier-free environment for all stakeholders including our clients/customers, employees, job applicants, suppliers, and any visitors who may enter our premises, access our information, or use our services. As an organization, we respect and uphold the requirements set forth under the Accessibility for Ontarians with Disabilities Act(2005), and its associated standards and regulations.
msg global solutions understands that we have a responsibility for ensuring a safe, dignified, and welcoming environment for everyone. We are committed to ensuring our organization’s compliance by incorporating accessibility legislation into our
policies, procedures, equipment requirements, training, and best practices. We will review these policies and practices annually, as organizational changes occur, or in anticipation of compliance deadlines. In addition, we will strive to meet the needs of individuals with disabilities in a timely and effective manner.
Providing an accessible and barrier-free environment is a shared effort, and as an organization, msg global solutions is committed to working with the necessary parties to make accessibility for all a reality. For more detailed information on our accessibility policies, plans, and training programs, please contact [email protected]
Alternate Format Request Form
msg global solutions is committed to providing accessible, quality services. Communications in alternate formats will be made available upon request within a reasonable time period in a mutually agreed upon format. Please complete and submit the below form to [email protected]
Accessibility Standards for Customer Service Record of Customer
Thank you for visiting msg global solutions and Cookhouse Labs. We value all our clients and strive to meet everyone’s needs. We look forward to your feedback. Please send us an email at [email protected]
Privacy Policy
msg global and its innovation lab program Cookhouse Labs Privacy Policy
We at msg global solutions and our affiliates or programs (e.g. Cookhouse Labs) are committed to protecting your privacy. This Privacy Policy applies to our website and all of its associates websites (the “Websites”). This Privacy Policy governs our data collection, processing and usage practices. It also describes your choices regarding use, access and correction of your Personal Information. By using the Websites, you consent to the data practices described in this Privacy Policy. If you do not agree with the data practices described in this Privacy Policy, you should not use the Websites.
The terms of our privacy policy, which can be found below, is based on the ten principles set out the Canadian Standards Association’s Model Code for the Protection of Personal Information.
Ten Privacy Principles
Accountability: We are responsible for personal information under our control and we have designated individuals who are accountable for our compliance with these privacy principles.
Identifying Purposes: We shall disclose the purposes for which we collect your personal information either before or at the time the information is collected.
Consent: Your knowledge and consent is required for our collection, use or disclosure of your personal information, subject to certain exceptions set out in the law. Your consent may be expressed in writing, verbally, electronically, and in certain circumstances, may also be implied.
Limiting Collection: Your personal information shall only be collected by fair and lawful means, and will be limited to that which is necessary for the identified purposes.
Limiting Use, Disclosure and Retention: Your personal information may only be used or disclosed for the purposes for which it was collected, other purposes to which you have consented or if required by law. Your personal information shall be retained only as long as necessary for the fulfillment of identified purposes, or as required or permitted by law.
Accuracy: We shall use reasonable efforts to ensure that your personal information is accurate, complete and as up-to-date as is necessary for the purposes for which it is to be used.
Safeguards: We shall protect your personal information using security safeguards appropriate to the sensitivity of your information to prevent unwanted release, misuse or intrusion.
Openness: Information about our privacy policies and procedures for handling your personal information shall be made available to you.
Individual Access: Upon written request, you will be informed of the existence, use and disclosure of your personal information. In addition, you will be given access to your personal information, as permitted by law. You may also verify the accuracy and completeness of your personal information and, where appropriate, request that it be amended.
Inquiries and Concerns: You may contact us if you have any questions or concerns about our privacy policies and procedures.
We periodically update this Privacy Policy. We encourage you to review this Privacy Policy periodically.
Important Information
1.1. Changes to this Privacy Policy
We may update or replace this Privacy Policy from time to time by posting a new version online. You should check this page occasionally to review any changes. If we make any material changes we will notify you by posting the revised Privacy Policy on our Websites. This helps you to always be aware of what information we collect, how we use it and under what circumstances, if any, it is disclosed. Your continued use of the Websites and/or continued provision of Personal Information to us will be subject to the terms of the then-current Privacy Policy.
1.2. Contact Us
If you have any questions about this Privacy Policy or our treatment of the information you provide us, please write to us by email at: [email protected], or by postal mail to: Cookhouse Labs, 30-34 Duncan St, Toronto, Ontario, Canada M5H 1A1, Attn: Privacy.
Information We Collect
2.1. When You Visit our Websites
You are free to explore the Websites without providing any information about yourself. However, when you visit the Websites, we may request that you provide Personal Information about yourself and we will collect Navigational Information.
2.2. “Personal Information”
This refers to any information that you voluntarily submit to us through the use of our Websites, and that identifies you personally, including contact information, such as your name, e-mail address, company name, address, phone number, and other information about yourself or your business. Personal Information can also include information about any transactions, both free and paid, that you enter into on the Websites, and information about you that is available on the internet, such as from Facebook, LinkedIn, Twitter, Instagram and Google, or publicly available information that we acquire from service providers.
2.3. “Navigational Information”
This refers to information about your computer and your visits to this website such as your IP address, geographical location, browser type, referral source, length of visit and pages viewed. Please see section 4 the “Navigation Information” section, below.
2.4. Information About Individuals Under 18
The Websites are not intended for or targeted at individuals under 18, and we do not knowingly or intentionally collect information about individuals under 18. If you believe that we have collected information about an individual under 18, please contact us at: [email protected], or by postal mail at: Cookhouse Labs, 30-34 Duncan Street , Toronto, Ontario, Canada M5H 1A1, Attention: Privacy, so that we may delete the information.
How We Use Information We Collect
3.1. We Never Sell Personal Information
We will never sell your Personal Information to any third party.
3.2. Use of Personal Information
In addition to the uses identified elsewhere in this Privacy Policy, we may use your Personal Information to: (a) improve your browsing experience by personalizing the Websites and to improve underlying marketing software; (b) send information to you which we think may be of interest to you by post, email, or other means; and (c) provide other companies with anonymized statistical information about our users — but this information will not be used to identify any individual user. We may, from time to time, contact you on behalf of external business partners about a particular offering that may be of interest to you. In those cases, we do not transfer your unique Personal Information to the third party. In addition, we may share data with trusted partners to contact you based on your request to receive such communications, help us perform statistical analysis, or provide customer support. Such third parties are prohibited from using your Personal Information except for these purposes, and they are required to maintain the confidentiality of your information.
3.3. Use of Navigational Information
We use Navigational Information to operate and improve the Websites and underlying marketing software. We may also use Navigational Information alone or in combination with Personal Information to provide you with personalized information about the Software Provider.
3.4. Customer Testimonials and Comments
We post customer testimonials and comments on our Websites, which may contain Personal Information. We obtain each customer’s consent via email prior to posting the customer’s name and testimonial.
3.4. Use of Credit Card Information
We do not directly collect credit card information from you. We use a third-party service provider to manage credit card processing. This service provider is not permitted to store, retain, or use information you provide except for the sole purpose of credit card processing on our behalf.
3.5. Service Providers
We employ other companies and people to provide services to visitors to our Websites, such as the use of underlying marketing software, and may need to share your information with them to provide information, products or services to you. Examples may include removing repetitive information from prospect lists, analyzing data, providing marketing assistance, processing credit card payments, supplementing the information you provide us in order to provide you with better service, and providing customer service. In all cases where we share your information with such agents, we explicitly require the agent to acknowledge and adhere to our privacy and customer data handling policies.
3.6. Security of your Personal Information
We use a variety of security technologies and procedures to help protect your Personal Information from unauthorized access, use or disclosure. We secure the Personal Information you provide on computer servers in a controlled, secure environment, protected from unauthorized access, use or disclosure. When sensitive Personal Information (such as geo-location data) is collected on our Websites and/or transmitted to other websites, it is protected through the use of encryption, such as the Secure Socket Layer (SSL) protocol.
If you have any questions about the security of your Personal Information, you can contact us at: [email protected], or by postal mail at: Cookhouse Labs, 30-34 Duncan Street, Toronto, Ontario, Canada M5H 1A1, Attention: Privacy.
3.7. Social Media Features
Our Websites include social media features (“Social Media Features”), such as the Facebook Like button, the Share This button or interactive mini-programs that run on our sites. These features may collect your IP address, which page you are visiting on our sites, and may set a cookie to enable the feature to function properly. Social Media Features are either hosted by a third party or hosted directly on our Websites. This Privacy Policy does not apply to these features. Your interactions with these features are governed by the privacy policy and other policies of the companies providing them.
3.8. External Websites
Our Websites provide links to other websites. We do not control, and are not responsible for, the content or practices of these other websites. Our provision of such links does not constitute our endorsement of these other websites, their content, their owners, or their practices. This Privacy Policy does not apply to these other websites, which are subject to any privacy and other policies they may have.
3.9. Retention of Personal Information
We retain Personal Information that you provide us as long as we consider it potentially useful in contacting you about our services and products, or as needed to comply with our legal obligations, resolve disputes and enforce our agreements, and then we securely delete the information. We will delete this information from the servers at an earlier date if you so request, as described in the “Opting Out and Unsubscribing” section below.
If you have elected to receive marketing communications from us, we retain information about your marketing preferences for a reasonable period of time from the date you last expressed interest in our content, products, or services, such as when you last opened an email. We retain information derived from cookies and other tracking technologies for a reasonable period of time from the date such information was created.
3.10. International Transfer of Information
To facilitate our global operations, we may transfer and access Personal Information from around the world, including Canada. This Privacy Policy shall apply even if we transfer Personal Information to other countries. We have taken appropriate safeguards to require that your Personal Information will remain protected.
3.11. Corporate Events
If we (or our assets) are acquired by another company, whether by merger, acquisition, bankruptcy or otherwise, that company would receive all information gathered on the Websites. In this event, you will be notified via email and/or a prominent notice on our Website, of any change in ownership, uses of your Personal Information, and choices you may have regarding your Personal Information.
3.12. Compelled Disclosure
We reserve the right to use or disclose your Personal Information if required by law or if we reasonably believe that use or disclosure is necessary to protect our rights; protect your safety or the safety of others; investigate fraud; or comply with a law, court order or legal process.
Navigational Information
4.1. Cookies
Cookhouse Labs use cookies or similar technologies to analyze trends, administer the Website, track users’ movements around the Website and to gather demographic information about our user base, as a whole.
We use “cookies” to help you personalize your online experience. A cookie is a text file that is placed on your hard disk by a web server. Cookies are not used to run programs or deliver viruses to your computer. Cookies are uniquely assigned to you, and can only be read by a web server in the domain that issued the cookie to you. One of the primary purposes of cookies is to provide a convenience feature to save you time. The purpose of a cookie is to tell the web server that you have returned to a specific page. For example, if you personalize pages on our Websites, a cookie helps us to recall your specific information on subsequent visits. When you return to the same Website, the information you previously provided can be retrieved, so you can easily use the customized features.
You have the ability to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. If you choose to decline cookies, you may not be able to fully experience the interactive features of the Websites you visit. Cookhouse Labs keeps track of the Websites and pages you visit within Cookhouse Labs, in order to determine what portion of the Website is the most popular or most used. This data is used to deliver customized content and promotions within the Website to customers whose behavior indicates that they are interested in a particular subject area.
4.2. Log Files
We may collect demographic information, such as your postal or zip code, age, gender, preferences, interests and favorites using log files that are not associated with your name or other Personal Information. There is also information about your computer hardware and software that is automatically collected by us. This information can include: your IP address, browser type, domain names, internet service provider (ISP), the files viewed on our site (e.g., HTML pages, graphics, etc.), operating system, clickstream data, access times and referring website addresses. This information is used by Cookhouse Labs for marketing purposes, to maintain the quality of the Websites and to provide general statistics regarding use of the Website. For these purposes, we do link this automatically-collected data to Personal Information, such as name, email address, address and phone number.
4.3. Clear Gifs (Web Beacons/Web Bugs)
We employ a software technology called clear gifs (a.k.a. “web beacons” or “web bugs”), that help us better manage the Website by informing us what content is effective. Clear gifs are tiny graphics with a unique identifier, similar in function to cookies, and are used to track the online movements of visitors to our Websites. In contrast to cookies, which are stored on a user’s computer hard drive, clear gifs are embedded invisibly on web pages or in emails and are about the size of the period at the end of this sentence. We use clear gifs in our HTML-based emails to let us know which emails have been opened by recipients. This allows us to gauge the effectiveness of certain communications and the effectiveness of our marketing campaigns. We tie the information gathered by clear gifs in emails to our customers’ Personal Information. If you would like to opt-out of these emails, please see “Opting Out and Unsubscribing”.
4.5. Third Party Tracking Technologies
The use of cookies and web beacons by any tracking utility company is not covered by our Privacy Policy. We do not have access or control over these third party tracking technologies.
How to Access & Control Your Personal Data
5.1. Reviewing, Correcting and Removing Your Personal Information
Upon request Cookhouse Labs will provide you with information about whether we hold any of your Personal Information. You have the following rights with respect to that information:
To request access, correction, updates or deletion of your personal information;
To object to processing of your personal information;
To restrict processing of your personal information;
To request portability of your personal information; and
To opt out of being solicited by Cookhouse Labs,
To exercise any of these rights, please contact us at: [email protected], or by postal mail at: Cookhouse Labs, 30-34 Duncan Street, Toronto, Ontario, Canada M5H 1A1, Attention: Privacy. We will respond to your request to change, correct or delete your information within a reasonable timeframe, and notify you of the action we have taken.
If we have collected and process your personal information with your consent, then you can withdraw your consent at any time. Withdrawing your consent will not affect the lawfulness of any processing we conducted prior to your withdrawal, nor will it affect processing of your personal information conducted in reliance on lawful processing grounds other than consent.
You have the right to complain to a data protection authority about our collection and use of your personal information.
5.2. Anti-Spam Policy
Our Acceptable Use Policy, at: www.cookhouselab.com/casl-acceptable-use, applies to us and, among other things, prohibits us from sending unsolicited commercial email in violation of applicable laws, and requires the inclusion of an “opt-out” mechanism in any commercial electronic messages that we send.
5.3. To Unsubscribe From Our Communications
You may unsubscribe from our marketing communications by clicking on the “unsubscribe” link located on the bottom of our commercial electronic messages, contacting us at: [email protected], or by postal mail at: Cookhouse Labs, 30-34 Duncan Street, Toronto, Ontario, Canada M5H 1A1, Attention: Privacy.
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Building hope in a space and movement where change feels unlikely
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Chemical and Biomolecular Engineering at The Ohio State University with research interest in bringing ... research to impact social, political, and ethical issues. His other passions lie in working to increase the ... Environmental Policy and Decision Making with Research Distinction from the School of Environment and Natural ...
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Extension Today: Healthy Holiday Sides
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research team are working to improve the nutritional quality of food crops, focusing on tomatoes which are ... Department of Horticulture and Crop Science, as well as the Department of Food Science and Technology. Recent research at ... skin cancers. Learn more in this article – Ohio State Researchers: Eating Tomatoes May Protect Against ...
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College of Food Agriculture and Environmental Sciences
© 2021, The Ohio State University
College of Food, Agricultural And Environmental Sciences Center For Cooperatives
1864 Shyville Road
Piketon, Ohio 45661
Phone: 740-289-2071 Ext 111
[email protected]
Accessibility Accommodation
If you have a disability and experience difficulty accessing this content request accommodation here.
Maintenance and Support
This site designed and maintained by CFAES Marketing and Communications. For technical support please contact the CFAES Helpdesk.
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Nate King-Smith
https://cornishlab.cfaes.ohio-state.edu/people/nate-king-smith
Nate King-Smith Graduate Research Associate [email protected] Nathaniel King-Smith ... Biological Engineering advised by Dr. Katrina Cornish. His research focus is on hydroponics and controlled ... (TK). Nate's research explores the possibility of vertical farming of the TK plant as well as ...
Sharon Varughese
https://cornishlab.cfaes.ohio-state.edu/people/sharon-varughese
Sharon Varughese Graduate Research Associate [email protected] Sharon earned B.S ... research interests focuses on using bio based emergent materials to enhance the design and testing of ...
Parker Evans
https://cornishlab.cfaes.ohio-state.edu/ParkerEvans
research focused on conducting polymer devices known as chemical actuators and their use in controlling ...
David Lankitus
https://cornishlab.cfaes.ohio-state.edu/DavidLankitus
program, a science communication and public outreach program for plant science researchers at OSU, and is ...
Communicating Science for Graduate Students
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It is incredibly important that scientists communicate their research in ways that the general ...
Applying to the Internal Grants Program?
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important that scientists communicate their research in ways that the general public can understand. In this ...
CFAES Door Decorating Competition
https://grad.cfaes.ohio-state.edu/news/cfaes-door-decorating-competition
Research and Graduate Education, and the Graduate Student Advisory Council will be hosting a Graduate ... as a base so as not to damage the doors. White paper can be picked up in in the Research and Graduate ... Education Suite: Columbus (106 Agricultural Administration) or in Wooster (Research Services 208). There are ...
Write In
https://grad.cfaes.ohio-state.edu/events/write
people at each location. Where: Agricultural Engineering 245 (Columbus) and Research Services 126 ...
Fall Fruit Update 2
https://southcenters.osu.edu/events/fall-fruit-update-2
Come join us for timely fruit research updates for your farm and garden, and to get your questions ... presentations recorded from the OSU South Centers research fields, as well as live question and answer segments. ...
CAST Science Communication Scholarship
https://grad.cfaes.ohio-state.edu/news/cast-science-communication-scholarship
scientific research with science communication. Specifically, this scholarship fund enables OSU graduate ... level students and veterinary medicine students, focusing on careers in agriculture and food research ... conversational): What is your research, and what part of your research are you communicating in your media example? ...
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College of Food Agriculture and Environmental Sciences
© 2021, The Ohio State University
College of Food, Agricultural And Environmental Sciences Center For Cooperatives
1864 Shyville Road
Piketon, Ohio 45661
Phone: 740-289-2071 Ext 111
[email protected]
Accessibility Accommodation
If you have a disability and experience difficulty accessing this content request accommodation here.
Maintenance and Support
This site designed and maintained by CFAES Marketing and Communications. For technical support please contact the CFAES Helpdesk.
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Three Campuses
https://cfaes.osu.edu/about/three-campuses
= 4,254 acres, with 88 county Extension offices and 11 research stations and field labs ...
Chow Line: Refrigerated leftovers safe to eat up to four days after Thanksgiving
https://cfaes.osu.edu/news/articles/chow-line-refrigerated-leftovers-safe-eat-four-days-after-thanksgiving
outreach and research arms, OSU Extension and the Ohio Agricultural Research and Development Center. Send ...
Building hope in a space and movement where change feels unlikely
https://epn.osu.edu/blog/building-hope-space-and-movement-where-change-feels-unlikely
Chemical and Biomolecular Engineering at The Ohio State University with research interest in bringing ... research to impact social, political, and ethical issues. His other passions lie in working to increase the ... Environmental Policy and Decision Making with Research Distinction from the School of Environment and Natural ...
A Research Powerhouse
https://cfaes.osu.edu/about/research-powerhouse
$50 million in new awards 637 proposals submitted 25 new innovators 38 invention disclosures 18 patent filings ...
CFAES Wooster event to explore robotics in agriculture
https://cfaes.osu.edu/news/articles/cfaes-wooster-event-explore-robotics-in-agriculture
[email protected] 330-202-3533 Extension OARDC Research College Events/Announcements Crops Livestock Research ...
Chow Line: Thawing a frozen turkey safely
https://cfaes.osu.edu/news/articles/chow-line-thawing-frozen-turkey-safely
and its outreach and research arms, Ohio State University Extension and the Ohio Agricultural Research ...
Strategic Alignment and Plan
https://cfaes.osu.edu/about/strategic-alignment-and-plan
(co-creators) in the teaching, research, and Extension missions. Enhance and reward excellent teaching and ... create breakthrough scientific research to advance solutions to the most pressing global challenges ... facing humanity. Utilize basic, applied, action, and convergent research to solve critical issues for ...
EPN Breakfast- December 7, 2021, Restoring Streams and Wetlands – Whose Job Is It Anyway?
https://epn.osu.edu/events/epn-breakfast-december-7-2021-restoring-streams-and-wetlands-%E2%80%93-whose-job-it-anyway
His thesis research (Atrazine Fate and Transport in a Created Flow-through Emergent Marsh: An ... Examination of Key Processes) was completed at the internationally-acclaimed Olentangy River Wetland Research ... working with governmental, research and policy interests to develop collaborative approaches and ...
EPN Breakfast- December 7, 2021, Restoring Streams and Wetlands – Whose Job Is It Anyway?
https://cfaes.osu.edu/news/events/epn-breakfast-december-7-2021-restoring-streams-and-wetlands-%E2%80%93-whose-job-is-it-anyway
His thesis research (Atrazine Fate and Transport in a Created Flow-through Emergent Marsh: An ... Examination of Key Processes) was completed at the internationally-acclaimed Olentangy River Wetland Research ... working with governmental, research and policy interests to develop collaborative approaches and ...
Ohio Maple Days 2021
https://agnr.osu.edu/events/ohio-maple-days-2021
Join us on Saturday, December 11th for a day of all things maple! We will highlight research from ... Ohi State's two ACER research grants and introduce you to some of our reserach partners from West ... Tapping Red Maple- Abby van den Berg, University of Vermont, Proctor Maple Research Center 3: 30 pm ...
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College of Food Agriculture and Environmental Sciences
© 2021, The Ohio State University
College of Food, Agricultural And Environmental Sciences Center For Cooperatives
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Piketon, Ohio 45661
Phone: 740-289-2071 Ext 111
[email protected]
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Scorpions are predatory arachnids of the order Scorpiones. They have eight legs and are easily recognized by the pair of grasping pedipalps and the narrow, segmented tail, often carried in a characteristic forward curve over the back, ending with a venomous stinger. Scorpions range in size from 9 mm / 0.3 in. (Typhlochactas mitchelli) to 23 cm / 9 in. (Heterometrus swammerdami).
The evolutionary history of scorpions goes back to the Silurian period 435 million years ago. They have adapted to a wide range of environmental conditions, and they can now be found on all continents except Antarctica. Scorpions number about 1,750 described species, with 13 extant (living) families recognized to date. The taxonomy has undergone changes and is likely to change further, as genetic studies are bringing forth new information.
All scorpions have a venomous sting, but the vast majority of the species do not represent a serious threat to humans, and in most cases, healthy adults do not need any medical treatment after being stung. Only about 25 species are known to have venom capable of killing a human. In some parts of the world with highly venomous species, human fatalities regularly occur, primarily in areas with limited access to medical treatment.
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As we head into VMworld 2021 this week, there will be many announcements about new and updated VMware products and features. However, there is one that I want to bring to your attention. It is something that I have been directly involved in, in some small way, and that something is Tanzu Community Edition. Tanzu Community Edition (sometimes referred to as TCE), is a free, open source Tanzu Kubernetes (TKG) distribution which has all of the same open source software found in our commercial editions of Tanzu. Personally, I find this to be a really cool announcement for a number of reasons, which I will briefly outline below.
Support for Docker
Probably the best reason for checking out Tanzu Community Edition is that it supports many different deployment options. For example, Tanzu Community Edition can be used to deploy a Tanzu Kubernetes cluster to your laptop/desktop – you do not need access to an vSphere environment, nor do you need access to hyperscalers such as AWS of Azure to try it out. So long as you have Docker on your desktop, you can stand up TCE. Not only that, but TCE provides two deployment options on Docker. You can deploy a standalone Kubernetes cluster, or you can stand-up the more common Tanzu Kubernetes configuration of a management cluster, plus X number of workload clusters, depending on the resources available on your laptop/desktop. Under the covers, this is leveraging Kind (Kubernetes in Docker), but you still gain that familiarity of working with the tanzu command line interface (CLI) for interacting with the cluster(s). Note that I stated that you did not need access to an vSphere or cloud infrastructure to use TCE. I should clarify that you can absolutely use these infrastructures with Tanzu Community Edition, should you wish to do so. For those of you who have vSphere home labs, this is an excellent opportunity to get hands-on skills with Tanzu Kubernetes. If you do plan to deploy and run TCE on Docker, pay particular attention to the resource requirements, particularly memory, as in many cases the defaults are not sufficient. The deployment is very simple, and can be driven either via the UI, or from the tanzu CLI. Here is a screenshot of a standalone deployment of TCE on Docker. It begins by offering you different infrastructures to deploy to. In this case, Docker is chosen:
There are only a few additional criteria required to deploy on Docker, such as cluster name and proxy settings (if any). Note that it also validates that you have Docker running locally on your laptop/desktop:
You can then decide whether you want to complete the deployment via the UI, or to copy and paste the actual tanzu deployment command to a command line on your laptop/desktop. Assuming you continue with the UI approach, and the deployment is successful, you will hopefully see something like this with a green “Installation complete” banner once the various images have been pulled in, and the Kind cluster / docker containers have successfully deployed. Note that depending on your available bandwidth, it may take a number of minutes to pull down the docker images required to standup the cluster. However, once the image has been pulled down to you laptop/desktop for the very first time, subsequent deployments should be much quicker.
And just to repeat, this is just one approach. TCE can be used to deploy both standalone and managed TKG clusters to Docker on your local desktop, but can also deploy fully conformant Kubernetes clusters on to vSphere, AWS and Azure.
A new way of learning and evaluating Kubernetes
As many regular readers will be aware, VMware offers free Kubernetes training through its KubeAcademy initiative. I’ve mentioned this a few times in previous blog posts on this site. With the release of Tanzu Community Edition, you now have a distribution of Tanzu Kubernetes which allows you to do your own training, testing and evaluation with VMware’s own brand of Kubernetes from your laptop or desktop. And even if you already have TKG in production, this free, open source distribution allows you to build on your existing skills and do your own research of potential applications without the risk of a “suck it and see” approach to deployments on your production clusters. You can get comfortable with the tanzu CLI for managing both clusters and packages, all from the comfort of your own laptop or desktop, or even to your own home lab vSphere environment should you have one available.
Community packages
Tanzu Community Edition comes with a whole plethora of community packages that are simple to deploy and use via the kapp-controller from Carvel. These packages include Pinniped and Dex for Identity Management, allowing the clusters to be integrated with LDAP for example. For monitoring, TCE includes a Prometheus package for log gathering/storing and Grafana for visualization. Our own Velero product is included for backup and restore of your apps. It also has Contour which provides Ingress/HTTPProxy services via Envoy. It come with fluent bit package for capturing and forwarding logs from a Kubernetes cluster, and a whole range of additional community packages. The reason this is so interesting is that you can now build and test a complete application stack in TCE before ever deploying it to your production TKG environment. Say, for example, that you were looking to standup a monitoring stack in production.
There could be a lot of dependencies to get right, and also a lot of configuration to setup. You may require a cert manager that ensures secure communication between the various parts of the stack. You may want to use Contour to provide Ingress/HTTPProxy to Prometheus and Grafana. Prometheus would need to be configured as a data source for Grafana, and you may need to create some basic dashboards to display cluster metrics. That is a lot of things to get right before ever seeing your first Grafana dashboard with some cluster metrics. The great thing about the community packages is that much of this integration is done for you. Using TCE community packages you can very quickly stand up a monitoring stack (even on Docker) before ever trying it out in production. You can then examine whether the certificate configuration is working, what data is being collected, examine resource consumption so that you can size your production environment correctly, and also think about what other additional dashboard information might be required for production. We have a number of guides around how to get started with packages in the official TCE documentation so check them out before you start.
One final thing to point out is that you will see us promote Tanzu Community Edition as “batteries included, but swappable”. By that, we mean that you are not obligated to use these community packages if you do not want to. Should you wish, you can deploy a TCE cluster, and then use other package managers (such as Helm) to deploy your applications of choice. This is because the Kubernetes clusters that are getting deployed in TCE are fully conformant Kubernetes clusters. Indeed, you could use a combination of package managers such as Helm and Carvel to deploy various components that make up an application. For example, if I wanted to test the fluent-bit logging functionality in TCE, I might deploy ElasticSearch and Kibana (ELK stack) and then use the tanzu package manager to configure and deploy the fluent-bit community package to ship the logs to ELK.
Community supported
As mentioned, Tanzu Community Edition is a free, open-source Tanzu Kubernetes distribution. There are no usage limitations and it is a community supported project. If you need assistance, join the TCE google group and look for the #tanzu-community-edition slack channel in Kubernetes.
For more information about Tanzu Community Edition, check out tanzucommunityedition.io. This has the download links and the official documentation. The first public release is v0.9.1. We look forward to hearing your feedback and suggestions.
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Nobody wants to be treated badly by an employer or headhunter. By rating your experience you can help ensure that you – and everyone else – are treated well in the next recruitment process.
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Welcome to the Quranic Arabic Corpus, an annotated linguistic resource which shows the Arabic grammar, syntax and morphology for each word in the Holy Quran. The corpus provides three levels of analysis: morphological annotation, a syntactic treebank and a semantic ontology.
The Quran is a significant religious text written in Quranic Arabic, and is followed by believers of the Islamic faith. The Quran contains 6,236 numbered verses (ayāt) and is divided into 114 chapters.
An example verse from the Quran:
(21:30) Have those who disbelieved not considered that the heavens and the earth were a joined entity, and We separated them and made from water every living thing? Then will they not believe?
Version 0.4 Released - new and updated linguistic features in this version of the corpus
Word by Word Quran - maps out the syntax of the entire Quran, with analysis and translation
Quranic Grammar - traditional Arabic grammar (إعراب) illustrated using dependency graphs
How you can get involved
This project contributes to the research of the Quran by applying natural language computing technology to analyze the Arabic text of each verse. The word by word grammar is very accurate, but ensuring complete accuracy is not possible without your help. If you come across a word and you feel that a better analysis could be provided, you can suggest a correction online by clicking on an Arabic word.
World map of users of the Quranic Arabic Corpus, provided by Google Analytics.
Countries with the highest number of users are shaded in darker green.
The map above shows worldwide interest in the Quranic Arabic Corpus. Every day, the website is used by over 2,500 people from 165 different countries. Help us review the information on this website so that together we can build the most accurate linguistic resource for Quranic Arabic.
The Quranic Arabic Dependency Treebank (QADT)
The Quranic treebank is an effort to map out the entire grammar of the Quran by linking Arabic words through dependencies. The linguistic structure of verses is represented using mathematical graph theory. The annotated corpus provides a novel visualization of Quranic syntax using dependency graphs.
The Ontology of Quranic Concepts
The Quranic Ontology uses knowledge representation to define the key concepts in the Quran, and shows the relationships between these concepts using predicate logic. Named entities in verses, such as the names of historic people and places mentioned in the Quran, are linked to concepts in the ontology.
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Global Plumbing Services Market Growth Analysis 2021, Opportunities, Trends, Developments and Forecast Till 2026 – The Host
IBMG adds to P&H offering with acquisition of Sussex Plumbing Supplies – Builders Merchants Journal
Growth in mechanical, electrical and plumbing services
Alabaster Company K&S Plumbing Services Wins Top Contractor Award – Shelby County Reporter
MEP (Mechanical, Electrical & Plumbing) Services Market Size To Increase Dramatically By 2026
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IBMG adds to P&H offering with acquisition of Sussex Plumbing Supplies – Builders Merchants Journal
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By Joyce L. Devitt on October 22, 2021 Plumbing supplies
The largest independent merchant group of builders in the south of England, Independent Builders Merchant Group purchased Sussex Plumbing Supplies Limited (SPS), expanding its network of specialist plumbing and heating dealers.
Founded in 2000, Sussex Plumbing Supplies is headquartered in Polegate, East Sussex, and has seven retail outlets and four bathroom, heating and kitchen showrooms in Sussex and Kent.
Glenn Vinall, owner and CEO of SPS, will continue to lead the company, working with Matt Bland, general manager of IBMG’s plumbing and heating division, to realize the growth opportunities this partnership will offer. Vinall said, “The team and I are proud of everything we’ve built to date, but we recognize that our company will be able to accomplish much more as part of a larger group. The partnership with IBMG will facilitate this growth and we look forward to continuing this success together. “
Following its recent merger with Grant & Stone, this acquisition pursues IBMG’s strategy of developing its divisions by combining acquisition, development of new sites and organic growth. Strategically, it will expand IBMG’s specialty plumbing and heating offering in Sussex and Kent and provide a solid platform for future growth. Culturally, the two companies are very aligned, sharing a common vision, independent ethics and a commitment to providing the highest standards of customer service.
Bland, said: “We are delighted to welcome SPS to the Group. We have a lot in common, so it’s a strong and logical partnership. Geographically, our partnership is very complementary and we are delighted to be able to strengthen our specialist plumbing and heating offer in the south-east of England.
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Winter storm recovery: plumbing supplies are running out, families try to rebuild their lives
Ferguson signs £ 308million sale of his plumbing supplies business Wolseley UK
Bell Plumbing Supplies exclusively targets Unico Systems – Builders Merchants Journal
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Fundraiser Update & this Weekend’s LivestreamCharles Murray & the Specter of White Nationalism | Counter-Currents
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Print June 25, 2021 28 comments
Fundraiser Update & this Weekend’s Livestream
Charles Murray & the Specter of White Nationalism
Greg Johnson
950 words
One of our readers has asked my opinion of Charles Murray’s new book Facing Reality: Two Truths about Race in America (New York: Encounter Books, 2021). I breezed through the ebook version of Facing Reality in just a few hours. I think that this book’s publication is an encouraging sign, and I hope that it will be widely read. America has long denied fundamental truths about race, but in the past year, the costs of these lies have become unbearable. In the words of Jef Costello, we need to “Speak the Truth or Kiss it All Goodbye.” Many new minds have been opened to our message by BLM and Critical Race Theory, so Murray’s book has appeared at an opportune moment.
The first truth Murray writes about is that different races in America have different average IQs, which means that they will perform differently in life even in the absence of racial discrimination. This is a very clear and compelling summary of the best science on the issue. This uncomfortable truth means that blaming the unhappy plight of blacks in America (overrepresented among the poor and ignorant, underrepresented among the prosperous and educated) on white racism is not the best explanation, since these patterns persist even in the absence of anti-black discrimination. Indeed, it persists despite pro-black discrimination. It also implies that we will only achieve racial “equity” by discriminating against other races for the benefit of blacks.
The second truth Murray writes about is that different races in America commit crimes at different rates. This is again a useful summary of the latest data. The reason why blacks, especially black men, have so many run-ins with the police is that they commit a disproportionate amount of crime. It has nothing to do with police “racism.” Indeed, if the police are “racist” at all, it is by under-policing blacks and over-policing whites. (See my article “Black Lies Matter” and Richard Houck’s “Law Enforcement and the Hostile Elite.”) Black Lives Matter’s sole purpose and effect is to make it safer for blacks to commit crimes, which has predictably led to an explosion of black crime. (See my article “Do Black Lives Matter?“)
For more thorough discussions of Facing Reality, see the reviews by Jared Taylor (“Two Cheers for Charles Murray“) and F. Roger Devlin (“Charles Murray’s Facing Reality“).
I want to focus on a feature of Murray’s argument also mentioned by Taylor and Devlin. Murray warns the current establishment that if they don’t stop promoting non-white identity politics that they are courting a terrible disaster: the rise of white identity politics. Murray instead hopes that America can embrace a color-blind liberal individualism recommended by the Reverend Martin Luther King, Jr. in that speech. It’s all so tiresome.
I am sure that Murray is being sincere. But he really couldn’t say anything else if he wanted his book to be published by a mainstream press and reviewed in the mainstream media. The entire cultural and political establishment in America is united in holding that “racism” and identity politics are terrible things — but only when they benefit white people. This spurious moral dogma is what separates us from the rest of the political spectrum. A morally outrageous double standard is the only thing holding back the floodwaters of white identity politics. But the waters continue to rise, as multiculturalism and open borders continue to ravage white societies.
I don’t know if Joe Sobran coined the phrase “courtier journalist,” but I first learned it from him. A courtier is someone who advises or amuses the powerful. Courtier journalists do this with their pens. It is not necessarily ignoble. For decades, I have been reading Patrick Buchanan columns where he cites historical precedents in order to warn the powerful about the consequences of their follies. He speaks as a member of the establishment to fellow members of the establishment. He assumes a common commitment to preserving the system, then argues that his proposals are the only way to do that.
Murray is one of a host of conservative or neoconservative “courtier scholars” who in recent years have argued against identity politics and for race-blind individualism by raising the specter of white identity politics. For instance, see my reviews of Mark Lilla’s The Once and Future Liberal, William Galston’s Antipluralism, and Francis Fukuyama’s Identity (here, here, and here).
It is flattering, I suppose, to be the stalking horse behind which neocons seek to dial American politics back to their preferred policies. As long as we aren’t loved, it is at least nice to be feared.
But the neocon gambit isn’t going to work, for two main reasons. First, the Left isn’t trying to preserve “the system.” They are trying to destroy it. Some of them hate the system even more than they love themselves. Thus they would be content to destroy it even if they suffer. Second, asking non-whites to dial back their identity politics is basically asking them to replace a winning strategy with a losing one. Nobody in his right mind would do that, no matter how nicely conservatives ask, no matter how piously they intone those lines from the Declaration and the “I Have a Dream” speech. Thus, self-respecting whites will be driven to white identity politics out of sheer self-preservation. (For more on this, see part 1 of my essay “White Identity Politics: Inevitable, Necessary, Moral.”)
Yes, it is tiresome to be invoked as a boogeyman. But in the long run, people like Murry, Lilla, Galston, and Fukuyama are doing us a favor. Their color-blind civic nationalism isn’t going to save white America. And as more whites realize this, white identity politics will seem far less scary than being treated like a conquered people in their former homelands. Thanks for the free advertising, Charles.
1. This Weekend’s Livestream
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28 comments
Captain John Charity Spring MA says:
June 26, 2021 at 11:27 am
The oven is now self cleaning and can’t be unlocked. It’s a good time to be alive believe it or not.
Reply
Josephus Cato says:
June 26, 2021 at 12:12 pm
Back in 2015/2016 when I was a “conservative Democrat with libertarian leanings” and very proud of myself for voting for Obama, I found myself very upset with the identity politics of the left. I felt alienated as a white person, the very same white person who felt so good about himself going to pro-illegal immigration assemblies in the Latino part of town and being the only white person there with no one to talk to. Eventually it came to light that one of those nefarious alt-right people, Milo Yiannopoulos was critical of the identity politics. Milo was, at least for me, a sort of seedy back alley entrance to the proverbial libertarian to alt-right pipeline. It felt so intellectually taboo at the time to read Milo. I think that the “alt-lite” acts as a sort of inflection point. I entered this pipeline with a disdain for identity politics but then I saw the light that all politics are identity politics and that white identity politics are simply the mirror image of leftwing identity politics.
I grew up in the 90’s and at least then it seemed like we were well on our way to building a color blind society. There were some bumps on the way like the OJ trial and the Rodney King riots. Eventually this would culminate with Obama winning. Finally, a post racial America. But then the 2016 election came and all the identity politics. “You’re a white male!” Then 2020 came with the George Floyd riot… I mean mostly peaceful protests. Murray’s book sounds good and interesting but as far as building a color-blind society where we see everyone as individuals I think that ship has sailed. As much as I’d like to still believe in the idyllic color blind society, I just can’t unhear Jared Taylor saying,” diversity is a source of conflict and tension.”
Reply
Lord Shang says:
June 26, 2021 at 7:38 pm
White identity is not remotely the moral equivalent to leftist idpol. If millions of whites moved to, say, Honduras, and then started aggressively trying to amass as much morally illegitimate power and politically ill-gotten wealth for our community as possible, one could make that comparison. White identity is a movement of both authentic justice for our people (you know, the paleskins whose ancestors founded, settled and built America, and who have an ethnocollective property right in the nation itself), as well as a post-colorblind movement of racial realism and security. Whites are not trying to take property or liberty from blacks or other minorities; we are trying to prevent them (in league with the white race traitors and socialists called “liberals”) from taking our liberty, rights, property, ability to defend ourselves from criminal predation, and right to unbiased consideration and open competition in school admissions and professional hiring (often now, of course, we’re trying to recover lost rights). Leftist idpol is aggressive; white idpol defensive. They are not “mirror images” at all.
Of course, white nationalists are those wiser ones who not only love their racial heritage and prefer their co-racials as fellow citizens, but who recognize that there will never be colorblindness (not that we want such, either; we want homelands and governments of our own); that whites are a rapidly shrinking population on this overcrowded planet; and that via nonwhite immigration invasions in combination with mendacious white guilt cultural propaganda (which affects both whites and nonwhites, weakening the will of the former, and increasing the racial envy and hatred of the latter), we are placing ourselves, but especially, future generations of whites, in a condition of both ever lowering cultural quality, and ever increasing racial-personal insecurity and physical vulnerability. We are the ones who take intergenerational ethics and good ‘ancestorship’ seriously.
Reply
Desert Flower says:
July 2, 2021 at 3:59 am
“Whites are not trying to take property or liberty from blacks or other minorities; we are trying to prevent them…”
Exactly.
Excellent post.
Reply
One-yard Sprinter says:
June 26, 2021 at 7:51 pm
In The Bell Curve and since, Charles Murray cites overwhelming evidence about intellectual inequality among races and differences in proclivity to crime, and then tries to inject a dose of anaesthetic into those who might draw the “wrong” conclusions. Here are the facts, he says, but lets not get all fussed about them. So his writing might as well appear on the op-ed page of USA Today.
Reply
Captain John Charity Spring MA says:
June 27, 2021 at 3:43 am
The Obama Primary campaign was a significant element of my drift out of the left. Not that he beat Clinton, but the way that Edwards was annihilated. He was a proxy for white southerners within the Party Limits. All the rightwingers who made fun of him in that intra party scuffle couldn’t see it was a prelude to a general demonisation of whitey across the entire nation. The Party decided at that moment to stop pandering to white southerners in any way at all.
Reply
Josephus Cato says:
June 27, 2021 at 11:16 am
It’s hard to believe that there was once those Clinton Gore ’92 stickers with the Confederate flag, let a lone a strong contingent of white working class conservative Democrats in the south and rust belt. Yeah, they raked Edwards through the coals. Wasn’t it the affair that was brought to light that did him in? If so, to be fair, that’s a pretty hard thing to survive in politics.
Reply
Captain John Charity Spring MA says:
June 27, 2021 at 12:01 pm
Not so much the individual politician but the manner in which the Party, Divided. Edwards became a sort of annihilated white man. A sort of symbol. At least in retrospect the party turned its back from supporting pandering to white people.
Reply
Lord Shang says:
June 27, 2021 at 4:00 pm
Edwards was a race traitor, as well as a crook (and, less importantly, an adulterer). No tears for that creep. It’s a good thing that the Democrats are now all but openly attacking whites for being whites. We need more polarization. The greatest enemies of white America are never the Malcolm Xs or Al Sharptons, but the MLKs and Obamas. That blacks and Democrats are moving away from King and Obama towards open black supremacism is a wonderful development. Our goal is not stability per se, but a life of peace, dignity and honor for our people, which will only occur with national partition and racial separation (at least for those whites who want it). Greater instability forces people to get off the fence and choose sides, which is the only way partition will happen. “Evolutionarily”, working within and maintaining the present corrupt system, whites will never win their racial freedom.
Reply
maxsnafu says:
June 28, 2021 at 3:13 am
Absolutely correct.
Reply
Captain John Charity Spring MA says:
June 28, 2021 at 11:45 am
I don’t disagree about Edwards as a man but the primary was a sign of extraordinary things to come.
Reply
Ruston says:
June 26, 2021 at 3:13 pm
Greg, I thought it was agreed that I would write an article on this book.
Reply
Greg Johnson says:
June 27, 2021 at 1:57 am
Nothing is stopping you.
Reply
Texas Chainsaw Makeover says:
June 26, 2021 at 3:36 pm
Jared Taylor put it best when he said Murray and his ilk expect whites to sit around quietly hoping the others give up their identity politics and our benevolent elites usher in the post racial utopia.
Tucker Carlson in his book says when whites embrace their identity, that is the end of America.
The one good thing about Murray’s book is his use of the terms African and European as surrogates for blacks and whites in America.
Somewhat related, you can always bet on neocons to invoke the sainted MLK in a discussion of race. I’d like just one of these people for once to do so but instead phrase it something like this ‘you know, that serial philanderer and commie rape accomplice was right about that content of character business ‘.
Reply
Desert Flower says:
June 27, 2021 at 8:08 am
Martin Luther King was a man of character. Bad character.
Reply
John says:
June 27, 2021 at 5:15 pm
“Tucker Carlson in his book says when whites embrace their identity, that is the end of America.” On the contrary, to restore, to reclaim, to ensure, to take back America as a first world nation is to enshrine the 14 words in our Constitution. “The Slow Cleanse aka Restoring White Homelands” by Greg Johnson.
Reply
Whitespace says:
June 27, 2021 at 11:43 pm
Texas Chainsaw Makeover: “Tucker Carlson in his book says when whites embrace their identity, that is the end of America.”
Tucker Carlson is basically right, even though he comes from the perspective of a civic nationalist. Whites are by and large culturally tolerant. Once that equilibrium has been disrupted to the point that white identity becomes mainstream, the established order is unsustainable.
In Canada’s case, and I speak as some western hick, white identity will only emerge in the wake of an even more severe disruption. I believe the process of effacing the Anglo-European character of Canada will be much more advanced here. Nevertheless, Tucker would be right. By the time whites embrace their identity in Canada, the nation is over. Our PM Justin “Trudeau” Castro was premature in calling Canada a post-national State, but he is not wrong.
Reply
John says:
June 27, 2021 at 12:34 am
Lipton Matthews interviewed Charles Murray.
Around the 41 minute mark Charles Murray says “…they[White Nationalist] can’t report my findings in full in my own words because my findings in full in the full words do not support White Nationalism do not support White supremacy”.
For us Europeans (not unlike any other race) wanting our own homelands is NOT White supremacy. The lying to label us European Nationalists as supremacists is failing. More & more ppl r waking up to these lies & MORE IMPORTANTLY the agenda behind these lies.
Reply
DarkPlato says:
June 27, 2021 at 6:52 am
I’m not finished it yet, a book of this length could take me several weeks, but so far and from the reviews, I see this mainly as a work of propaganda. It’s important for its source, a mainstream academic, and its apologetic stance, which are aimed at winning over Murray’s intellectual set of white humanities and social science professors and Ivy League grads. As for its intellectual content, that’s nothing more than we read at any of these websites every day. This book is written that in hopes a milquetoast academic will read it, then move on to Amren and Ccc, then who knows Counter currents and TOO, and then (oh god) daily stormer and TRS. It’s like gateway drug; pot leads to meth and crack.
Reply
DarkPlato says:
June 27, 2021 at 7:04 am
Being facetious of course.
Reply
Weave says:
June 27, 2021 at 8:04 am
You are not wrong comparing the daily stormer to crack. I hit that site up 6 times a day, minimum. The way AA writes is hilarious.
Reply
DarkPlato says:
June 27, 2021 at 9:03 am
He’s a unique talent.
Reply
Francis XB says:
June 27, 2021 at 10:05 am
“A specter is haunting the Regime —the specter of White Nationalism.”
I like that.
Make it viral!
Reply
John says:
June 27, 2021 at 3:55 pm
POTUS Donald Trump DID NOT deport ALL illegals, DID NOT build the wall, DID NOT cancel birthright citizenship, the 3 minimum items to fix USA’s slide into a 3rd world society. Trump was our last chance to save USA & he did not do what needed to b done at the bare minimum. European flight to Idaho, Montana, Wyoming is just buying us some time (as over 100,000 continue to INVADE every 30 days), it is NOT the long term solution, the long term solution is keeping America a European country as our Founding Fathers intended it in first Naturalization Act of 1790. We r witnessing the unraveling of America in this multiracial mode. Trump’s first rally since leaving office was held Saturday 26 June 2021 in Ohio & he did not demonstrate an understanding of the perilous situation America is in. If the sheeple that attended his rally understood the perilous situation, & shown a Specter of European Nationalism, they would have booed Trump off the stage if he did not demonstrate this understanding w/in the first 30 minutes. Donald J. Trump IS A PRESSURE RELIEF VALVE, A WAY FOR US TO VENT OUR FRUSTRATION MEANWHILE THE TRAJECTORY OF OUR REPLACEMENT IS UNAFFECTED, PRETEND FIXING THE PROBLEM WHEN IN FACT THE FIX IS IN. THE SHEEPLE ARE TAKEN FOR A RIDE. THE ULTIMATE BETRAYAL.
Reply
WWWM says:
June 29, 2021 at 3:40 am
America is going to perish in a very painful way. Thomas Chittum’s book “Civil War II” is essentially a guideline of what is happening now. America is the ultimate multi-cultural experiment and the entire globe will have a front row seat.
Reply
Nicolas Bourbaki says:
June 28, 2021 at 7:31 am
Very recently I had an online debate in the “politics section” of a hobbyist website. The website is predominately Progressive and the topic was “Is the IQ in the US Declining”.
I mentioned that the average IQ in the US used to be around 100 but has now declined to 97. I quoted Herrnstein & Murray’s “The Bell Curve: Intelligence & Class Structure in American Life”, 1994. Very opinionated Progressives took up the case that the “Bell Curve” had been thoroughly discredited as “pseudo-science” and referenced a 2hr,39min YouTube Video posted by “Shaun”. In it, Shaun says that Herrnstein &Murray made a basic mistake in genetics. They specifically pointed out that they need not concern themselves anymore with the book.
I used an “ad hominem” counter that we could find no info on Shaun & didn’t even know if he finished high school. Herrnstein was Psychology Chair at Harvard & Murray has a lot of research into heredity; thus I doubted they made such a basic mistake.
Nonetheless, for everything put out by sources on our side, there are Videos put out by anonymous YouTubers saying this or that is pseudoscience.
Also wasn’t James Watson, who won the Nobel Award for his work in genetics, stripped of his titles and position for basically saying the same thing as Murray? Watson kept his Nobel award but sold the medal in 2014.
I’m sure there are typos here; I really wish we could edit comments.
Reply
Vehmgericht says:
June 30, 2021 at 12:10 am
Not only ‘Shaun’ of YouTube, but the Guardian, BBC and the entire liberal establishment regard IQ as a nonsense, signifying nothing more than an aptitude for silly puzzles.
Reply
Desert Flower says:
July 2, 2021 at 4:07 am
“Some of them hate the system even more than they love themselves. Thus they would be content to destroy it even if they suffer.”
Chilling.
Reply
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| 834.5 | 36,236 |
https://counter-currents.com/2021/06/charles-murray-and-the-specter-of-white-nationalism/?replytocom=1739329
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counter-currents.com
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2,608,097 |
Rooted in Trust is a pandemic information response plan countering the unprecedented scale and speed of the spread of rumors and misinformation about COVID-19. The plan is global in reach and grounded in hyper-local information ecosystems in the Philippines, Afghanistan, Lebanon, Mali, Sudan, the Central African Republic and Colombia to ensure impact on the global level and on the local level directly in vulnerable communities affected by humanitarian crises.
Ushahidi Inc
Ushahidi, which translates to “testimony” in Swahili, was developed to map reports of violence in Kenya after the post-election violence in 2008. Since then, thousands have used our crowdsourcing tools to raise their voice. We’re a technology leader in Africa, headquartered in Nairobi, with a global team. We are a social enterprise that provides software and services to numerous sectors and civil society to help improve the bottom up flow of information.
We believe that if marginalized people are able to easily communicate to those who aim to serve them...
rbutr
rbutr tells you when the webpage you are viewing has been disputed,
rebutted or contradicted elsewhere on the internet.
rbutr is a community-driven app which connects webpages together on the basis that one page argues against the other.
Visit a rebutted page and you will be told "There are rebuttals to this page." You can then open up the rebutting article(s).
Found a great rebuttal? Connect it to the page(s) it rebuts so that people reading those articles can know about it.
[INACTIVE] Quod
Quod -currently in its beta form- is a project to create an open database of misinformation on the web. It was created to crowdsource the difficult task of identifying and cataloging misinformation published online. You can participate by reporting misinformation you encounter online.
(Copied from website)
MozCheck
MozCheck is the first fact checking organization in Mozambique. Since December 2017, the team of volunteers has published verifications of electoral promises of the President of the Republic on social networks (Facebook and Twitter). In the coming months and years, MozCheck intends to check electoral promises of more Mozambican politicians from different party affiliations and levels of action. It also intends to develop other projects in the area of fact checking.
Real 411
Real411 was created to give all citizens the power to report digital disinformation during the run up to the 2019 South African National and Provincial Elections. When individuals report to the Real411, they are reporting to the Electoral Commission’s (IEC) Directorate for Electoral Offences. The IEC will rule on acts of disinformation for appropriate action to be taken.
Dirt Protocol
DIRT is a protocol for decentralized information curation that uses token staking to incentivize honesty. DIRT is not a Token Curated Registry (TCR). DIRT is a protocol to build TCRs.
Digital Forensic Research Lab
Catalyzing a global network of digital forensic researchers, following conflicts in real time. Mission: To identify, expose, and explain disinformation where and when it occurs using open source research; to promote objective truth as a foundation of government for and by people; to protect democratic institutions and norms from those who would seek to undermine them in the digital engagement space. To create a new model of expertise adapted for impact and real-world results.
Digital Democracy Monitor
The Digital Democracy Monitor Toolkit seeks to empower researchers with the knowledge, tools and examples to analyse democratic discourse online.
This toolkit was prepared by Democracy Reporting International (DRI) as part of our efforts on Social Media and Democracy.
BellingCat Online Investigation Toolkit
Welcome to Bellingcat’s freely available online open source investigation toolkit. You can follow our work via our website, Twitter and Facebook. (We also provide three to five day open source investigation workshops.) This is version 5.3 (August 19, 2020). The list includes satellite and mapping services, tools for verifying photos and videos, websites to archive web pages, and much more. The list is long, and may seem daunting. There are guides at the end of the document, highlighting the methods and use of these tools in further detail.
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Team up with your Husqvarna Garden Tractor and its attachments. It’s capable of so much more than just lawn mowing. With a wide range of specifically designed attachments it can become a versatile partner in the garden all year round.
Attachment - Rear-mounted
With Husqvarna’s rear-mounted attachments, including collectors, trailers, counter weights, moss rakes, aerators and spreaders, you can easily adapt your Rider to the season and situation.
FROM
$ 1,679.00
Rec. retail price incl GST
More Details
Sweeper
Robust sweeper with high capacity collection. Quick and tool-less assembly. Collapses for easy storage and adjustable brush height. Working width - 112cm. Collector Volume 705L
FROM
$ 1,679.00
Rec. retail price incl GST
More Details
FROM
$ 1,049.00
Rec. retail price incl GST
More Details
Spreader - Large
This handy spreader attaches to your ride-on mower and is ideal for spreading fertilser and seed in larger gardens. Comes with easily adjustable flow, enclosed commercial-grade gearbox for smooth rolling and easy maintenance and large pneumatic tyres for easy manoeuvrability. Durable nylon cover included. Load Capacity - 60kg. Spread width - 3.0 - 3.6m
FROM
$ 1,049.00
Rec. retail price incl GST
More Details
FROM
$ 451.00
Rec. retail price incl GST
More Details
Spreader - Small
This handy spreader attaches to your ride-on mower and is ideal for spreading fertilser and seed in your garden. Comes with easy-flow adjustment suitable for smaller areas. Load Capacity - 38kg. Spread width 1.2 - 3.0m
FROM
$ 451.00
Rec. retail price incl GST
More Details
Attachments
FROM
$ 19.00
Rec. retail price incl GST
More Details
Battery Status Indicator
A practical quick connector for those with batteries that are difficult to access or are hidden. The eyelet terminals are fitted to the battery, giving effortless connection without the need of actually reaching the battery. Fits Husqvarna's Battery charger XS 800 (new connection) and BC 0.8.
FROM
$ 19.00
Rec. retail price incl GST
More Details
FROM
$ 109.00
Rec. retail price incl GST
More Details
Battery Charger BC 0.8 12V / 0.8
For all 12V lead-acid batteries from 1.2-32Ah. The charger is suitable for maintenance charging up to 100Ah. Perfect for seasonal vehicles. Fully automatic for optimal charging and maintenance charging that maximises battery life and performance. Reverse-polarity protected and water-resistant (IP 65) for outdoor use. Ideal for use with Battery status indicator.
FROM
$ 109.00
Rec. retail price incl GST
More Details
FROM
$ 209.00
Rec. retail price incl GST
More Details
Battery Charger XS Lithium
The Lithium XS battery charger is an advanced microprocessor controlled charger and maintainer. With up to 5A of charge current available, it can charge batteries from 5Ah to 60Ah and maintain up to 120Ah. An easy to use charger with 8 charging steps, a testing sequence that tells you if the battery can take and retain the charge, a unique maximisation step to restore full capacity and patented maintenance charging for maximum performance.
FROM
$ 209.00
Rec. retail price incl GST
More Details
FROM
$ 239.00
Rec. retail price incl GST
More Details
Deflector
For tractors with integrated collection. Convert the tractor from collection to rear ejection. With the deflector, the cuttings are directed backwards and downwards. Fits all collecting tractors CTH from model year 2013.
FROM
$ 239.00
Rec. retail price incl GST
More Details
FROM
$ 79.00
Rec. retail price incl GST
More Details
Mulching Plug
Convert your Lawn Tractor to mulching - effectively providing nutrients to the lawn, reducing evaporation and keeping the soil temperature cooler. Prevents objects from being thrown.
FROM
$ 79.00
Rec. retail price incl GST
More Details
FROM
$ 119.00
Rec. retail price incl GST
More Details
Mulching Kit
Convert your ZTR® Zero Turn or Lawn Tractor to mulching - effectively providing nutrients to the lawn, reducing evaporation and keeping the soil temperature cooler. Prevents objects from being thrown.
FROM
$ 119.00
Rec. retail price incl GST
More Details
FROM
$ 159.00
Rec. retail price incl GST
More Details
Tractor Cover
Fits most Husqvarna tractors. All season protection for lawn and garden tractors. Reinforced vent allows air circulation and helps prevent ballooning in winds. Adjustable top and elastic cord in bottom hem for a quick and custom fit. Reinforced handle provides comfort when installing and removing cover. Heavy duty fabric. Handy storage bag. Fits tractors with decks up to 54".
FROM
$ 159.00
Rec. retail price incl GST
More Details
FROM
$ 399.00
Rec. retail price incl GST
More Details
Loading Ramps
Curved loading ramps (set of 2) ideal for loading tractor and ZTR® mowers. Corrosion-resistant aluminium with an efficient non-slip surface. Suits trailers with a height up to 700mm and a max load of 450kg. 2000 x 200 x 50mm
FROM
$ 399.00
Rec. retail price incl GST
More Details
FROM
$ 139.00
Rec. retail price incl GST
More Details
Large Ride-on Cover
Large Rider/Lawn Tractor Cover. Made in water resistant nylon. Adjustable drawstring at the lower part, ventilation holes. Fits collecting tractors and riders.
FROM
$ 139.00
Rec. retail price incl GST
More Details
FROM
$ 85.00
Rec. retail price incl GST
More Details
Seat Cover
Protects new seats and renews old seats. Cushion quilting for added comfort. 2 rear storage pockets for tools and gear. Reflective piping for extra visibility. Elastic shock cord bottom for a quick custom fit. Fits seats with backrests up to 15" tall. No provision for armrests.
FROM
$ 85.00
Rec. retail price incl GST
More Details
Lubricants & Filling Equipment
By lubricating your Rider and using the right kind of fuel it will not only last longer, it will help you work optimally. We have a selection of lubricants and cans for easier work and better results.
FROM
$ 64.95
Rec. retail price incl GST
More Details
Fuel Can 15L
15L Fuel Can for wheeled machines featuring three ergonomically positioned handles and specially designed spout. Easy to lift, carry and refuel, without any splash or waste.
FROM
$ 64.95
Rec. retail price incl GST
More Details
FROM
$ 37.95
Rec. retail price incl GST
More Details
Fuel Spout - Fuel Can 15L
Fuel spout with optimised flow and autostop function for use with Fuel Can 15L.
FROM
$ 37.95
Rec. retail price incl GST
More Details
FROM
$ 13.95
Rec. retail price incl GST
More Details
10W/30 4-Stroke Oil
A premium multi-grade engine oil specially designed for use with 4-stroke, air cooled engines. Specification: SAE10W30, API SM (Petrol) API CF-4 (Diesel)
FROM
$ 13.95
Rec. retail price incl GST
More Details
Personal Protective Equipment
Your body is your most important tool. We offer a wide range of protective products such as footwear, glasses, gloves and hearing protection.
FROM
$ 36.95
Rec. retail price incl GST
More Details
Garden Earmuffs
Garden earmuffs developed for garden enthusiasts. Light, comfortable with an extra wide headband for secure fit. Certified to EN 352-1, SNR 25dB(A) Class 4
FROM
$ 36.95
Rec. retail price incl GST
More Details
FROM
$ 16.95
Rec. retail price incl GST
More Details
Ear Plugs
Disposable foam ear plugs in a tapered shape for comfort. Convenient pack size of 25 pairs. 32dB(A) NRR
FROM
$ 16.95
Rec. retail price incl GST
More Details
FROM
$ 6.95
Rec. retail price incl GST
More Details
Ear Plugs - Corded
Corded disposable foam ear plugs in a tapered shape for comfort. Convenient pack size of 3. 32dB(A) NRR
FROM
$ 6.95
Rec. retail price incl GST
More Details
FROM
$ 41.95
Rec. retail price incl GST
More Details
Gloves, Technical light
A tight and comfortable glove made in reverse goat leather with ultra-soft brushed suede. Goat leather has good protection against moisture since it contains natural fat and is therefore suitable for demanding work. Laminated spandex fabric back with reflective piping on the back and a wrist in neoprene with a velcro closure. The thumb has a terry cloth fabric for wiping nose and face.
FROM
$ 41.95
Rec. retail price incl GST
More Details
FROM
$ 34.95
Rec. retail price incl GST
More Details
Gloves, Functional
Featuring goat leather double palm with foam laminated spandex on the rear and nylon neoprene knuckles, all providing added comfort and durability during long use. Knitted wrist and a velcro closure makes it easy to put on. Goat leather has a good protection against moisture since it contains natural fat and is therefore suitable for demanding work.
FROM
$ 34.95
Rec. retail price incl GST
More Details
FROM
$ 18.95
Rec. retail price incl GST
More Details
Gloves, Classic Light
A slim fit multi-use glove providing all-round protection and comfort. Goat leather palm with jersey fabric on the rear. Goat leather provides good protection against moisture since it contains natural fat and is therefore suitable for demanding work.
FROM
$ 18.95
Rec. retail price incl GST
More Details
FROM
$ 19.95
Rec. retail price incl GST
More Details
Protective Glasses - Yellow X
Protective glasses feature scratch resistant yellow tinted lenses for better vision in dark conditions. Extendable frames and angle adjustable lenses. Certified to EN 166, ANSI Z87+
FROM
$ 19.95
Rec. retail price incl GST
More Details
FROM
$ 19.95
Rec. retail price incl GST
More Details
Protective Glasses - Clear X
Protective glasses feature scratch resistant clear lenses with extendable frames and angle adjustable lenses. Certified to EN 166, ANSI Z87+
FROM
$ 19.95
Rec. retail price incl GST
More Details
Other products that we stock
FROM
$ 36.95
Rec. retail price incl GST
More Details
Garden Earmuffs
Garden earmuffs developed for garden enthusiasts. Light, comfortable with an extra wide headband for secure fit. Certified to EN 352-1, SNR 25dB(A) Class 4
FROM
$ 36.95
Rec. retail price incl GST
More Details
FROM
$ 16.95
Rec. retail price incl GST
More Details
Ear Plugs
Disposable foam ear plugs in a tapered shape for comfort. Convenient pack size of 25 pairs. 32dB(A) NRR
FROM
$ 16.95
Rec. retail price incl GST
More Details
FROM
$ 6.95
Rec. retail price incl GST
More Details
Ear Plugs - Corded
Corded disposable foam ear plugs in a tapered shape for comfort. Convenient pack size of 3. 32dB(A) NRR
FROM
$ 6.95
Rec. retail price incl GST
More Details
FROM
$ 41.95
Rec. retail price incl GST
More Details
Gloves, Technical light
A tight and comfortable glove made in reverse goat leather with ultra-soft brushed suede. Goat leather has good protection against moisture since it contains natural fat and is therefore suitable for demanding work. Laminated spandex fabric back with reflective piping on the back and a wrist in neoprene with a velcro closure. The thumb has a terry cloth fabric for wiping nose and face.
FROM
$ 41.95
Rec. retail price incl GST
More Details
FROM
$ 34.95
Rec. retail price incl GST
More Details
Gloves, Functional
Featuring goat leather double palm with foam laminated spandex on the rear and nylon neoprene knuckles, all providing added comfort and durability during long use. Knitted wrist and a velcro closure makes it easy to put on. Goat leather has a good protection against moisture since it contains natural fat and is therefore suitable for demanding work.
FROM
$ 34.95
Rec. retail price incl GST
More Details
FROM
$ 18.95
Rec. retail price incl GST
More Details
Gloves, Classic Light
A slim fit multi-use glove providing all-round protection and comfort. Goat leather palm with jersey fabric on the rear. Goat leather provides good protection against moisture since it contains natural fat and is therefore suitable for demanding work.
FROM
$ 18.95
Rec. retail price incl GST
More Details
FROM
$ 64.95
Rec. retail price incl GST
More Details
Fuel Can 15L
15L Fuel Can for wheeled machines featuring three ergonomically positioned handles and specially designed spout. Easy to lift, carry and refuel, without any splash or waste.
FROM
$ 64.95
Rec. retail price incl GST
More Details
FROM
$ 37.95
Rec. retail price incl GST
More Details
Fuel Spout - Fuel Can 15L
Fuel spout with optimised flow and autostop function for use with Fuel Can 15L.
FROM
$ 37.95
Rec. retail price incl GST
More Details
FROM
$ 13.95
Rec. retail price incl GST
More Details
10W/30 4-Stroke Oil
A premium multi-grade engine oil specially designed for use with 4-stroke, air cooled engines. Specification: SAE10W30, API SM (Petrol) API CF-4 (Diesel)
FROM
$ 13.95
Rec. retail price incl GST
More Details
FROM
$ 19.95
Rec. retail price incl GST
More Details
Protective Glasses - Yellow X
Protective glasses feature scratch resistant yellow tinted lenses for better vision in dark conditions. Extendable frames and angle adjustable lenses. Certified to EN 166, ANSI Z87+
FROM
$ 19.95
Rec. retail price incl GST
More Details
FROM
$ 19.95
Rec. retail price incl GST
More Details
Protective Glasses - Clear X
Protective glasses feature scratch resistant clear lenses with extendable frames and angle adjustable lenses. Certified to EN 166, ANSI Z87+
FROM
$ 19.95
Rec. retail price incl GST
More Details
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Stocked Brands
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Countrywide Power Equipment
Countrywide Power Equipment is a dedicated Husqvarna Dealer and a major supplier of Husqvarna products in the Avon Valley and Central Wheatbelt area. We offer a complete sales, service, repair and spare parts business and specialise in meeting the needs of both domestic and commercial customers. We also supply a wide range of pumps and generators and a number of other small engines, so whether it’s a fire fighter pump, water pump or a generator we can supply.
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| 2,222.5 | 13,693 |
https://countrywidepowerequipment.com.au/accessories/tractor-accessories
|
countrywidepowerequipment.com.au
| 1.000292 |
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Vegas has a holiday offer which you can save 50% at Vegas. You can also save 30% off hotels at Lopesan, save 25% off holiday stay at Danubius or save 25% on food, beverage and laundry at Tridenthotels.
About vegas.com
Vegas is a website that helps customers to find their best-suited hotel for their clients when going on holiday in Las Vegas. It also provides shows, tours, wedding and different clubs like golf, swimming and bars. From day to day deal at $12.95 to trending from $64.95, you can enjoy staying a luxurious hotel around Vegas without worrying about the price because it offers a wide range and affordable too.
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Southern Cross University acknowledges and pays respect to the ancestors, Elders and descendants of the Lands upon which we meet and study.
We are mindful that within and without the buildings, these Lands always were and always will be Aboriginal Land.
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Parkland is a track from the recent Lee Harvey Osmond cd (available for your enjoyment on this very website – listen for free, buy for cheap). It is also a song off of the much-rumoured-about-album The Kennedy Suite, which we have been working on (and off) for the past 18 months. The album is a suite of songs about the JFK assassination, written by our friend Scott Garbe. Each song in the suite is from the perspective of a historical or fictional character involved, in some manner, with the events of that tragic day. Its a brilliant collection of songs and the album will feature performances by several Canadian artists. We hope to have it out in 2010. In the meantime here is a video that someone made for the song Parkland, which is a song written from the perspective of an orderly working at Parkland hospital that day. Enjoy.
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Here is a slide show with soundtrack that Jeff put together of our latest European adventure. It sums it all up rather nicely….
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We allow third-party advertising companies to collect certain information when you visit our site. These companies may utilize cookies, pixels or other technologies to collect and use non-personally identifiable information (e.g., hashed data, browser type, time and date, subject of advertisements clicked or scrolled over) during your visits to this and other websites in order to provide advertisements about goods and services likely to be of greater interest to you (i.e. interest-based advertising). They may also use persistent identifiers to anonymously track your Internet usage over time and across other websites in their networks beyond our site. For example, if you visit one of our tour pages, we may later serve you a targeted ad for that particular tour as you browse the internet. If you visit our contact us page and fill out the form, we will collect personal information – specifically your name and email address. This website does not respond to “Do Not Track” signals sent from your browser or device. Any material changes to this notice will be posted to this website.
To learn more about interest-based advertisings or to opt-out of this type of advertising, you can visit the websites of the Digital Advertising Alliance at www.aboutads.info and Networking Advertising Initiative at www.networkadvertising.org/choices.
Carolina Polo & Carriage Tour Company
Phone Click here to call (844) 616-0399 (844) 616-0399 Phone Click here to call (843) 577-6767 (843) 577-6767 Envelope Click here to email [email protected] [email protected]
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Urge Congress to fully fund global childhood immunizations. Every 20 seconds, a child dies from vaccine-preventable disease, resulting in 1.5 million deaths every year, and accounting for nearly a third of all childhood deaths. Vaccines are one of the safest, most cost-effective ways to save children’s lives, improve healthcare systems, and reach a goal of long-term economic stability.
Funding the Centers for Disease Control and Prevention (CDC)’s polio eradication and measles elimination programs through the Health and Human Services appropriations bill, and funding the US Agency for International Development (USAID), UNICEF, the World Health Organization, and the Global Alliance for Vaccines and Immunization (Gavi, the Vaccine Alliance) programs through the State Department/Foreign Operations appropriations bill will deliver childhood immunizations and save millions of lives. These efforts also improve global disease detection systems that enable other nations to effectively detect and efficiently respond to a range of other diseases. Stopping diseases at their source is the most cost-effective approach, saves the most lives, and makes the world more resilient to new viruses that could cause the next pandemic.
We must not let COVID-19 jeopardize decades of progress in global childhood immunization. We know immunization saves lives. UNICEF procures vaccines that reach almost half of the world’s children. The World Health Organization coordinates emergency response. USAID focuses on economic development and especially programs for women and girls. There is an extensive international collaboration and coordination among United Nations agencies such as UNICEF, multilateral partners like Gavi, the Vaccine Alliance, and the countries themselves. Federal funding from Congress is critical.
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Reauthorize the Violence Against Women Act (VAWA)
Time to reauthorize the Violence Against Women Act, devoting comprehensive resources to local communities by enhancing and expanding prevention programs and services to survivors of domestic violence, sexual assault, dating violence, and stalking.
Family Violence Prevention and Services Act Reauthorization
Domestic violence programs nationwide need critical funding and technical support. Hotlines and shelters must be available and properly staffed to meet the needs of families. This is an important time to invest heavily in family violence prevention.
Service Dogs for Veterans
Service dogs trained to support veterans helped significantly reduce PTSD symptoms according to a recent study by the Department of Veterans Affairs. Time to establish a pilot program giving veterans the opportunity to train and adopt service dogs.
Protect Funding for Victims of Crime
Urge Senators to enact H.R. 1652, the Victims of Crime Act (VOCA) bill to increase deposits and restore critical funding to the Crime Victims Fund which provides grants to state and local programs that assist victims of crime.
Fund Global Vaccination Efforts
Urge Congress to fully fund immunization programs in FY 2022. The United States plays a vital role in the lives of children around the world. Support the CDC’s polio and measles programs, and funding for global childhood immunizations.
Increase Funds to Prevent Child Abuse
It is time for Congress to reauthorize the bipartisan Child Abuse Prevention and Treatment Act programs, and appropriate $750 million funding for Community-Based Child Abuse Prevention grants.
Equal Pay for Equal Work
Full time working women make $.82 compared to every $1.00 a man makes. The wage gap persists in nearly every line of work, regardless of education, experience, occupation, industry, or job title, and is even larger for women of color.
Time for Women to have Equal Rights
It’s time for women to have equal rights under the law! Urge Congress to remove the deadline for ratification of the Equal Rights Amendment (ERA) to the U.S. Constitution and reaffirm congressional support for women’s equality.
End Campus Sexual Assault
Victims of sexual assault require specialized medical treatment and counseling services. Campus sexual assault survivors suffer high rates of PTSD, depression, and drug or alcohol abuse which can hamper both the ability to succeed in school and future employment. Educational equity for women and girls requires fair, responsive, fully developed campus sexual assault policies, knowledgeable administrators, and ultimately, an end to sexual violence on college campuses.
Reauthorize the Violence Against Women Act
It’s time to reauthorize the Violence Against Women Act, H.R. 1620, devoting resources to local communities by enhancing and expanding prevention programs and services to survivors of domestic violence, sexual assault, dating violence, and stalking.
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Nickelodeon Rainbow Slime by Cra-Z-Art! Get this ooey, gooey, and colorful rainbow slime today! All you need to do is stretch it, squeeze it, and ooze it for hours of endless fun! Super colorful, pre-made, perfect consistency slime is packaged in a plastic jar for convenient storage. Colors come stacked in container, mix them all together and see the great color combinations! Great for at home slime fun, birthday parties and favors, to take on the go, little gifts, and more. Slime is soft to stretch and mold without tearing. Keep children engaged and let them express their creativity and imagination with Rainbow Slime. Can be used for fun at home or for slime-tastic parties! Safe and nontoxic. For ages 6 and up. It's Slime Time!
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Crafting is fun for all ages and interests. This blog evolved from researching resources for friends and articles I write as a freelance writer. It is a place to share the information I discovered. Please feel free to leave comments and questions. I am always looking at new projects ideas for family and friends in areas of my own abilities and theirs. As a notice to my readers this blog does contain Amazon links and Google Adsense. As an Amazon Associate I earn from qualifying purchases.
Thursday, January 27, 2011
Skipper Party Dress
This picture does not quite do the dress justice. It is much fuller and prettier in person. This is another dress I adapted from Annie's Attic Fashion Doll Little Sister, Pattern Number 87D22 published 1986. This pattern used fingering yarn and it was so much fun working on the skirt. The formation of the skirt creates the illusion of an underskirt. The pattern creates the style with layers of crochet skirt instead of an actual underskirt. This was a new style of skirt pattern for me. I am anxious to make a few in different colors. The top of the dress has been simplified. I also added an elastic waist as I explained in previous blog posts. I also experimented with using buttons instead of snaps for closures in the back this time.
It has been fun experimenting with the Skipper patterns. With these patterns, I hope to be able to adapt them and make more styles for the doll. I have not found many free Skipper patterns so being able to adapt the few vintage books that are available is an advantage. For those that are interested, I have been using a three- inch elastic for her waist and crocheting over the waist band before working on the skirt and then the top. Barbie's outfits have used a four-inch elastic sewed together and then crocheted over for her outfits.
Barbie and American Girl Doll Free Patterns are listed on my blog at these locations:
Barbie Crochet and Sewing Page
Barbie Knitting Page
Ken Page
Kelly/Skipper Page
American Girl Doll Crochet and Sewing
American Girl Doll Knitting
Additional Doll Patterns also Available on the Holiday Blogs:
Valentines
Easter
Graduation
Patriotic
Halloween Page
Thanksgiving
Christmas
Pictures may not be used without written permission.
Posted by Sheltie Times at 7:00 AM
Labels: Barbie Crochet, Barbie free crochet patterns, Barbie free knit patterns, Barbie free patterns, Skipper, Skipper Crochet
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This is a guide to extending R, describing the process of creating R add-on packages, writing R documentation, R’s system and foreign language interfaces, and the R API.
This manual is for R, version 4.2.0 Under development (2021-11-24).
Copyright © 1999–2021 R Core Team
Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies.
Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one.
Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
Table of Contents
Acknowledgements
1 Creating R packages
1.1 Package structure
1.1.1 The DESCRIPTION file
1.1.2 Licensing
1.1.3 Package Dependencies
1.1.3.1 Suggested packages
1.1.4 The INDEX file
1.1.5 Package subdirectories
1.1.6 Data in packages
1.1.7 Non-R scripts in packages
1.1.8 Specifying URLs
1.2 Configure and cleanup
1.2.1 Using Makevars
1.2.1.1 OpenMP support
1.2.1.2 Using pthreads
1.2.1.3 Compiling in sub-directories
1.2.2 Configure example
1.2.3 Using F9x code
1.2.4 Using C++ code
1.3 Checking and building packages
1.3.1 Checking packages
1.3.2 Building package tarballs
1.3.3 Building binary packages
1.4 Writing package vignettes
1.4.1 Encodings and vignettes
1.4.2 Non-Sweave vignettes
1.5 Package namespaces
1.5.1 Specifying imports and exports
1.5.2 Registering S3 methods
1.5.3 Load hooks
1.5.4 useDynLib
1.5.5 An example
1.5.6 Namespaces with S4 classes and methods
1.6 Writing portable packages
1.6.1 PDF size
1.6.2 Check timing
1.6.3 Encoding issues
1.6.4 Portable C and C++ code
1.6.4.1 Common symbols
1.6.5 Binary distribution
1.7 Diagnostic messages
1.8 Internationalization
1.8.1 C-level messages
1.8.2 R messages
1.8.3 Preparing translations
1.9 CITATION files
1.10 Package types
1.10.1 Frontend
1.11 Services
2 Writing R documentation files
2.1 Rd format
2.1.1 Documenting functions
2.1.2 Documenting data sets
2.1.3 Documenting S4 classes and methods
2.1.4 Documenting packages
2.2 Sectioning
2.3 Marking text
2.4 Lists and tables
2.5 Cross-references
2.6 Mathematics
2.7 Figures
2.8 Insertions
2.9 Indices
2.10 Platform-specific documentation
2.11 Conditional text
2.12 Dynamic pages
2.13 User-defined macros
2.14 Encoding
2.15 Processing documentation files
2.16 Editing Rd files
3 Tidying and profiling R code
3.1 Tidying R code
3.2 Profiling R code for speed
3.3 Profiling R code for memory use
3.3.1 Memory statistics from Rprof
3.3.2 Tracking memory allocations
3.3.3 Tracing copies of an object
3.4 Profiling compiled code
3.4.1 Linux
3.4.1.1 sprof
3.4.1.2 oprofile and operf
3.4.2 Solaris
3.4.3 macOS
4 Debugging
4.1 Browsing
4.2 Debugging R code
4.3 Checking memory access
4.3.1 Using gctorture
4.3.2 Using valgrind
4.3.3 Using the Address Sanitizer
4.3.3.1 Using the Leak Sanitizer
4.3.4 Using the Undefined Behaviour Sanitizer
4.3.5 Other analyses with ‘clang’
4.3.6 Other analyses with ‘gcc’
4.3.7 Using ‘Dr. Memory’
4.3.8 Fortran array bounds checking
4.4 Debugging compiled code
4.4.1 Finding entry points in dynamically loaded code
4.4.2 Inspecting R objects when debugging
4.4.3 Debugging on macOS
4.5 Using Link-time Optimization
5 System and foreign language interfaces
5.1 Operating system access
5.2 Interface functions .C and .Fortran
5.3 dyn.load and dyn.unload
5.4 Registering native routines
5.4.1 Speed considerations
5.4.2 Example: converting a package to use registration
5.4.3 Linking to native routines in other packages
5.5 Creating shared objects
5.6 Interfacing C++ code
5.6.1 External C++ code
5.7 Fortran I/O
5.8 Linking to other packages
5.8.1 Unix-alikes
5.8.2 Windows
5.9 Handling R objects in C
5.9.1 Handling the effects of garbage collection
5.9.2 Allocating storage
5.9.3 Details of R types
5.9.4 Attributes
5.9.5 Classes
5.9.6 Handling lists
5.9.7 Handling character data
5.9.8 Finding and setting variables
5.9.9 Some convenience functions
5.9.9.1 Semi-internal convenience functions
5.9.10 Named objects and copying
5.10 Interface functions .Call and .External
5.10.1 Calling .Call
5.10.2 Calling .External
5.10.3 Missing and special values
5.11 Evaluating R expressions from C
5.11.1 Zero-finding
5.11.2 Calculating numerical derivatives
5.12 Parsing R code from C
5.12.1 Accessing source references
5.13 External pointers and weak references
5.13.1 An example
5.14 Vector accessor functions
5.15 Character encoding issues
6 The R API: entry points for C code
6.1 Memory allocation
6.1.1 Transient storage allocation
6.1.2 User-controlled memory
6.2 Error signaling
6.2.1 Error signaling from Fortran
6.3 Random number generation
6.4 Missing and IEEE special values
6.5 Printing
6.5.1 Printing from Fortran
6.6 Calling C from Fortran and vice versa
6.6.1 Fortran character strings
6.6.2 Fortran LOGICAL
6.6.3 Passing functions
6.7 Numerical analysis subroutines
6.7.1 Distribution functions
6.7.2 Mathematical functions
6.7.3 Numerical Utilities
6.7.4 Mathematical constants
6.8 Optimization
6.9 Integration
6.10 Utility functions
6.11 Re-encoding
6.12 Condition handling and cleanup code
6.13 Allowing interrupts
6.14 Platform and version information
6.15 Inlining C functions
6.16 Controlling visibility
6.17 Using these functions in your own C code
6.18 Organization of header files
7 Generic functions and methods
7.1 Adding new generics
8 Linking GUIs and other front-ends to R
8.1 Embedding R under Unix-alikes
8.1.1 Compiling against the R library
8.1.2 Setting R callbacks
8.1.3 Registering symbols
8.1.4 Meshing event loops
8.1.5 Threading issues
8.2 Embedding R under Windows
8.2.1 Using (D)COM
8.2.2 Calling R.dll directly
8.2.3 Finding R_HOME
Function and variable index
Concept index
Next: Creating R packages, Previous: Writing R Extensions, Up: Writing R Extensions [Contents][Index]
Acknowledgements
The contributions to early versions of this manual by Saikat DebRoy (who wrote the first draft of a guide to using .Call and .External) and Adrian Trapletti (who provided information on the C++ interface) are gratefully acknowledged.
Next: Writing R documentation files, Previous: Acknowledgements, Up: Writing R Extensions [Contents][Index]
1 Creating R packages
Packages provide a mechanism for loading optional code, data and documentation as needed. The R distribution itself includes about 30 packages.
In the following, we assume that you know the library() command, including its lib.loc argument, and we also assume basic knowledge of the R CMD INSTALL utility. Otherwise, please look at R’s help pages on
?library
?INSTALL
before reading on.
For packages which contain code to be compiled, a computing environment including a number of tools is assumed; the “R Installation and Administration” manual describes what is needed for each OS.
Once a source package is created, it must be installed by the command R CMD INSTALL. See Add-on-packages in R Installation and Administration .
Other types of extensions are supported (but rare): See Package types.
Some notes on terminology complete this introduction. These will help with the reading of this manual, and also in describing concepts accurately when asking for help.
A package is a directory of files which extend R, a source package (the master files of a package), or a tarball containing the files of a source package, or an installed package, the result of running R CMD INSTALL on a source package. On some platforms (notably macOS and Windows) there are also binary packages, a zip file or tarball containing the files of an installed package which can be unpacked rather than installing from sources.
A package is not1 a library. The latter is used in two senses in R documentation.
A directory into which packages are installed, e.g. /usr/lib/R/library: in that sense it is sometimes referred to as a library directory or library tree (since the library is a directory which contains packages as directories, which themselves contain directories).
That used by the operating system, as a shared, dynamic or static library or (especially on Windows) a DLL, where the second L stands for ‘library’. Installed packages may contain compiled code in what is known on Unix-alikes as a shared object and on Windows as a DLL. The concept of a shared library (dynamic library on macOS) as a collection of compiled code to which a package might link is also used, especially for R itself on some platforms. On most platforms these concepts are interchangeable (shared objects and DLLs can both be loaded into the R process and be linked against), but macOS distinguishes between shared objects (extension .so) and dynamic libraries (extension .dylib).
There are a number of well-defined operations on source packages.
The most common is installation which takes a source package and installs it in a library using R CMD INSTALL or install.packages.
Source packages can be built. This involves taking a source directory and creating a tarball ready for distribution, including cleaning it up and creating PDF documentation from any vignettes it may contain. Source packages (and most often tarballs) can be checked, when a test installation is done and tested (including running its examples); also, the contents of the package are tested in various ways for consistency and portability.
Compilation is not a correct term for a package. Installing a source package which contains C, C++ or Fortran code will involve compiling that code. There is also the possibility of ‘byte’ compiling the R code in a package (using the facilities of package compiler): nowadays this is enabled by default for all packages. So compiling a package may come to mean byte-compiling its R code.
It used to be unambiguous to talk about loading an installed package using library(), but since the advent of package namespaces this has been less clear: people now often talk about loading the package’s namespace and then attaching the package so it becomes visible on the search path. Function library performs both steps, but a package’s namespace can be loaded without the package being attached (for example by calls like splines::ns).
The concept of lazy loading of code or data is mentioned at several points. This is part of the installation, always selected for R code but optional for data. When used the R objects of the package are created at installation time and stored in a database in the R directory of the installed package, being loaded into the session at first use. This makes the R session start up faster and use less (virtual) memory. (For technical details, see Lazy loading in R Internals .)
CRAN is a network of WWW sites holding the R distributions and contributed code, especially R packages. Users of R are encouraged to join in the collaborative project and to submit their own packages to CRAN: current instructions are linked from https://CRAN.R-project.org/banner.shtml#submitting.
Package structure
Configure and cleanup
Checking and building packages
Writing package vignettes
Package namespaces
Writing portable packages
Diagnostic messages
Internationalization
CITATION files
Package types
Services
Next: Configure and cleanup, Previous: Creating R packages, Up: Creating R packages [Contents][Index]
1.1 Package structure
The sources of an R package consist of a subdirectory containing the files DESCRIPTION and NAMESPACE, and the subdirectories R, data, demo, exec, inst, man, po, src, tests, tools and vignettes (some of which can be missing, but which should not be empty). The package subdirectory may also contain files INDEX, configure, cleanup, LICENSE, LICENCE and NEWS. Other files such as INSTALL (for non-standard installation instructions), README/README.md2, or ChangeLog will be ignored by R, but may be useful to end users. The utility R CMD build may add files in a build directory (but this should not be used for other purposes).
Except where specifically mentioned,3 packages should not contain Unix-style ‘hidden’ files/directories (that is, those whose name starts with a dot).
The DESCRIPTION and INDEX files are described in the subsections below. The NAMESPACE file is described in the section on Package namespaces.
The optional files configure and cleanup are (Bourne) shell scripts which are, respectively, executed before and (if option --clean was given) after installation on Unix-alikes, see Configure and cleanup. The analogues on Windows are configure.win and cleanup.win.
For the conventions for files NEWS and ChangeLog in the GNU project see https://www.gnu.org/prep/standards/standards.html#Documentation.
The package subdirectory should be given the same name as the package. Because some file systems (e.g., those on Windows and by default on macOS) are not case-sensitive, to maintain portability it is strongly recommended that case distinctions not be used to distinguish different packages. For example, if you have a package named foo, do not also create a package named Foo.
To ensure that file names are valid across file systems and supported operating systems, the ASCII control characters as well as the characters ‘"’, ‘*’, ‘:’, ‘/’, ‘<’, ‘>’, ‘?’, ‘\’, and ‘|’ are not allowed in file names. In addition, files with names ‘con’, ‘prn’, ‘aux’, ‘clock$’, ‘nul’, ‘com1’ to ‘com9’, and ‘lpt1’ to ‘lpt9’ after conversion to lower case and stripping possible “extensions” (e.g., ‘lpt5.foo.bar’), are disallowed. Also, file names in the same directory must not differ only by case (see the previous paragraph). In addition, the basenames of ‘.Rd’ files may be used in URLs and so must be ASCII and not contain %. For maximal portability filenames should only contain only ASCII characters not excluded already (that is A-Za-z0-9._!#$%&+,;=@^(){}'[] — we exclude space as many utilities do not accept spaces in file paths): non-English alphabetic characters cannot be guaranteed to be supported in all locales. It would be good practice to avoid the shell metacharacters (){}'[]$~: ~ is also used as part of ‘8.3’ filenames on Windows. In addition, packages are normally distributed as tarballs, and these have a limit on path lengths: for maximal portability 100 bytes.
A source package if possible should not contain binary executable files: they are not portable, and a security risk if they are of the appropriate architecture. R CMD check will warn about them4 unless they are listed (one filepath per line) in a file BinaryFiles at the top level of the package. Note that CRAN will not accept submissions containing binary files even if they are listed.
The R function package.skeleton can help to create the structure for a new package: see its help page for details.
The DESCRIPTION file
Licensing
Package Dependencies
The INDEX file
Package subdirectories
Data in packages
Non-R scripts in packages
Specifying URLs
Next: Licensing, Previous: Package structure, Up: Package structure [Contents][Index]
1.1.1 The DESCRIPTION file
The DESCRIPTION file contains basic information about the package in the following format:
Package: pkgname
Version: 0.5-1
Date: 2015-01-01
Title: My First Collection of Functions
Authors@R: c(person("Joe", "Developer", role = c("aut", "cre"),
email = "[email protected]"),
person("Pat", "Developer", role = "aut"),
person("A.", "User", role = "ctb",
email = "[email protected]"))
Author: Joe Developer [aut, cre],
Pat Developer [aut],
A. User [ctb]
Maintainer: Joe Developer <[email protected]>
Depends: R (>= 3.1.0), nlme
Suggests: MASS
Description: A (one paragraph) description of what
the package does and why it may be useful.
License: GPL (>= 2)
URL: https://www.r-project.org, http://www.another.url
BugReports: https://pkgname.bugtracker.url
The format is that of a version of a ‘Debian Control File’ (see the help for ‘read.dcf’ and https://www.debian.org/doc/debian-policy/ch-controlfields.html: R does not require encoding in UTF-8 and does not support comments starting with ‘#’). Fields start with an ASCII name immediately followed by a colon: the value starts after the colon and a space. Continuation lines (for example, for descriptions longer than one line) start with a space or tab. Field names are case-sensitive: all those used by R are capitalized.
For maximal portability, the DESCRIPTION file should be written entirely in ASCII — if this is not possible it must contain an ‘Encoding’ field (see below).
Several optional fields take logical values: these can be specified as ‘yes’, ‘true’, ‘no’ or ‘false’: capitalized values are also accepted.
The ‘Package’, ‘Version’, ‘License’, ‘Description’, ‘Title’, ‘Author’, and ‘Maintainer’ fields are mandatory, all other fields are optional. Fields ‘Author’ and ‘Maintainer’ can be auto-generated from ‘Authors@R’, and may be omitted if the latter is provided: however if they are not ASCII we recommend that they are provided.
The mandatory ‘Package’ field gives the name of the package. This should contain only (ASCII) letters, numbers and dot, have at least two characters and start with a letter and not end in a dot. If it needs explaining, this should be done in the ‘Description’ field (and not the ‘Title’ field).
The mandatory ‘Version’ field gives the version of the package. This is a sequence of at least two (and usually three) non-negative integers separated by single ‘.’ or ‘-’ characters. The canonical form is as shown in the example, and a version such as ‘0.01’ or ‘0.01.0’ will be handled as if it were ‘0.1-0’. It is not a decimal number, so for example 0.9 < 0.75 since 9 < 75.
The mandatory ‘License’ field is discussed in the next subsection.
The mandatory ‘Title’ field should give a short description of the package. Some package listings may truncate the title to 65 characters. It should use title case (that is, use capitals for the principal words: tools::toTitleCase can help you with this), not use any markup, not have any continuation lines, and not end in a period (unless part of …). Do not repeat the package name: it is often used prefixed by the name. Refer to other packages and external software in single quotes, and to book titles (and similar) in double quotes.
The mandatory ‘Description’ field should give a comprehensive description of what the package does. One can use several (complete) sentences, but only one paragraph. It should be intelligible to all the intended readership (e.g. for a CRAN package to all CRAN users). It is good practice not to start with the package name, ‘This package’ or similar. As with the ‘Title’ field, double quotes should be used for quotations (including titles of books and articles), and single quotes for non-English usage, including names of other packages and external software. This field should also be used for explaining the package name if necessary. URLs should be enclosed in angle brackets, e.g. ‘<https://www.r-project.org>’: see also Specifying URLs.
The mandatory ‘Author’ field describes who wrote the package. It is a plain text field intended for human readers, but not for automatic processing (such as extracting the email addresses of all listed contributors: for that use ‘Authors@R’). Note that all significant contributors must be included: if you wrote an R wrapper for the work of others included in the src directory, you are not the sole (and maybe not even the main) author.
The mandatory ‘Maintainer’ field should give a single name followed by a valid (RFC 2822) email address in angle brackets. It should not end in a period or comma. This field is what is reported by the maintainer function and used by bug.report. For a CRAN package it should be a person, not a mailing list and not a corporate entity: do ensure that it is valid and will remain valid for the lifetime of the package.
Note that the display name (the part before the address in angle brackets) should be enclosed in double quotes if it contains non-alphanumeric characters such as comma or period. (The current standard, RFC 5322, allows periods but RFC 2822 did not.)
Both ‘Author’ and ‘Maintainer’ fields can be omitted if a suitable ‘Authors@R’ field is given. This field can be used to provide a refined and machine-readable description of the package “authors” (in particular specifying their precise roles), via suitable R code. It should create an object of class "person", by either a call to person or a series of calls (one per “author”) concatenated by c(): see the example DESCRIPTION file above. The roles can include ‘"aut"’ (author) for full authors, ‘"cre"’ (creator) for the package maintainer, and ‘"ctb"’ (contributor) for other contributors, ‘"cph"’ (copyright holder, which should be the legal name for an institution or corporate body), among others. See ?person for more information. Note that no role is assumed by default. Auto-generated package citation information takes advantage of this specification. The ‘Author’ and ‘Maintainer’ fields are auto-generated from it if needed when building5 or installing.
An optional ‘Copyright’ field can be used where the copyright holder(s) are not the authors. If necessary, this can refer to an installed file: the convention is to use file inst/COPYRIGHTS.
The optional ‘Date’ field gives the release date of the current version of the package. It is strongly recommended6 to use the ‘yyyy-mm-dd’ format conforming to the ISO 8601 standard.
The ‘Depends’, ‘Imports’, ‘Suggests’, ‘Enhances’, ‘LinkingTo’ and ‘Additional_repositories’ fields are discussed in a later subsection.
Dependencies external to the R system should be listed in the ‘SystemRequirements’ field, possibly amplified in a separate README file. This includes specifying a non-default C++ standard and the need for GNU make.
The ‘URL’ field may give a list of URLs separated by commas or whitespace, for example the homepage of the author or a page where additional material describing the software can be found. These URLs are converted to active hyperlinks in CRAN package listings. See Specifying URLs.
The ‘BugReports’ field may contain a single URL to which bug reports about the package should be submitted. This URL will be used by bug.report instead of sending an email to the maintainer. A browser is opened for a ‘http://’ or ‘https://’ URL. To specify another email address for bug reports, use ‘Contact’ instead: however bug.report will try to extract an email address (preferably from a ‘mailto:’ URL or enclosed in angle brackets) from ‘BugReports’.
Base and recommended packages (i.e., packages contained in the R source distribution or available from CRAN and recommended to be included in every binary distribution of R) have a ‘Priority’ field with value ‘base’ or ‘recommended’, respectively. These priorities must not be used by other packages.
A ‘Collate’ field can be used for controlling the collation order for the R code files in a package when these are processed for package installation. The default is to collate according to the ‘C’ locale. If present, the collate specification must list all R code files in the package (taking possible OS-specific subdirectories into account, see Package subdirectories) as a whitespace separated list of file paths relative to the R subdirectory. Paths containing white space or quotes need to be quoted. An OS-specific collation field (‘Collate.unix’ or ‘Collate.windows’) will be used in preference to ‘Collate’.
The ‘LazyData’ logical field controls whether the R datasets use lazy-loading. A ‘LazyLoad’ field was used in versions prior to 2.14.0, but now is ignored.
The ‘KeepSource’ logical field controls if the package code is sourced using keep.source = TRUE or FALSE: it might be needed exceptionally for a package designed to always be used with keep.source = TRUE.
The ‘ByteCompile’ logical field controls if the package R code is to be byte-compiled on installation: the default is to byte-compile. This can be overridden by installing with flag --no-byte-compile.
The ‘UseLTO’ logical field is used on a Unix-alike to indicate if source code in the package is to be compiled with Link-Time Optimization (see Using Link-time Optimization) if R was installed with --enable-lto (default true) or --enable-lto=R (default false). This can be overridden by by the flags --use-LTO and --no-use-LTO. LTO is said to give most size and performance improvements for large and complex (heavily templated) C++ projects.
The ‘StagedInstall’ logical field controls if package installation is ‘staged’, that is done to a temporary location and moved to the final location when successfully completed. This field was introduced in R 3.6.0 and it true by default: it is considered to be a temporary measure which may be withdrawn in future.
The ‘ZipData’ logical field has been ignored since R 2.13.0.
The ‘Biarch’ logical field is used on Windows to select the INSTALL option --force-biarch for this package.
The ‘BuildVignettes’ logical field can be set to a false value to stop R CMD build from attempting to build the vignettes, as well as preventing7 R CMD check from testing this. This should only be used exceptionally, for example if the PDFs include large figures which are not part of the package sources (and hence only in packages which do not have an Open Source license).
The ‘VignetteBuilder’ field names (in a comma-separated list) packages that provide an engine for building vignettes. These may include the current package, or ones listed in ‘Depends’, ‘Suggests’ or ‘Imports’. The utils package is always implicitly appended. See Non-Sweave vignettes for details. Note that if, for example, a vignette has engine ‘knitr::rmarkdown’, then knitr provides the engine but both knitr and rmarkdown are needed for using it, so both these packages need to be in the ‘VignetteBuilder’ field and at least suggested (as rmarkdown is only suggested by knitr, and hence not available automatically along with it). Many packages using knitr also need the package formatR which it suggests and so the user package needs to do so too and include this in ‘VignetteBuilder’.
If the DESCRIPTION file is not entirely in ASCII it should contain an ‘Encoding’ field specifying an encoding. This is used as the encoding of the DESCRIPTION file itself and of the R and NAMESPACE files, and as the default encoding of .Rd files. The examples are assumed to be in this encoding when running R CMD check, and it is used for the encoding of the CITATION file. Only encoding names latin1, latin2 and UTF-8 are known to be portable. (Do not specify an encoding unless one is actually needed: doing so makes the package less portable. If a package has a specified encoding, you should run R CMD build etc in a locale using that encoding.)
The ‘NeedsCompilation’ field should be set to "yes" if the package contains native code which needs to be compiled, otherwise "no" (when the package could be installed from source on any platform without additional tools). This is used by install.packages(type = "both") in R >= 2.15.2 on platforms where binary packages are the norm: it is normally set by R CMD build or the repository assuming compilation is required if and only if the package has a src directory.
The ‘OS_type’ field specifies the OS(es) for which the package is intended. If present, it should be one of unix or windows, and indicates that the package can only be installed on a platform with ‘.Platform$OS.type’ having that value.
The ‘Type’ field specifies the type of the package: see Package types.
One can add subject classifications for the content of the package using the fields ‘Classification/ACM’ or ‘Classification/ACM-2012’ (using the Computing Classification System of the Association for Computing Machinery, https://www.acm.org/publications/class-2012; the former refers to the 1998 version), ‘Classification/JEL’ (the Journal of Economic Literature Classification System, https://www.aeaweb.org/econlit/jelCodes.php, or ‘Classification/MSC’ or ‘Classification/MSC-2010’ (the Mathematics Subject Classification of the American Mathematical Society, https://mathscinet.ams.org/msc/msc2010.html; the former refers to the 2000 version). The subject classifications should be comma-separated lists of the respective classification codes, e.g., ‘Classification/ACM: G.4, H.2.8, I.5.1’.
A ‘Language’ field can be used to indicate if the package documentation is not in English: this should be a comma-separated list of standard (not private use or grandfathered) IETF language tags as currently defined by RFC 5646 (https://tools.ietf.org/html/rfc5646, see also https://en.wikipedia.org/wiki/IETF_language_tag), i.e., use language subtags which in essence are 2-letter ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) or 3-letter ISO 639-3 (https://en.wikipedia.org/wiki/ISO_639-3) language codes.
An ‘RdMacros’ field can be used to hold a comma-separated list of packages from which the current package will import Rd macro definitions. These package should also be listed in ‘Imports’ (or ‘Depends’). The macros in these packages will be imported after the system macros, in the order listed in the ‘RdMacros’ field, before any macro definitions in the current package are loaded. Macro definitions in individual .Rd files in the man directory are loaded last, and are local to later parts of that file. In case of duplicates, the last loaded definition will be used.8 Both R CMD Rd2pdf and R CMD Rdconv have an optional flag --RdMacros=pkglist. The option is also a comma-separated list of package names, and has priority over the value given in DESCRIPTION. Packages using Rd macros should depend on R 3.2.0 or later.
Note: There should be no ‘Built’ or ‘Packaged’ fields, as these are added by the package management tools.
There is no restriction on the use of other fields not mentioned here (but using other capitalizations of these field names would cause confusion). Fields Note, Contact (for contacting the authors/developers9) and MailingList are in common use. Some repositories (including CRAN and R-forge) add their own fields.
Next: Package Dependencies, Previous: The DESCRIPTION file, Up: Package structure [Contents][Index]
1.1.2 Licensing
Licensing for a package which might be distributed is an important but potentially complex subject.
It is very important that you include license information! Otherwise, it may not even be legally correct for others to distribute copies of the package, let alone use it.
The package management tools use the concept of ‘free or open source software’ (FOSS, e.g., https://en.wikipedia.org/wiki/FOSS) licenses: the idea being that some users of R and its packages want to restrict themselves to such software. Others need to ensure that there are no restrictions stopping them using a package, e.g. forbidding commercial or military use. It is a central tenet of FOSS software that there are no restrictions on users nor usage.
Do not use the ‘License’ field for information on copyright holders: if needed, use a ‘Copyright’ field.
The mandatory ‘License’ field in the DESCRIPTION file should specify the license of the package in a standardized form. Alternatives are indicated via vertical bars. Individual specifications must be one of
One of the “standard” short specifications
GPL-2 GPL-3 LGPL-2 LGPL-2.1 LGPL-3 AGPL-3 Artistic-2.0
BSD_2_clause BSD_3_clause MIT
as made available via https://www.R-project.org/Licenses/ and contained in subdirectory share/licenses of the R source or home directory.
The names or abbreviations of other licenses contained in the license data base in file share/licenses/license.db in the R source or home directory, possibly (for versioned licenses) followed by a version restriction of the form ‘(op v)’ with ‘op’ one of the comparison operators ‘<’, ‘<=’, ‘>’, ‘>=’, ‘==’, or ‘!=’ and ‘v’ a numeric version specification (strings of non-negative integers separated by ‘.’), possibly combined via ‘,’ (see below for an example). For versioned licenses, one can also specify the name followed by the version, or combine an existing abbreviation and the version with a ‘-’.
Abbreviations GPL and LGPL are ambiguous and usually10 taken to mean any version of the license: but it is better not to use them.
One of the strings ‘file LICENSE’ or ‘file LICENCE’ referring to a file named LICENSE or LICENCE in the package (source and installation) top-level directory.
The string ‘Unlimited’, meaning that there are no restrictions on distribution or use other than those imposed by relevant laws (including copyright laws).
If a package license restricts a base license (where permitted, e.g., using GPL-3 or AGPL-3 with an attribution clause), the additional terms should be placed in file LICENSE (or LICENCE), and the string ‘+ file LICENSE’ (or ‘+ file LICENCE’, respectively) should be appended to the corresponding individual license specification. Note that several commonly used licenses do not permit restrictions: this includes GPL-2 and hence any specification which includes it.
Examples of standardized specifications include
License: GPL-2
License: LGPL (>= 2.0, < 3) | Mozilla Public License
License: GPL-2 | file LICENCE
License: GPL (>= 2) | BSD_3_clause + file LICENSE
License: Artistic-2.0 | AGPL-3 + file LICENSE
Please note in particular that “Public domain” is not a valid license, since it is not recognized in some jurisdictions.
Please ensure that the license you choose also covers any dependencies (including system dependencies) of your package: it is particularly important that any restrictions on the use of such dependencies are evident to people reading your DESCRIPTION file.
Fields ‘License_is_FOSS’ and ‘License_restricts_use’ may be added by repositories where information cannot be computed from the name of the license. ‘License_is_FOSS: yes’ is used for licenses which are known to be FOSS, and ‘License_restricts_use’ can have values ‘yes’ or ‘no’ if the LICENSE file is known to restrict users or usage, or known not to. These are used by, e.g., the available.packages filters.
The optional file LICENSE/LICENCE contains a copy of the license of the package. To avoid any confusion only include such a file if it is referred to in the ‘License’ field of the DESCRIPTION file.
Whereas you should feel free to include a license file in your source distribution, please do not arrange to install yet another copy of the GNU COPYING or COPYING.LIB files but refer to the copies on https://www.R-project.org/Licenses/ and included in the R distribution (in directory share/licenses). Since files named LICENSE or LICENCE will be installed, do not use these names for standard license files. To include comments about the licensing rather than the body of a license, use a file named something like LICENSE.note.
A few “standard” licenses are rather license templates which need additional information to be completed via ‘+ file LICENSE’.
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1.1.3 Package Dependencies
The ‘Depends’ field gives a comma-separated list of package names which this package depends on. Those packages will be attached before the current package when library or require is called. Each package name may be optionally followed by a comment in parentheses specifying a version requirement. The comment should contain a comparison operator, whitespace and a valid version number, e.g. ‘MASS (>= 3.1-20)’.
The ‘Depends’ field can also specify a dependence on a certain version of R — e.g., if the package works only with R version 4.0.0 or later, include ‘R (>= 4.0)’ in the ‘Depends’ field. (As here, trailing zeroes can be dropped and it is recommended that they are.) You can also require a certain SVN revision for R-devel or R-patched, e.g. ‘R (>= 2.14.0), R (>= r56550)’ requires a version later than R-devel of late July 2011 (including released versions of 2.14.0).
It makes no sense to declare a dependence on R without a version specification, nor on the package base: this is an R package and package base is always available.
A package or ‘R’ can appear more than once in the ‘Depends’ field, for example to give upper and lower bounds on acceptable versions.
It is inadvisable to use a dependence on R with patchlevel (the third digit) other than zero. Doing so with packages which others depend on will cause the other packages to become unusable under earlier versions in the series, and e.g. versions 4.x.1 are widely used throughout the Northern Hemisphere academic year.
Both library and the R package checking facilities use this field: hence it is an error to use improper syntax or misuse the ‘Depends’ field for comments on other software that might be needed. The R INSTALL facilities check if the version of R used is recent enough for the package being installed, and the list of packages which is specified will be attached (after checking version requirements) before the current package.
The ‘Imports’ field lists packages whose namespaces are imported from (as specified in the NAMESPACE file) but which do not need to be attached. Namespaces accessed by the ‘::’ and ‘:::’ operators must be listed here, or in ‘Suggests’ or ‘Enhances’ (see below). Ideally this field will include all the standard packages that are used, and it is important to include S4-using packages (as their class definitions can change and the DESCRIPTION file is used to decide which packages to re-install when this happens). Packages declared in the ‘Depends’ field should not also be in the ‘Imports’ field. Version requirements can be specified and are checked when the namespace is loaded.
The ‘Suggests’ field uses the same syntax as ‘Depends’ and lists packages that are not necessarily needed. This includes packages used only in examples, tests or vignettes (see Writing package vignettes), and packages loaded in the body of functions. E.g., suppose an example11 from package foo uses a dataset from package bar. Then it is not necessary to have bar use foo unless one wants to execute all the examples/tests/vignettes: it is useful to have bar, but not necessary. Version requirements can be specified but should be checked by the code which uses the package.
Finally, the ‘Enhances’ field lists packages “enhanced” by the package at hand, e.g., by providing methods for classes from these packages, or ways to handle objects from these packages (so several packages have ‘Enhances: chron’ because they can handle datetime objects from chron even though they prefer R’s native datetime functions). Version requirements can be specified, but are currently not used. Such packages cannot be required to check the package: any tests which use them must be conditional on the presence of the package. (If your tests use e.g. a dataset from another package it should be in ‘Suggests’ and not ‘Enhances’.)
The general rules are
A package should be listed in only one of these fields.
Packages whose namespace only is needed to load the package using library(pkgname) should be listed in the ‘Imports’ field and not in the ‘Depends’ field. Packages listed in import or importFrom directives in the NAMESPACE file should almost always be in ‘Imports’ and not ‘Depends’.
Packages that need to be attached to successfully load the package using library(pkgname) must be listed in the ‘Depends’ field.
All packages that are needed12 to successfully run R CMD check on the package must be listed in one of ‘Depends’ or ‘Suggests’ or ‘Imports’. Packages used to run examples or tests conditionally (e.g. via if(require(pkgname))) should be listed in ‘Suggests’ or ‘Enhances’. (This allows checkers to ensure that all the packages needed for a complete check are installed.)
Packages needed to use datasets from the package should be in ‘Imports’: this includes those needed to define S4 classes used.
In particular, packages providing “only” data for examples or vignettes should be listed in ‘Suggests’ rather than ‘Depends’ in order to make lean installations possible.
Version dependencies in the ‘Depends’ and ‘Imports’ fields are used by library when it loads the package, and install.packages checks versions for the ‘Depends’, ‘Imports’ and (for dependencies = TRUE) ‘Suggests’ fields.
It is important that the information in these fields is complete and accurate: it is for example used to compute which packages depend on an updated package and which packages can safely be installed in parallel.
This scheme was developed before all packages had namespaces (R 2.14.0 in October 2011), and good practice changed once that was in place.
Field ‘Depends’ should nowadays be used rarely, only for packages which are intended to be put on the search path to make their facilities available to the end user (and not to the package itself): for example it makes sense that a user of package latticeExtra would want the functions of package lattice made available.
Almost always packages mentioned in ‘Depends’ should also be imported from in the NAMESPACE file: this ensures that any needed parts of those packages are available when some other package imports the current package.
The ‘Imports’ field should not contain packages which are not imported from (via the NAMESPACE file or :: or ::: operators), as all the packages listed in that field need to be installed for the current package to be installed. (This is checked by R CMD check.)
R code in the package should call library or require only exceptionally. Such calls are never needed for packages listed in ‘Depends’ as they will already be on the search path. It used to be common practice to use require calls for packages listed in ‘Suggests’ in functions which used their functionality, but nowadays it is better to access such functionality via :: calls.
A package that wishes to make use of header files in other packages to compile its C/C++ code needs to declare them as a comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file. For example
LinkingTo: link1, link2
The ‘LinkingTo’ field can have a version requirement which is checked at installation.
Specifying a package in ‘LinkingTo’ suffices if these are C/C++ headers containing source code or static linking is done at installation: the packages do not need to be (and usually should not be) listed in the ‘Depends’ or ‘Imports’ fields. This includes CRAN package BH and almost all users of RcppArmadillo and RcppEigen. Note that ‘LinkingTo’ applies only to installation: if a packages wishes to use headers to compile code in tests or vignettes the package providing them needs to be listed in ‘Suggests’ or perhaps ‘Depends’.
For another use of ‘LinkingTo’ see Linking to native routines in other packages.
The ‘Additional_repositories’ field is a comma-separated list of repository URLs where the packages named in the other fields may be found. It is currently used by R CMD check to check that the packages can be found, at least as source packages (which can be installed on any platform).
Suggested packages
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1.1.3.1 Suggested packages
Note that someone wanting to run the examples/tests/vignettes may not have a suggested package available (and it may not even be possible to install it for that platform). The recommendation used to be to make their use conditional via if(require("pkgname")): this is OK if that conditioning is done in examples/tests/vignettes, although using if(requireNamespace("pkgname")) is preferred, if possible.
However, using require for conditioning in package code is not good practice as it alters the search path for the rest of the session and relies on functions in that package not being masked by other require or library calls. It is better practice to use code like
if (requireNamespace("rgl", quietly = TRUE)) {
rgl::plot3d(...)
} else {
## do something else not involving rgl.
}
Note the use of rgl:: as that object would not necessarily be visible (and if it is, it need not be the one from that namespace: plot3d occurs in several other packages). If the intention is to give an error if the suggested package is not available, simply use e.g. rgl::plot3d.
If the conditional code produces print output, function withAutoprint can be useful.
Note that the recommendation to use suggested packages conditionally in tests does also apply to packages used to manage test suites: a notorious example was testthat which in version 1.0.0 contained illegal C++ code and hence could not be installed on standards-compliant platforms.
Some people have assumed that a ‘recommended’ package in ‘Suggests’ can safely be used unconditionally, but this is not so. (R can be installed without recommended packages, and which packages are ‘recommended’ may change.)
As noted above, packages in ‘Enhances’ must be used conditionally and hence objects within them should always be accessed via ::.
On most systems, R CMD check can be run with only those packages declared in ‘Depends’ and ‘Imports’ by setting environment variable _R_CHECK_DEPENDS_ONLY_=true, whereas setting _R_CHECK_SUGGESTS_ONLY_=true also allows suggested packages, but not those in ‘Enhances’ nor those not mentioned in the DESCRIPTION file. It is recommended that a package is checked with each of these set, as well as with neither.
WARNING: Be extremely careful if you do things which would be run at installation time depending on whether suggested packages are available or not—this includes top-level code in R code files, .onLoad functions and the definitions of S4 classes and methods. The problem is that once a namespace of a suggested package is loaded, references to it may be captured in the installed package (most commonly in S4 methods), but the suggested package may not be available when the installed package is used (which especially for binary packages might be on a different machine). Even worse, the problems might not be confined to your package, for the namespaces of your suggested packages will also be loaded whenever any package which imports yours is installed and so may be captured there.
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1.1.4 The INDEX file
The optional file INDEX contains a line for each sufficiently interesting object in the package, giving its name and a description (functions such as print methods not usually called explicitly might not be included). Normally this file is missing and the corresponding information is automatically generated from the documentation sources (using tools::Rdindex()) when installing from source.
The file is part of the information given by library(help = pkgname).
Rather than editing this file, it is preferable to put customized information about the package into an overview help page (see Documenting packages) and/or a vignette (see Writing package vignettes).
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1.1.5 Package subdirectories
The R subdirectory contains R code files, only. The code files to be installed must start with an ASCII (lower or upper case) letter or digit and have one of the extensions13 .R, .S, .q, .r, or .s. We recommend using .R, as this extension seems to be not used by any other software. It should be possible to read in the files using source(), so R objects must be created by assignments. Note that there need be no connection between the name of the file and the R objects created by it. Ideally, the R code files should only directly assign R objects and definitely should not call functions with side effects such as require and options. If computations are required to create objects these can use code ‘earlier’ in the package (see the ‘Collate’ field) plus functions in the ‘Depends’ packages provided that the objects created do not depend on those packages except via namespace imports.
Extreme care is needed if top-level computations are made to depend on availability or not of other packages. In particular this applies to setMethods and setClass calls.
Two exceptions are allowed: if the R subdirectory contains a file sysdata.rda (a saved image of one or more R objects: please use suitable compression as suggested by tools::resaveRdaFiles, and see also the ‘SysDataCompression’ DESCRIPTION field.) this will be lazy-loaded into the namespace environment – this is intended for system datasets that are not intended to be user-accessible via data. Also, files ending in ‘.in’ will be allowed in the R directory to allow a configure script to generate suitable files.
Only ASCII characters (and the control characters tab, formfeed, LF and CR) should be used in code files. Other characters are accepted in comments14, but then the comments may not be readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally15 fail when the package is installed. Any byte will be allowed in a quoted character string but ‘\uxxxx’ escapes should be used for non-ASCII characters. However, non-ASCII character strings may not be usable in some locales and may display incorrectly in others.
Various R functions in a package can be used to initialize and clean up. See Load hooks.
The man subdirectory should contain (only) documentation files for the objects in the package in R documentation (Rd) format. The documentation filenames must start with an ASCII (lower or upper case) letter or digit and have the extension .Rd (the default) or .rd. Further, the names must be valid in ‘file://’ URLs, which means16 they must be entirely ASCII and not contain ‘%’. See Writing R documentation files, for more information. Note that all user-level objects in a package should be documented; if a package pkg contains user-level objects which are for “internal” use only, it should provide a file pkg-internal.Rd which documents all such objects, and clearly states that these are not meant to be called by the user. See e.g. the sources for package grid in the R distribution. Note that packages which use internal objects extensively should not export those objects from their namespace, when they do not need to be documented (see Package namespaces).
Having a man directory containing no documentation files may give an installation error.
The man subdirectory may contain a subdirectory named macros; this will contain source for user-defined Rd macros. (See User-defined macros.) These use the Rd format, but may not contain anything but macro definitions, comments and whitespace.
The R and man subdirectories may contain OS-specific subdirectories named unix or windows.
The sources and headers for the compiled code are in src, plus optionally a file Makevars or Makefile. When a package is installed using R CMD INSTALL, make is used to control compilation and linking into a shared object for loading into R. There are default make variables and rules for this (determined when R is configured and recorded in R_HOME/etcR_ARCH/Makeconf), providing support for C, C++, fixed- or free-form Fortran, Objective C and Objective C++17 with associated extensions .c, .cc or .cpp, .f, .f90 or .f95, .m, and .mm, respectively. We recommend using .h for headers, also for C++18 or Fortran 9x include files. (Use of extension .C for C++ is no longer supported.) Files in the src directory should not be hidden (start with a dot), and hidden files will under some versions of R be ignored.
It is not portable (and may not be possible at all) to mix all these languages in a single package. Because R itself uses it, we know that C and fixed-form Fortran can be used together, and mixing C, C++ and Fortran usually work for the platform’s native compilers.
If your code needs to depend on the platform there are certain defines which can used in C or C++. On all Windows builds (even 64-bit ones) ‘_WIN32’ will be defined: on 64-bit Windows builds also ‘_WIN64’. On macOS ‘__APPLE__’ is defined19; for an ‘Apple Silicon’ platform, test for both ‘__APPLE__’ and ‘__arm64__’.
The default rules can be tweaked by setting macros20 in a file src/Makevars (see Using Makevars). Note that this mechanism should be general enough to eliminate the need for a package-specific src/Makefile. If such a file is to be distributed, considerable care is needed to make it general enough to work on all R platforms. If it has any targets at all, it should have an appropriate first target named ‘all’ and a (possibly empty) target ‘clean’ which removes all files generated by running make (to be used by ‘R CMD INSTALL --clean’ and ‘R CMD INSTALL --preclean’). There are platform-specific file names on Windows: src/Makevars.win takes precedence over src/Makevars and src/Makefile.win must be used. Some make programs require makefiles to have a complete final line, including a newline.
A few packages use the src directory for purposes other than making a shared object (e.g. to create executables). Such packages should have files src/Makefile and src/Makefile.win (unless intended for only Unix-alikes or only Windows).
In very special cases packages may create binary files other than the shared objects/DLLs in the src directory. Such files will not be installed in a multi-architecture setting since R CMD INSTALL --libs-only is used to merge multiple sub-architectures and it only copies shared objects/DLLs. If a package wants to install other binaries (for example executable programs), it should provide an R script src/install.libs.R which will be run as part of the installation in the src build directory instead of copying the shared objects/DLLs. The script is run in a separate R environment containing the following variables: R_PACKAGE_NAME (the name of the package), R_PACKAGE_SOURCE (the path to the source directory of the package), R_PACKAGE_DIR (the path of the target installation directory of the package), R_ARCH (the arch-dependent part of the path, often empty), SHLIB_EXT (the extension of shared objects) and WINDOWS (TRUE on Windows, FALSE elsewhere). Something close to the default behavior could be replicated with the following src/install.libs.R file:
files <- Sys.glob(paste0("*", SHLIB_EXT))
dest <- file.path(R_PACKAGE_DIR, paste0('libs', R_ARCH))
dir.create(dest, recursive = TRUE, showWarnings = FALSE)
file.copy(files, dest, overwrite = TRUE)
if(file.exists("symbols.rds"))
file.copy("symbols.rds", dest, overwrite = TRUE)
On the other hand, executable programs could be installed along the lines of
execs <- c("one", "two", "three")
if(WINDOWS) execs <- paste0(execs, ".exe")
if ( any(file.exists(execs)) ) {
dest <- file.path(R_PACKAGE_DIR, paste0('bin', R_ARCH))
dir.create(dest, recursive = TRUE, showWarnings = FALSE)
file.copy(execs, dest, overwrite = TRUE)
}
Note the use of architecture-specific subdirectories of bin where needed.
The data subdirectory is for data files: See Data in packages.
The demo subdirectory is for R scripts (for running via demo()) that demonstrate some of the functionality of the package. Demos may be interactive and are not checked automatically, so if testing is desired use code in the tests directory to achieve this. The script files must start with a (lower or upper case) letter and have one of the extensions .R or .r. If present, the demo subdirectory should also have a 00Index file with one line for each demo, giving its name and a description separated by a tab or at least three spaces. (This index file is not generated automatically.) Note that a demo does not have a specified encoding and so should be an ASCII file (see Encoding issues). Function demo() will use the package encoding if there is one, but this is mainly useful for non-ASCII comments.
The contents of the inst subdirectory will be copied recursively to the installation directory. Subdirectories of inst should not interfere with those used by R (currently, R, data, demo, exec, libs, man, help, html and Meta, and earlier versions used latex, R-ex). The copying of the inst happens after src is built so its Makefile can create files to be installed. To exclude files from being installed, one can specify a list of exclude patterns in file .Rinstignore in the top-level source directory. These patterns should be Perl-like regular expressions (see the help for regexp in R for the precise details), one per line, to be matched case-insensitively against the file and directory paths, e.g. doc/.*[.]png$ will exclude all PNG files in inst/doc based on the extension.
Note that with the exceptions of INDEX, LICENSE/LICENCE and NEWS, information files at the top level of the package will not be installed and so not be known to users of Windows and macOS compiled packages (and not seen by those who use R CMD INSTALL or install.packages() on the tarball). So any information files you wish an end user to see should be included in inst. Note that if the named exceptions also occur in inst, the version in inst will be that seen in the installed package.
Things you might like to add to inst are a CITATION file for use by the citation function, and a NEWS.Rd file for use by the news function. See its help page for the specific format restrictions of the NEWS.Rd file.
Another file sometimes needed in inst is AUTHORS or COPYRIGHTS to specify the authors or copyright holders when this is too complex to put in the DESCRIPTION file.
Subdirectory tests is for additional package-specific test code, similar to the specific tests that come with the R distribution. Test code can either be provided directly in a .R (or .r as from R 3.4.0) file, or via a .Rin file containing code which in turn creates the corresponding .R file (e.g., by collecting all function objects in the package and then calling them with the strangest arguments). The results of running a .R file are written to a .Rout file. If there is a corresponding21 .Rout.save file, these two are compared, with differences being reported but not causing an error. The directory tests is copied to the check area, and the tests are run with the copy as the working directory and with R_LIBS set to ensure that the copy of the package installed during testing will be found by library(pkg_name). Note that the package-specific tests are run in a vanilla R session without setting the random-number seed, so tests which use random numbers will need to set the seed to obtain reproducible results (and it can be helpful to do so in all cases, to avoid occasional failures when tests are run).
If directory tests has a subdirectory Examples containing a file pkg-Ex.Rout.save, this is compared to the output file for running the examples when the latter are checked. Reference output should be produced without having the --timings option set (and note that --as-cran sets it).
If reference output is included for examples, tests or vignettes do make sure that it is fully reproducible, as it will be compared verbatim to that produced in a check run, unless the ‘IGNORE_RDIFF’ markup is used. Things which trip up maintainers include displayed version numbers from loading other packages, printing numerical results to an unreproducibly high precision and printing timings. Another trap is small values which are in fact rounding error from zero: consider using zapsmall.
Subdirectory exec could contain additional executable scripts the package needs, typically scripts for interpreters such as the shell, Perl, or Tcl. NB: only files (and not directories) under exec are installed (and those with names starting with a dot are ignored), and they are all marked as executable (mode 755, moderated by ‘umask’) on POSIX platforms. Note too that this is not suitable for executable programs since some platforms (including Windows) support multiple architectures using the same installed package directory.
Subdirectory po is used for files related to localization: see Internationalization.
Subdirectory tools is the preferred place for auxiliary files needed during configuration, and also for sources need to re-create scripts (e.g. M4 files for autoconf: some prefer to put those in a subdirectory m4 of tools).
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1.1.6 Data in packages
The data subdirectory is for data files, either to be made available via lazy-loading or for loading using data(). (The choice is made by the ‘LazyData’ field in the DESCRIPTION file: the default is not to do so.) It should not be used for other data files needed by the package, and the convention has grown up to use directory inst/extdata for such files.
Data files can have one of three types as indicated by their extension: plain R code (.R or .r), tables (.tab, .txt, or .csv, see ?data for the file formats, and note that .csv is not the standard22 CSV format), or save() images (.RData or .rda). The files should not be hidden (have names starting with a dot). Note that R code should be if possible “self-sufficient” and not make use of extra functionality provided by the package, so that the data file can also be used without having to load the package or its namespace: it should run as silently as possible and not change the search() path by attaching packages or other environments.
Images (extensions .RData23 or .rda) can contain references to the namespaces of packages that were used to create them. Preferably there should be no such references in data files, and in any case they should only be to packages listed in the Depends and Imports fields, as otherwise it may be impossible to install the package. To check for such references, load all the images into a vanilla R session, run str() on all the datasets, and look at the output of loadedNamespaces().
Particular care is needed where a dataset or one of its components is of an S4 class, especially if the class is defined in a different package. First, the package containing the class definition has to be available to do useful things with the dataset, so that package must be listed in Imports or Depends (even if this gives a check warning about unused imports). Second, the definition of an S4 class can change, and often is unnoticed when in a package with a different author. So it may be wiser to use the .R form and use that to create the dataset object when needed (loading package namespaces but not attaching them by using requireNamespace(pkg, quietly = TRUE) and using pkg:: to refer to objects in the namespace).
If you are not using ‘LazyData’ and either your data files are large or e.g., you use data/foo.R scripts to produce your data, loading your namespace, you can speed up installation by providing a file datalist in the data subdirectory. This should have one line per topic that data() will find, in the format ‘foo’ if data(foo) provides ‘foo’, or ‘foo: bar bah’ if data(foo) provides ‘bar’ and ‘bah’. R CMD build will automatically add a datalist file to data directories of over 1Mb, using the function tools::add_datalist.
Tables (.tab, .txt, or .csv files) can be compressed by gzip, bzip2 or xz, optionally with additional extension .gz, .bz2 or .xz.
If your package is to be distributed, do consider the resource implications of large datasets for your users: they can make packages very slow to download and use up unwelcome amounts of storage space, as well as taking many seconds to load. It is normally best to distribute large datasets as .rda images prepared by save(, compress = TRUE) (the default). Using bzip2 or xz compression will usually reduce the size of both the package tarball and the installed package, in some cases by a factor of two or more.
Package tools has a couple of functions to help with data images: checkRdaFiles reports on the way the image was saved, and resaveRdaFiles will re-save with a different type of compression, including choosing the best type for that particular image.
Many packages using ‘LazyData’ will benefit from using a form of compression other than gzip in the installed lazy-loading database. This can be selected by the --data-compress option to R CMD INSTALL or by using the ‘LazyDataCompression’ field in the DESCRIPTION file. Useful values are bzip2, xz and the default, gzip: value none is also accepted. The only way to discover which is best is to try them all and look at the size of the pkgname/data/Rdata.rdb file. A function to do that (quoting sizes in KB) is
CheckLazyDataCompression <- function(pkg)
{
pkg_name <- sub("_.*", "", pkg)
lib <- tempfile(); dir.create(lib)
zs <- c("gzip", "bzip2", "xz")
res <- integer(3); names(res) <- zs
for (z in zs) {
opts <- c(paste0("--data-compress=", z),
"--no-libs", "--no-help", "--no-demo", "--no-exec", "--no-test-load")
install.packages(pkg, lib, INSTALL_opts = opts, repos = NULL, quiet = TRUE)
res[z] <- file.size(file.path(lib, pkg_name, "data", "Rdata.rdb"))
}
ceiling(res/1024)
}
(applied to a source package without any ‘LazyDataCompression’ field). R CMD check will warn if it finds a pkgname/data/Rdata.rdb file of more than 5MB without ‘LazyDataCompression’ being set. If you see that, run CheckLazyDataCompression() and set the field – to gzip in the unlikely event24 that is the best choice.
The analogue for sysdata.rda is field ‘SysDataCompression’: the default is xz for files bigger than 1MB otherwise gzip.
Lazy-loading is not supported for very large datasets (those which when serialized exceed 2GB, the limit for the format on 32-bit platforms).
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1.1.7 Non-R scripts in packages
Code which needs to be compiled (C, C++, Fortran …) is included in the src subdirectory and discussed elsewhere in this document.
Subdirectory exec could be used for scripts for interpreters such as the shell, BUGS, JavaScript, Matlab, Perl, php (amap), Python or Tcl (Simile), or even R. However, it seems more common to use the inst directory, for example WriteXLS/inst/Perl, NMF/inst/m-files, RnavGraph/inst/tcl, RProtoBuf/inst/python and emdbook/inst/BUGS and gridSVG/inst/js.
Java code is a special case: except for very small programs, .java files should be byte-compiled (to a .class file) and distributed as part of a .jar file: the conventional location for the .jar file(s) is inst/java. It is desirable (and required under an Open Source license) to make the Java source files available: this is best done in a top-level java directory in the package—the source files should not be installed.
If your package requires one of these interpreters or an extension then this should be declared in the ‘SystemRequirements’ field of its DESCRIPTION file. (Users of Java most often do so via rJava, when depending on/importing that suffices unless there is a version requirement on Java code in the package.)
Windows and Mac users should be aware that the Tcl extensions ‘BWidget’ and ‘Tktable’ (which have sometimes been included in the Windows25 and macOS R installers) are extensions and do need to be declared (and that ‘Tktable’ is less widely available than it used to be, including not in the main repositories for major Linux distributions). ‘BWidget’ needs to be installed by the user on other OSes. This is fairly easy to do: first find the Tcl search path:
library(tcltk)
strsplit(tclvalue('auto_path'), " ")[[1]]
then download the sources from https://sourceforge.net/projects/tcllib/files/BWidget/ and in a terminal run something like
tar xf bwidget-1.9.14.tar.gz
sudo mv bwidget-1.9.14 /usr/local/lib
substituting a location on the Tcl search path for /usr/local/lib if needed. (If no location on that search path is writeable, you will need to add one each time BWidget is to be used with tcltk::addTclPath().)
To (silently) test for the presence of ‘Tktable’ one can use
library(tcltk)
have_tktable <- !isFALSE(suppressWarnings(tclRequire('Tktable')))
Installing ‘Tktable’ needs a C compiler and the Tk headers (not necessarily installed with Tcl/Tk). At the time of writing the latest sources (from 2008) were available from https://sourceforge.net/projects/tktable/files/tktable/2.10/Tktable2.10.tar.gz, but needed patching for current Tk (8.6.11, but not 8.6.10) – a patch can be found at https://www.stats.ox.ac.uk/pub/bdr/Tktable/. For a system installation of Tk you may need to install Tktable as ‘root’ as on e.g. Fedora all the locations on auto_path are owned by ‘root’.
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1.1.8 Specifying URLs
URLs in many places in the package documentation will be converted to clickable hyperlinks in at least some of their renderings. So care is needed that their forms are correct and portable.
The full URL should be given, including the scheme (often ‘http://’ or ‘https://’) and a final ‘/’ for references to directories.
Spaces in URLs are not portable and how they are handled does vary by HTTP server and by client. There should be no space in the host part of an ‘http://’ URL, and spaces in the remainder should be encoded, with each space replaced by ‘%20’.
Other characters may benefit from being encoded: see the help on URLencode().
The canonical URL for a CRAN package is
https://cran.r-project.org/package=pkgname
and not a version starting ‘https://cran.r-project.org/web/packages/pkgname’.
Next: Checking and building packages, Previous: Package structure, Up: Creating R packages [Contents][Index]
1.2 Configure and cleanup
Note that most of this section is specific to Unix-alikes: see the comments later on about the Windows port of R.
If your package needs some system-dependent configuration before installation you can include an executable (Bourne26 shell script configure in your package which (if present) is executed by R CMD INSTALL before any other action is performed. This can be a script created by the Autoconf mechanism, but may also be a script written by yourself. Use this to detect if any nonstandard libraries are present such that corresponding code in the package can be disabled at install time rather than giving error messages when the package is compiled or used. To summarize, the full power of Autoconf is available for your extension package (including variable substitution, searching for libraries, etc.). Background and useful tips on Autoconf and related tools (including pkg-config described below) can be found at https://autotools.io/.
A configure script is run in an environment which has all the environment variables set for an R session (see R_HOME/etc/Renviron) plus R_PACKAGE_NAME (the name of the package), R_PACKAGE_DIR (the path of the target installation directory of the package, a temporary location for staged installs) and R_ARCH (the arch-dependent part of the path, often empty).
Under a Unix-alike only, an executable (Bourne shell) script cleanup is executed as the last thing by R CMD INSTALL if option --clean was given, and by R CMD build when preparing the package for building from its source.
As an example consider we want to use functionality provided by a (C or Fortran) library foo. Using Autoconf, we can create a configure script which checks for the library, sets variable HAVE_FOO to TRUE if it was found and to FALSE otherwise, and then substitutes this value into output files (by replacing instances of ‘@HAVE_FOO@’ in input files with the value of HAVE_FOO). For example, if a function named bar is to be made available by linking against library foo (i.e., using -lfoo), one could use
AC_CHECK_LIB(foo, fun, [HAVE_FOO=TRUE], [HAVE_FOO=FALSE])
AC_SUBST(HAVE_FOO)
......
AC_CONFIG_FILES([foo.R])
AC_OUTPUT
in configure.ac (assuming Autoconf 2.50 or later).
The definition of the respective R function in foo.R.in could be
foo <- function(x) {
if(!@HAVE_FOO@)
stop("Sorry, library ‘foo’ is not available")
...
From this file configure creates the actual R source file foo.R looking like
foo <- function(x) {
if(!FALSE)
stop("Sorry, library ‘foo’ is not available")
...
if library foo was not found (with the desired functionality). In this case, the above R code effectively disables the function.
One could also use different file fragments for available and missing functionality, respectively.
You will very likely need to ensure that the same C compiler and compiler flags are used in the configure tests as when compiling R or your package. Under a Unix-alike, you can achieve this by including the following fragment early in configure.ac (before calling AC_PROG_CC or anything which calls it)
: ${R_HOME=`R RHOME`}
if test -z "${R_HOME}"; then
echo "could not determine R_HOME"
exit 1
fi
CC=`"${R_HOME}/bin/R" CMD config CC`
CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
(Using ‘${R_HOME}/bin/R’ rather than just ‘R’ is necessary in order to use the correct version of R when running the script as part of R CMD INSTALL, and the quotes since ‘${R_HOME}’ might contain spaces.)
If your code does load checks (for example, to check for an entry point in a library or to run code) then you will also need
LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS`
Packages written with C++ need to pick up the details for the C++ compiler and switch the current language to C++ by something like
CXX=`"${R_HOME}/bin/R" CMD config CXX`
if test -z "$CXX"; then
AC_MSG_ERROR([No C++ compiler is available])
fi
CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
AC_LANG(C++)
The latter is important, as for example C headers may not be available to C++ programs or may not be written to avoid C++ name-mangling. Note that an R installation is not required to have a C++ compiler so ‘CXX’ may be empty. If the package specifies a non-default C++ standard, use the config variable names (such as CXX17) appropriate to the standard, but still set CXX and CXXFLAGS.
You can use R CMD config to get the value of the basic configuration variables, and also the header and library flags necessary for linking a front-end executable program against R, see R CMD config --help for details. If you do, it is essential that you use both the command and the appropriate flags, so that for example ‘CC’ must always be used with ‘CFLAGS’ and (for code to be linked into a shared library) ‘CPICFLAGS’. For Fortran, be careful to use ‘FC FFLAGS FPICFLAGS’ for fixed-form Fortran and ‘FC FCFLAGS FPICFLAGS’ for free-form Fortran.
To check for an external BLAS library using the AX_BLAS macro from the official Autoconf Macro Archive27, one can use
FC=`"${R_HOME}/bin/R" CMD config FC`
FCLAGS=`"${R_HOME}/bin/R" CMD config FFLAGS`
AC_PROG_FC
FLIBS=`"${R_HOME}/bin/R" CMD config FLIBS`
AX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))
Note that FLIBS as determined by R must be used to ensure that Fortran code works on all R platforms.
N.B.: If the configure script creates files, e.g. src/Makevars, you do need a cleanup script to remove them. Otherwise R CMD build may ship the files that are created. For example, package RODBC has
#!/bin/sh
rm -f config.* src/Makevars src/config.h
As this example shows, configure often creates working files such as config.log.
If your configure script needs auxiliary files, it is recommended that you ship them in a tools directory (as R itself does).
You should bear in mind that the configure script will not be used on Windows systems. If your package is to be made publicly available, please give enough information for a user on a non-Unix-alike platform to configure it manually, or provide a configure.win script to be used on that platform. (Optionally, there can be a cleanup.win script. Both should be shell scripts to be executed by ash, which is a minimal version of Bourne-style sh.) When configure.win is run the environment variables R_HOME (which uses ‘/’ as the file separator), R_ARCH and R_ARCH_BIN will be set. Use R_ARCH to decide if this is a 64-bit build (its value there is ‘/x64’) and to install DLLs to the correct place (${R_HOME}/libs${R_ARCH}). Use R_ARCH_BIN to find the correct place under the bin directory, e.g. ${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe.
In some rare circumstances, the configuration and cleanup scripts need to know the location into which the package is being installed. An example of this is a package that uses C code and creates two shared object/DLLs. Usually, the object that is dynamically loaded by R is linked against the second, dependent, object. On some systems, we can add the location of this dependent object to the object that is dynamically loaded by R. This means that each user does not have to set the value of the LD_LIBRARY_PATH (or equivalent) environment variable, but that the secondary object is automatically resolved. Another example is when a package installs support files that are required at run time, and their location is substituted into an R data structure at installation time. The names of the top-level library directory (i.e., specifiable via the ‘-l’ argument) and the directory of the package itself are made available to the installation scripts via the two shell/environment variables R_LIBRARY_DIR and R_PACKAGE_DIR. Additionally, the name of the package (e.g. ‘survival’ or ‘MASS’) being installed is available from the environment variable R_PACKAGE_NAME. (Currently the value of R_PACKAGE_DIR is always ${R_LIBRARY_DIR}/${R_PACKAGE_NAME}, but this used not to be the case when versioned installs were allowed. Its main use is in configure.win scripts for the installation path of external software’s DLLs.) Note that the value of R_PACKAGE_DIR may contain spaces and other shell-unfriendly characters, and so should be quoted in makefiles and configure scripts.
One of the more tricky tasks can be to find the headers and libraries of external software. One tool which is increasingly available on Unix-alikes (but not by default28 on macOS) to do this is pkg-config. The configure script will need to test for the presence of the command itself29 (see for example package Cairo), and if present it can be asked if the software is installed, of a suitable version and for compilation/linking flags by e.g.
$ pkg-config --exists ‘QtCore >= 4.0.0’ # check the status
$ pkg-config --modversion QtCore
4.8.7
$ pkg-config --cflags QtCore
-DQT_SHARED -I/usr/include/QtCore
$ pkg-config --libs QtCore
-lQtCore
$ pkg-config --static --libs QtCore
-lQtCore -lpthread -lz -lm -ldl -lgthread-2.0 -pthread -lglib-2.0 -lrt
Note that pkg-config --libs gives the information required to link against the default version30 of that library (usually the dynamic one), and pkg-config --static --libs may be needed if the static library is to be used.
Static libraries are commonly used on macOS (and Windows) to facilitate bundling external software with binary distributions of packages. This means that portable (source) packages need to allow for this. It is not safe to just use pkg-config --static --libs, as that will often include further libraries that are not necessarily installed on the user’s system (or maybe only the versioned library such as libjbig.so.2.1 is installed and not libjbig.so which would be needed to use -ljbig often included in pkg-config --static --libs libtiff-4).
Sometimes the name by which the software is known to pkg-config is not what one might expect (e.g. ‘gtk+-2.0’ even for 2.22). To get a complete list use
pkg-config --list-all | sort
If using Autoconf it is good practice to include all the Autoconf sources in the the package (and required for an Open Source package and tested by R CMD check --as-cran). This will include the file configure.ac31 in the top-level directory of the package. If extensions written in m4 are needed, these should be included under the directory tools and included from configure.ac via e.g.,
m4_include([tools/ax_pthread.m4])
Alternatively, Autoconf can be asked to search all .m4 files in a directory by including something like32
AC_CONFIG_MACRO_DIR([tools/m4])
One source of such extensions is the ‘Autoconf Archive’ (https://www.gnu.org/software/autoconf-archive/. It is not safe to assume this is installed on users’ machines, so the extension should be shipped with the package (taking care to comply with its licence).
Using Makevars
Configure example
Using F9x code
Using C++ code
Next: Configure example, Previous: Configure and cleanup, Up: Configure and cleanup [Contents][Index]
1.2.1 Using Makevars
Sometimes writing your own configure script can be avoided by supplying a file Makevars: also one of the most common uses of a configure script is to make Makevars from Makevars.in.
A Makevars file is a makefile and is used as one of several makefiles by R CMD SHLIB (which is called by R CMD INSTALL to compile code in the src directory). It should be written if at all possible in a portable style, in particular (except for Makevars.win) without the use of GNU extensions.
The most common use of a Makevars file is to set additional preprocessor options (for example include paths and definitions) for C/C++ files via PKG_CPPFLAGS, and additional compiler flags by setting PKG_CFLAGS, PKG_CXXFLAGS or PKG_FFLAGS, for C, C++ or Fortran respectively (see Creating shared objects).
N.B.: Include paths are preprocessor options, not compiler options, and must be set in PKG_CPPFLAGS as otherwise platform-specific paths (e.g. ‘-I/usr/local/include’) will take precedence. PKG_CPPFLAGS should contain ‘-I’, ‘-D’, ‘-U’ and (where supported) ‘-include’ and ‘-pthread’ options: everything else should be a compiler flag. The order of flags matters, and using ‘-I’ in PKG_CFLAGS or PKG_CXXFLAGS has led to hard-to-debug platform-specific errors.
Makevars can also be used to set flags for the linker, for example ‘-L’ and ‘-l’ options, via PKG_LIBS.
When writing a Makevars file for a package you intend to distribute, take care to ensure that it is not specific to your compiler: flags such as -O2 -Wall -pedantic (and all other -W flags: for the Oracle compilers these are used to pass arguments to compiler phases) are all specific to GCC.
Also, do not set variables such as CPPFLAGS, CFLAGS etc.: these should be settable by users (sites) through appropriate personal (site-wide) Makevars files. See Customizing package compilation in R Installation and Administration ,
There are some macros33 which are set whilst configuring the building of R itself and are stored in R_HOME/etcR_ARCH/Makeconf. That makefile is included as a Makefile after Makevars[.win], and the macros it defines can be used in macro assignments and make command lines in the latter. These include
FLIBS ¶
A macro containing the set of libraries need to link Fortran code. This may need to be included in PKG_LIBS: it will normally be included automatically if the package contains Fortran source files in the src directory.
BLAS_LIBS ¶
A macro containing the BLAS libraries used when building R. This may need to be included in PKG_LIBS. Beware that if it is empty then the R executable will contain all the double-precision and double-complex BLAS routines, but no single-precision nor complex routines. If BLAS_LIBS is included, then FLIBS also needs to be34 included following it, as most BLAS libraries are written at least partially in Fortran.
LAPACK_LIBS ¶
A macro containing the LAPACK libraries (and paths where appropriate) used when building R. This may need to be included in PKG_LIBS. It may point to a dynamic library libRlapack which contains the main double-precision LAPACK routines as well as those double-complex LAPACK routines needed to build R, or it may point to an external LAPACK library, or may be empty if an external BLAS library also contains LAPACK.
[libRlapack includes all the double-precision LAPACK routines which were current in 2003: a list of which routines are included is in file src/modules/lapack/README. Note that an external LAPACK/BLAS library need not do so, as some were ‘deprecated’ (and not compiled by default) in LAPACK 3.6.0 in late 2015.]
For portability, the macros BLAS_LIBS and FLIBS should always be included after LAPACK_LIBS (and in that order).
SAFE_FFLAGS ¶
A macro containing flags which are needed to circumvent over-optimization of FORTRAN code: it is might be ‘-g -O2 -ffloat-store’ or ‘-g -O2 -msse2 -mfpmath=sse’ on ‘ix86’ platforms using gfortran. Note that this is not an additional flag to be used as part of PKG_FFLAGS, but a replacement for FFLAGS. See the example later in this section.
Setting certain macros in Makevars will prevent R CMD SHLIB setting them: in particular if Makevars sets ‘OBJECTS’ it will not be set on the make command line. This can be useful in conjunction with implicit rules to allow other types of source code to be compiled and included in the shared object. It can also be used to control the set of files which are compiled, either by excluding some files in src or including some files in subdirectories. For example
OBJECTS = 4dfp/endianio.o 4dfp/Getifh.o R4dfp-object.o
Note that Makevars should not normally contain targets, as it is included before the default makefile and make will call the first target, intended to be all in the default makefile. If you really need to circumvent that, use a suitable (phony) target all before any actual targets in Makevars.[win]: for example package fastICA used to have
PKG_LIBS = @BLAS_LIBS@
SLAMC_FFLAGS=$(R_XTRA_FFLAGS) $(FPICFLAGS) $(SHLIB_FFLAGS) $(SAFE_FFLAGS)
all: $(SHLIB)
slamc.o: slamc.f
$(FC) $(SLAMC_FFLAGS) -c -o slamc.o slamc.f
needed to ensure that the LAPACK routines find some constants without infinite looping. The Windows equivalent was
all: $(SHLIB)
slamc.o: slamc.f
$(FC) $(SAFE_FFLAGS) -c -o slamc.o slamc.f
(since the other macros are all empty on that platform, and R’s internal BLAS was not used). Note that the first target in Makevars will be called, but for back-compatibility it is best named all.
If you want to create and then link to a library, say using code in a subdirectory, use something like
.PHONY: all mylibs
all: $(SHLIB)
$(SHLIB): mylibs
mylibs:
(cd subdir; $(MAKE))
Be careful to create all the necessary dependencies, as there is no guarantee that the dependencies of all will be run in a particular order (and some of the CRAN build machines use multiple CPUs and parallel makes). In particular,
all: mylibs
does not suffice. GNU make does allow the construct
.NOTPARALLEL: all
all: mylibs $(SHLIB)
but that is not portable. dmake and pmake allow the similar .NO_PARALLEL, also not portable: some variants of pmake accept .NOTPARALLEL as an alias for .NO_PARALLEL.
Note that on Windows it is required that Makevars[.win] does create a DLL: this is needed as it is the only reliable way to ensure that building a DLL succeeded. If you want to use the src directory for some purpose other than building a DLL, use a Makefile.win file.
It is sometimes useful to have a target ‘clean’ in Makevars or Makevars.win: this will be used by R CMD build to clean up (a copy of) the package sources. When it is run by build it will have fewer macros set, in particular not $(SHLIB), nor $(OBJECTS) unless set in the file itself. It would also be possible to add tasks to the target ‘shlib-clean’ which is run by R CMD INSTALL and R CMD SHLIB with options --clean and --preclean.
If you want to run R code in Makevars, e.g. to find configuration information, please do ensure that you use the correct copy of R or Rscript: there might not be one in the path at all, or it might be the wrong version or architecture. The correct way to do this is via
"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" filename
"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e ‘R expression’
where $(R_ARCH_BIN) is only needed currently on Windows.
Environment or make variables can be used to select different macros for 32- and 64-bit code, for example (GNU make syntax, allowed on Windows)
ifeq "$(WIN)" "64"
PKG_LIBS = value for 64-bit Windows
else
PKG_LIBS = value for 32-bit Windows
endif
On Windows there is normally a choice between linking to an import library or directly to a DLL. Where possible, the latter is much more reliable: import libraries are tied to a specific toolchain, and in particular on 64-bit Windows two different conventions have been commonly used. So for example instead of
PKG_LIBS = -L$(XML_DIR)/lib -lxml2
one can use
PKG_LIBS = -L$(XML_DIR)/bin -lxml2
since on Windows -lxxx will look in turn for
libxxx.dll.a
xxx.dll.a
libxxx.a
xxx.lib
libxxx.dll
xxx.dll
where the first and second are conventionally import libraries, the third and fourth often static libraries (with .lib intended for Visual C++), but might be import libraries. See for example https://sourceware.org/binutils/docs-2.20/ld/WIN32.html#WIN32.
The fly in the ointment is that the DLL might not be named libxxx.dll, and in fact on 32-bit Windows there is a libxml2.dll whereas on one build for 64-bit Windows the DLL is called libxml2-2.dll. Using import libraries can cover over these differences but can cause equal difficulties.
If static libraries are available they can save a lot of problems with run-time finding of DLLs, especially when binary packages are to be distributed and even more when these support both architectures. Where using DLLs is unavoidable we normally arrange (via configure.win) to ship them in the same directory as the package DLL.
OpenMP support
Using pthreads
Compiling in sub-directories
Next: Using pthreads, Previous: Using Makevars, Up: Using Makevars [Contents][Index]
1.2.1.1 OpenMP support
There is some support for packages which wish to use OpenMP35. The make macros
SHLIB_OPENMP_CFLAGS
SHLIB_OPENMP_CXXFLAGS
SHLIB_OPENMP_FFLAGS
are available for use in src/Makevars or src/Makevars.win. Include the appropriate macro in PKG_CFLAGS, PKG_CXXFLAGS and so on, and also in PKG_LIBS (but see below for Fortran). C/C++ code that needs to be conditioned on the use of OpenMP can be used inside #ifdef _OPENMP: note that some toolchains used for R (including Apple’s for macOS and some others using clang36) have no OpenMP support at all, not even omp.h.
For example, a package with C code written for OpenMP should have in src/Makevars the lines
PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)
Note that the macro SHLIB_OPENMP_CXXFLAGS applies to the default C++ compiler and not necessarily to the C++17/20 compiler: users of the latter should do their own configure checks. If you do use your own checks, make sure that OpenMP support is complete by compiling and linking an OpenMP-using program: on some platforms the runtime library is optional and on others that library depends on other optional libraries.
Some care is needed when compilers are from different families which may use different OpenMP runtimes (e.g. clang vs GCC including gfortran, although it is often possible to use the clang runtime with GCC but not vice versa: however gfortran >= 9 may generate calls not in the clang runtime). For a package with Fortran code using OpenMP the appropriate lines are
PKG_FFLAGS = $(SHLIB_OPENMP_FFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)
as the C compiler will be used to link the package code. There are platforms on which this does not work for some OpenMP-using code and installation will fail. Since R >= 3.6.2 the best alternative for a package with only Fortran sources using OpenMP is to use
USE_FC_TO_LINK =
PKG_FFLAGS = $(SHLIB_OPENMP_FFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_FFLAGS)
in src/Makevars or src/Makevars.win. Note however, that when this is used $(FLIBS) should not be included in PKG_LIBS since it is for linking Fortran-compiled code by the C compiler.
Common platforms may inline all OpenMP calls and so tolerate the omission of the OpenMP flag from PKG_LIBS, but this usually results in an installation failure with a different compiler or compilation flags. So cross-check that e.g. -fopenmp appears in the linking line in the installation logs.
It is not portable to use OpenMP with more than one of C, C++ and Fortran in a single package since it is not uncommon that the compilers are of different families.
For portability, any C/C++ code using the omp_* functions should include the omp.h header: some compilers (but not all) include it when OpenMP mode is switched on (e.g. via flag -fopenmp).
There is nothing37 to say what version of OpenMP is supported: version 4.0 (and much of 4.5 or 5.0) is supported by recent versions of the Linux, Windows and Solaris platforms, but portable packages cannot assume that end users have recent versions. Apple clang on macOS has no OpenMP support. https://www.openmp.org/resources/openmp-compilers-tools/ gives some idea of what compilers support what versions.
Rarely, using OpenMP with clang on Linux generates calls in libatomic, resulting in loading messages like
undefined symbol: __atomic_compare_exchange
undefined symbol: __atomic_load
The workaround is to link with -latomic (having checked it exists).
The performance of OpenMP varies substantially between platforms. The Windows implementation has substantial overheads, so is only beneficial if quite substantial tasks are run in parallel. Also, on Windows new threads are started with the default38 FPU control word, so computations done on OpenMP threads will not make use of extended-precision arithmetic which is the default for the main process.
Do not include these macros unless your code does make use of OpenMP (possibly for C++ via included external headers): this can result in the OpenMP runtime being linked in, threads being started, ….
Calling any of the R API from threaded code is ‘for experts only’ and strongly discouraged. Many functions in the R API modify internal R data structures and might corrupt these data structures if called simultaneously from multiple threads. Most R API functions can signal errors, which must only happen on the R main thread. Also, external libraries (e.g. LAPACK) may not be thread-safe.
Packages are not standard-alone programs, and an R process could contain more than one OpenMP-enabled package as well as other components (for example, an optimized BLAS) making use of OpenMP. So careful consideration needs to be given to resource usage. OpenMP works with parallel regions, and for most implementations the default is to use as many threads as ‘CPUs’ for such regions. Parallel regions can be nested, although it is common to use only a single thread below the first level. The correctness of the detected number of ‘CPUs’ and the assumption that the R process is entitled to use them all are both dubious assumptions. One way to limit resources is to limit the overall number of threads available to OpenMP in the R process: this can be done via environment variable OMP_THREAD_LIMIT, where implemented.39 Alternatively, the number of threads per region can be limited by the environment variable OMP_NUM_THREADS or API call omp_set_num_threads, or, better, for the regions in your code as part of their specification. E.g. R uses40
#pragma omp parallel for num_threads(nthreads) …
That way you only control your own code and not that of other OpenMP users.
Note that setting environment variables to control OpenMP is implementation-dependent and may need to be done outside the R process or before any use of OpenMP (which might be by another process or R itself). Also, implementation-specific variables such as KMP_THREAD_LIMIT might take precedence.
Next: Compiling in sub-directories, Previous: OpenMP support, Up: Using Makevars [Contents][Index]
1.2.1.2 Using pthreads
There is no direct support for the POSIX threads (more commonly known as pthreads): by the time we considered adding it several packages were using it unconditionally so it seems that nowadays it is universally available on POSIX operating systems (hence not Windows).
For reasonably recent versions of gcc and clang the correct specification is
PKG_CPPFLAGS = -pthread
PKG_LIBS = -pthread
(and the plural version is also accepted on some systems/versions). For other platforms the specification is
PKG_CPPFLAGS = -D_REENTRANT
PKG_LIBS = -lpthread
(and note that the library name is singular). This is what -pthread does on all known current platforms (although earlier versions of OpenBSD used a different library name).
For a tutorial see https://hpc-tutorials.llnl.gov/posix/.
POSIX threads are not normally used on Windows, which has its own native concepts of threads. However, there are two projects implementing pthreads on top of Windows, pthreads-w32 and winpthreads (part of the MinGW-w64 project).
Whether Windows toolchains implement pthreads is up to the toolchain provider. A make variable SHLIB_PTHREAD_FLAGS is available for use in src/Makevars.win: this should be included in both PKG_CPPFLAGS (or the Fortran compiler flags) and PKG_LIBS.
The presence of a working pthreads implementation cannot be unambiguously determined without testing for yourself: however, that ‘_REENTRANT’ is defined41 in C/C++ code is a good indication.
Note that not all pthreads implementations are equivalent as parts are optional (see https://pubs.opengroup.org/onlinepubs/009695399/basedefs/pthread.h.html): for example, macOS lacks the ‘Barriers’ option.
See also the comments on thread-safety and performance under OpenMP: on all known R platforms OpenMP is implemented via pthreads and the known performance issues are in the latter.
Previous: Using pthreads, Up: Using Makevars [Contents][Index]
1.2.1.3 Compiling in sub-directories
Package authors fairly often want to organize code in sub-directories of src, for example if they are including a separate piece of external software to which this is an R interface.
One simple way is simply to set OBJECTS to be all the objects that need to be compiled, including in sub-directories. For example, CRAN package RSiena has
SOURCES = $(wildcard data/*.cpp network/*.cpp utils/*.cpp model/*.cpp model/*/*.cpp model/*/*/*.cpp)
OBJECTS = siena07utilities.o siena07internals.o siena07setup.o siena07models.o $(SOURCES:.cpp=.o)
One problem with that approach is that unless GNU make extensions are used, the source files need to be listed and kept up-to-date. As in the following from CRAN package lossDev:
OBJECTS.samplers = samplers/ExpandableArray.o samplers/Knots.o \
samplers/RJumpSpline.o samplers/RJumpSplineFactory.o \
samplers/RealSlicerOV.o samplers/SliceFactoryOV.o samplers/MNorm.o
OBJECTS.distributions = distributions/DSpline.o \
distributions/DChisqrOV.o distributions/DTOV.o \
distributions/DNormOV.o distributions/DUnifOV.o distributions/RScalarDist.o
OBJECTS.root = RJump.o
OBJECTS = $(OBJECTS.samplers) $(OBJECTS.distributions) $(OBJECTS.root)
Where the subdirectory is self-contained code with a suitable makefile, the best approach is something like
PKG_LIBS = -LCsdp/lib -lsdp $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
$(SHLIB): Csdp/lib/libsdp.a
Csdp/lib/libsdp.a:
@(cd Csdp/lib && $(MAKE) libsdp.a \
CC="$(CC)" CFLAGS="$(CFLAGS) $(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)")
Note the quotes: the macros can contain spaces, e.g. CC = "gcc -m64 -std=gnu99". Several authors have forgotten about parallel makes: the static library in the subdirectory must be made before the shared object ($(SHLIB)) and so the latter must depend on the former. Others forget the need42 for position-independent code.
We really do not recommend using src/Makefile instead of src/Makevars, and as the example above shows, it is not necessary.
Next: Using F9x code, Previous: Using Makevars, Up: Configure and cleanup [Contents][Index]
1.2.2 Configure example
It may be helpful to give an extended example of using a configure script to create a src/Makevars file: this is based on that in the RODBC package.
The configure.ac file follows: configure is created from this by running autoconf in the top-level package directory (containing configure.ac).
AC_INIT([RODBC], 1.1.8) dnl package name, version
dnl A user-specifiable option
odbc_mgr=""
AC_ARG_WITH([odbc-manager],
AC_HELP_STRING([--with-odbc-manager=MGR],
[specify the ODBC manager, e.g. odbc or iodbc]),
[odbc_mgr=$withval])
if test "$odbc_mgr" = "odbc" ; then
AC_PATH_PROGS(ODBC_CONFIG, odbc_config)
fi
dnl Select an optional include path, from a configure option
dnl or from an environment variable.
AC_ARG_WITH([odbc-include],
AC_HELP_STRING([--with-odbc-include=INCLUDE_PATH],
[the location of ODBC header files]),
[odbc_include_path=$withval])
RODBC_CPPFLAGS="-I."
if test [ -n "$odbc_include_path" ] ; then
RODBC_CPPFLAGS="-I. -I${odbc_include_path}"
else
if test [ -n "${ODBC_INCLUDE}" ] ; then
RODBC_CPPFLAGS="-I. -I${ODBC_INCLUDE}"
fi
fi
dnl ditto for a library path
AC_ARG_WITH([odbc-lib],
AC_HELP_STRING([--with-odbc-lib=LIB_PATH],
[the location of ODBC libraries]),
[odbc_lib_path=$withval])
if test [ -n "$odbc_lib_path" ] ; then
LIBS="-L$odbc_lib_path ${LIBS}"
else
if test [ -n "${ODBC_LIBS}" ] ; then
LIBS="-L${ODBC_LIBS} ${LIBS}"
else
if test -n "${ODBC_CONFIG}"; then
odbc_lib_path=`odbc_config --libs | sed s/-lodbc//`
LIBS="${odbc_lib_path} ${LIBS}"
fi
fi
fi
dnl Now find the compiler and compiler flags to use
: ${R_HOME=`R RHOME`}
if test -z "${R_HOME}"; then
echo "could not determine R_HOME"
exit 1
fi
CC=`"${R_HOME}/bin/R" CMD config CC`
CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
if test -n "${ODBC_CONFIG}"; then
RODBC_CPPFLAGS=`odbc_config --cflags`
fi
CPPFLAGS="${CPPFLAGS} ${RODBC_CPPFLAGS}"
dnl Check the headers can be found
AC_CHECK_HEADERS(sql.h sqlext.h)
if test "${ac_cv_header_sql_h}" = no ||
test "${ac_cv_header_sqlext_h}" = no; then
AC_MSG_ERROR("ODBC headers sql.h and sqlext.h not found")
fi
dnl search for a library containing an ODBC function
if test [ -n "${odbc_mgr}" ] ; then
AC_SEARCH_LIBS(SQLTables, ${odbc_mgr}, ,
AC_MSG_ERROR("ODBC driver manager ${odbc_mgr} not found"))
else
AC_SEARCH_LIBS(SQLTables, odbc odbc32 iodbc, ,
AC_MSG_ERROR("no ODBC driver manager found"))
fi
dnl for 64-bit ODBC need SQL[U]LEN, and it is unclear where they are defined.
AC_CHECK_TYPES([SQLLEN, SQLULEN], , , [# include <sql.h>])
dnl for unixODBC header
AC_CHECK_SIZEOF(long, 4)
dnl substitute RODBC_CPPFLAGS and LIBS
AC_SUBST(RODBC_CPPFLAGS)
AC_SUBST(LIBS)
AC_CONFIG_HEADERS([src/config.h])
dnl and do substitution in the src/Makevars.in and src/config.h
AC_CONFIG_FILES([src/Makevars])
AC_OUTPUT
where src/Makevars.in would be simply
PKG_CPPFLAGS = @RODBC_CPPFLAGS@
PKG_LIBS = @LIBS@
A user can then be advised to specify the location of the ODBC driver manager files by options like (lines broken for easier reading)
R CMD INSTALL \
--configure-args='--with-odbc-include=/opt/local/include \
--with-odbc-lib=/opt/local/lib --with-odbc-manager=iodbc' \
RODBC
or by setting the environment variables ODBC_INCLUDE and ODBC_LIBS.
Next: Using C++ code, Previous: Configure example, Up: Configure and cleanup [Contents][Index]
1.2.3 Using F9x code
R assumes that source files with extension .f are fixed-form Fortran 90 (which includes Fortran 77), and passes them to the compiler specified by macro ‘FC’. On known platforms the Fortran compiler will also accept free-form Fortran 90/95 code with extension .f90 or .f95, but those are not used by R itself so this is not required.
The same compiler is used43 for both fixed-form and free-form Fortran code (with different file extensions and possibly different flags). Macro PKG_FFLAGS can be used for package-specific flags: for the un-encountered case that both are included in a single package and that different flags are needed for the two forms, macro PKG_FCFLAGS is also available for free-form Fortran.
The code used to build R allows a ‘Fortran 90’ compiler to be selected as ‘FC’, so platforms might be encountered which only support Fortran 90. However, Fortran 95 is widely supported.
Some compilers specified by ‘FC’ will accept Fortran 2003, 2008 or 2018 code: such code should still use file extension .f90 or .f95. Most platforms use gfortran where you may need to include -std=f2003, -std=f2008 or (from version 8) -std=f2018 in PKG_FFLAGS or PKG_FCFLAGS: the default is ‘GNU Fortran’, Fortran 95 with non-standard extensions. The Oracle f95 compiler ‘accepts some Fortran 2003/8 features’ (search for ‘Oracle Developer Studio 12.6: Fortran User’s Guide’ and look for §4.6). Intel Fortran has full Fortran 2008 support from version 17.0, and some 2018 support in version 16.0 and more in version 19.0.
Modern versions of Fortran support modules, whereby compiling one source file creates a module file which is then included in others. (Module files typically have a .mod extension: they do depend on the compiler used and so should never be included in a package.) This creates a dependence which make will not know about and often causes installation with a parallel make to fail. Thus it is necessary to add explicit dependencies to src/Makevars to tell make the constraints on the order of compilation. For example, if file iface.f90 creates a module ‘iface’ used by files cmi.f90 and dmi.f90 then src/Makevars needs to contain something like
cmi.o dmi.o: iface.o
Note that it is not portable (although some platforms do accept it) to define a module of the same name in multiple source files.
Previous: Using F9x code, Up: Configure and cleanup [Contents][Index]
1.2.4 Using C++ code
R can be built without a C++ compiler although one is available (but not necessarily installed) on all known R platforms. As from R 4.0.0 a C++ compiler will be selected only if it conforms to the 2011 standard (‘C++11’). A minor update44 (‘C++14’) was published in December 2014 and will be used by default as from R 4.1.0 if supported. Further revisions ‘C++17’ (in December 2017), and ‘C++20’ (with many new features in December 2020) have been published since.
What standard a C++ compiler aims to support can be hard to determine: the value45 of __cplusplus may help but some compilers use it to denote a standard which is partially supported and some the latest standard which is (almost) fully supported.
The webpage https://en.cppreference.com/w/cpp/compiler_support gives some information on which compilers are known to support recent C++ features.
Different versions of R have specified different minimum C++ standards, so for maximal portability a package should specify the standard it requires. In order to specify C++11 code in a package, Makevars file (or Makevars.win on Windows) should include the line
CXX_STD = CXX11
Compilation and linking will then be done with the C++11 compiler (if any).
Packages without a src/Makevars or src/Makefile file may specify that they require C++11 for code in the src directory by including ‘C++11’ in the ‘SystemRequirements’ field of the DESCRIPTION file, e.g.
SystemRequirements: C++11
If a package does have a src/Makevars[.win] file then setting the make variable ‘CXX_STD’ is preferred, as it allows R CMD SHLIB to work correctly in the package’s src directory.
If a package using C++ has a configure script it is essential that it selects the correct C++ standard, via something like
CXX11=`"${R_HOME}/bin/R" CMD config CXX11`
if test -z "$CXX11"; then
AC_MSG_ERROR([No C++11 compiler is available])
fi
CXX11STD=`"${R_HOME}/bin/R" CMD config CXX11STD`
CXX="${CXX11} ${CXX11STD}"
CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX11FLAGS`
AC_LANG(C++)
if C++11 was specified, but using CXX instead of CXX11 if no standard was specified.
If you want to compile C++ code in a subdirectory, make sure you pass down the macros to specify the appropriate compiler, e.g. in src/Makevars
sublibs:
@(cd libs && $(MAKE) \
CXX="$(CXX11) $(CXX11STD)" CXXFLAGS="$(CXX11FLAGS) $(CXX11PICFLAGS)")
Note that the mechanisms described here specify C++11 for code compiled by R CMD SHLIB as used by default by R CMD INSTALL. They do not necessarily apply if there is a src/Makefile file, nor to compilation done in vignettes or via other packages.
Support for a C++14 compiler (where available) has been in R since version 3.4.0. Similar considerations to C++11 apply, with the variables associated with the C++14 compiler using the prefix ‘CXX14’ instead of ‘CXX11’. For example, to use C++14 code in a package, the package’s Makevars file (or Makevars.win on Windows) should include the line
CXX_STD = CXX14
Essentially complete C++14 support is available from GCC 5, LLVM clang 3.4 and currently-used versions of Apple clang.
Code needing C++14 features can check for their presence via ‘SD-6 feature tests’46. Such a check could be
#include <memory> // header where this is defined
#if defined(__cpp_lib_make_unique) && (__cpp_lib_make_unique >= 201304)
using std::make_unique;
#else
// your emulation
#endif
Note that Windows builds prior to R 4.0.0 used g++ 4.9.x which had only partial C++14 support, and the flag to obtain that support was not included in the default Windows build of R — one could try something like
CXX14="$(BINPREF)g++ $(M_ARCH)"
CXX14FLAGS="-O2 -Wall"
CXX14STD=-std=gnu1y
in HOME/.R/Makevars.win. The g++ version used from R 4.0.0 supports C++14 with flag -std=gnu14 and for back-compatibility -std=gnu1y.
C++17 (as from R 3.4.0) and C++20 (as from R 4.0.0) can be specified in an analogous way (replacing 14 by 17 or 20) but compiler/OS support is platform-dependent. Some C++17 and C++20 support is available with the default builds of R on macOS and Windows as from R 4.0.0. Much of g++’s support for C++17 needs version 7 or later: that is more recent than some still-current Linux distributions and the OpenCSW compilers for Solaris.
Note that C++17 or C++20 ‘support’ does not mean complete support: use feature tests as well as resources such as https://en.cppreference.com/w/cpp/compiler_support, https://gcc.gnu.org/projects/cxx-status.html and https://clang.llvm.org/cxx_status.html to see if the features you want to use are widely implemented.
A requirement of C++17 or later should always be declared in the ‘SystemRequirements’ field (as well as in src/Makevars or src/Makefile) so this is shown on the package’s summary pages on CRAN or similar.
Next: Writing package vignettes, Previous: Configure and cleanup, Up: Creating R packages [Contents][Index]
1.3 Checking and building packages
Before using these tools, please check that your package can be installed. R CMD check will inter alia do this, but you may get more detailed error messages doing the install directly.
If your package specifies an encoding in its DESCRIPTION file, you should run these tools in a locale which makes use of that encoding: they may not work at all or may work incorrectly in other locales (although UTF-8 locales will most likely work).
Note: R CMD check and R CMD build run R processes with --vanilla in which none of the user’s startup files are read. If you need R_LIBS set (to find packages in a non-standard library) you can set it in the environment: also you can use the check and build environment files (as specified by the environment variables R_CHECK_ENVIRON and R_BUILD_ENVIRON; if unset, files47 ~/.R/check.Renviron and ~/.R/build.Renviron are used) to set environment variables when using these utilities.
Note to Windows users: R CMD build may make use of the Windows toolset (see the “R Installation and Administration” manual) if present and in your path, and it is required for packages which need it to install (including those with configure.win or cleanup.win scripts or a src directory) and e.g. need vignettes built.
You may need to set the environment variable TMPDIR to point to a suitable writable directory with a path not containing spaces – use forward slashes for the separators. Also, the directory needs to be on a case-honouring file system (some network-mounted file systems are not).
Checking packages
Building package tarballs
Building binary packages
Next: Building package tarballs, Previous: Checking and building packages, Up: Checking and building packages [Contents][Index]
1.3.1 Checking packages
Using R CMD check, the R package checker, one can test whether source R packages work correctly. It can be run on one or more directories, or compressed package tar archives with extension .tar.gz, .tgz, .tar.bz2 or .tar.xz.
It is strongly recommended that the final checks are run on a tar archive prepared by R CMD build.
This runs a series of checks, including
The package is installed. This will warn about missing cross-references and duplicate aliases in help files.
The file names are checked to be valid across file systems and supported operating system platforms.
The files and directories are checked for sufficient permissions (Unix-alikes only).
The files are checked for binary executables, using a suitable version of file if available48. (There may be rare false positives.)
The DESCRIPTION file is checked for completeness, and some of its entries for correctness. Unless installation tests are skipped, checking is aborted if the package dependencies cannot be resolved at run time. (You may need to set R_LIBS in the environment if dependent packages are in a separate library tree.) One check is that the package name is not that of a standard package, nor one of the defunct standard packages (‘ctest’, ‘eda’, ‘lqs’, ‘mle’, ‘modreg’, ‘mva’, ‘nls’, ‘stepfun’ and ‘ts’). Another check is that all packages mentioned in library or requires or from which the NAMESPACE file imports or are called via :: or ::: are listed (in ‘Depends’, ‘Imports’, ‘Suggests’): this is not an exhaustive check of the actual imports.
Available index information (in particular, for demos and vignettes) is checked for completeness.
The package subdirectories are checked for suitable file names and for not being empty. The checks on file names are controlled by the option --check-subdirs=value. This defaults to ‘default’, which runs the checks only if checking a tarball: the default can be overridden by specifying the value as ‘yes’ or ‘no’. Further, the check on the src directory is only run if the package does not contain a configure script (which corresponds to the value ‘yes-maybe’) and there is no src/Makefile or src/Makefile.in.
To allow a configure script to generate suitable files, files ending in ‘.in’ will be allowed in the R directory.
A warning is given for directory names that look like R package check directories – many packages have been submitted to CRAN containing these.
The R files are checked for syntax errors. Bytes which are non-ASCII are reported as warnings, but these should be regarded as errors unless it is known that the package will always be used in the same locale.
It is checked that the package can be loaded, first with the usual default packages and then only with package base already loaded. It is checked that the namespace can be loaded in an empty session with only the base namespace loaded. (Namespaces and packages can be loaded very early in the session, before the default packages are available, so packages should work then.)
The R files are checked for correct calls to library.dynam. Package startup functions are checked for correct argument lists and (incorrect) calls to functions which modify the search path or inappropriately generate messages. The R code is checked for possible problems using codetools. In addition, it is checked whether S3 methods have all the arguments of the corresponding generic, and whether the final argument of replacement functions is called ‘value’. All foreign function calls (.C, .Fortran, .Call and .External calls) are tested to see if they have a PACKAGE argument, and if not, whether the appropriate DLL might be deduced from the namespace of the package. Any other calls are reported. (The check is generous, and users may want to supplement this by examining the output of tools::checkFF("mypkg", verbose=TRUE), especially if the intention were to always use a PACKAGE argument)
The Rd files are checked for correct syntax and metadata, including the presence of the mandatory fields (\name, \alias, \title and \description). The Rd name and title are checked for being non-empty, and there is a check for missing cross-references (links).
A check is made for missing documentation entries, such as undocumented user-level objects in the package.
Documentation for functions, data sets, and S4 classes is checked for consistency with the corresponding code.
It is checked whether all function arguments given in \usage sections of Rd files are documented in the corresponding \arguments section.
The data directory is checked for non-ASCII characters and for the use of reasonable levels of compression.
C, C++ and Fortran source and header files49 are tested for portable (LF-only) line endings. If there is a Makefile or Makefile.in or Makevars or Makevars.in file under the src directory, it is checked for portable line endings and the correct use of ‘$(BLAS_LIBS)’ and ‘$(LAPACK_LIBS)’
Compiled code is checked for symbols corresponding to functions which might terminate R or write to stdout/stderr instead of the console. Note that the latter might give false positives in that the symbols might be pulled in with external libraries and could never be called. Windows50 users should note that the Fortran and C++ runtime libraries are examples of such external libraries.
Some checks are made of the contents of the inst/doc directory. These always include checking for files that look like leftovers, and if suitable tools (such as qpdf) are available, checking that the PDF documentation is of minimal size.
The examples provided by the package’s documentation are run. (see Writing R documentation files, for information on using \examples to create executable example code.) If there is a file tests/Examples/pkg-Ex.Rout.save, the output of running the examples is compared to that file.
Of course, released packages should be able to run at least their own examples. Each example is run in a ‘clean’ environment (so earlier examples cannot be assumed to have been run), and with the variables T and F redefined to generate an error unless they are set in the example: See Logical vectors in An Introduction to R .
If the package sources contain a tests directory then the tests specified in that directory are run. (Typically they will consist of a set of .R source files and target output files .Rout.save.) Please note that the comparison will be done in the end user’s locale, so the target output files should be ASCII if at all possible. (The command line option --test-dir=foo may be used to specify tests in a non-standard location. For example, unusually slow tests could be placed in inst/slowTests and then R CMD check --test-dir=inst/slowTests would be used to run them. Other names that have been suggested are, for example, inst/testWithOracle for tests that require Oracle to be installed, inst/randomTests for tests which use random values and may occasionally fail by chance, etc.)
The code in package vignettes (see Writing package vignettes) is executed, and the vignette PDFs re-made from their sources as a check of completeness of the sources (unless there is a ‘BuildVignettes’ field in the package’s DESCRIPTION file with a false value). If there is a target output file .Rout.save in the vignette source directory, the output from running the code in that vignette is compared with the target output file and any differences are reported (but not recorded in the log file). (If the vignette sources are in the deprecated location inst/doc, do mark such target output files to not be installed in .Rinstignore.)
If there is an error51 in executing the R code in vignette foo.ext, a log file foo.ext.log is created in the check directory. The vignette PDFs are re-made in a copy of the package sources in the vign_test subdirectory of the check directory, so for further information on errors look in directory pkgname/vign_test/vignettes. (It is only retained if there are errors or if environment variable _R_CHECK_CLEAN_VIGN_TEST_ is set to a false value.)
The PDF version of the package’s manual is created (to check that the Rd files can be converted successfully). This needs LaTeX and suitable fonts and LaTeX packages to be installed. See Making the manuals in R Installation and Administration .
All these tests are run with collation set to the C locale, and for the examples and tests with environment variable LANGUAGE=en: this is to minimize differences between platforms.
Use R CMD check --help to obtain more information about the usage of the R package checker. A subset of the checking steps can be selected by adding command-line options. It also allows customization by setting environment variables _R_CHECK_*_ as described in Tools in R Internals : a set of these customizations similar to those used by CRAN can be selected by the option --as-cran (which works best if Internet access is available). Some Windows users may need to set environment variable R_WIN_NO_JUNCTIONS to a non-empty value. The test of cyclic declarations52in DESCRIPTION files needs repositories (including CRAN) set: do this in ~/.Rprofile, by e.g.
options(repos = c(CRAN="https://cran.r-project.org"))
One check customization which can be revealing is
_R_CHECK_CODETOOLS_PROFILE_="suppressLocalUnused=FALSE"
which reports unused local assignments. Not only does this point out computations which are unnecessary because their results are unused, it also can uncover errors. (Two such are to intend to update an object by assigning a value but mistype its name or assign in the wrong scope, for example using <- where <<- was intended.) This can give false positives, most commonly because of non-standard evaluation for formulae and because the intention is to return objects in the environment of a function for later use.
Complete checking of a package which contains a file README.md needs a reasonably current version of pandoc installed: see https://pandoc.org/installing.html.
You do need to ensure that the package is checked in a suitable locale if it contains non-ASCII characters. Such packages are likely to fail some of the checks in a C locale, and R CMD check will warn if it spots the problem. You should be able to check any package in a UTF-8 locale (if one is available). Beware that although a C locale is rarely used at a console, it may be the default if logging in remotely or for batch jobs.
Multiple sub-architectures: On systems which support multiple sub-architectures (principally Windows), R CMD check will install and check a package which contains compiled code under all available sub-architectures. (Use option --force-multiarch to force this for packages without compiled code, which are otherwise only checked under the main sub-architecture.) This will run the loading tests, examples and tests directory under each installed sub-architecture in turn, and give an error if any fail. Where environment variables (including perhaps PATH) need to be set differently for each sub-architecture, these can be set in architecture-specific files such as R_HOME/etc/i386/Renviron.site.
An alternative approach is to use R CMD check --no-multiarch to check the primary sub-architecture, and then to use something like R --arch=x86_64 CMD check --extra-arch or (Windows) /path/to/R/bin/x64/Rcmd check --extra-arch to run for each additional sub-architecture just the checks53 which differ by sub-architecture. (This approach is required for packages which are installed by R CMD INSTALL --merge-multiarch.)
Where packages need additional commands to install all the sub-architectures these can be supplied by e.g. --install-args=--force-biarch.
Next: Building binary packages, Previous: Checking packages, Up: Checking and building packages [Contents][Index]
1.3.2 Building package tarballs
Packages may be distributed in source form as “tarballs” (.tar.gz files) or in binary form. The source form can be installed on all platforms with suitable tools and is the usual form for Unix-like systems; the binary form is platform-specific, and is the more common distribution form for the Windows and macOS platforms.
Using R CMD build, the R package builder, one can build R package tarballs from their sources (for example, for subsequent release). It is recommended that packages are built for release by the current release version of R or ‘r-patched’, to avoid inadvertently picking up new features of a development version of R.
Prior to actually building the package in the standard gzipped tar file format, a few diagnostic checks and cleanups are performed. In particular, it is tested whether object indices exist and can be assumed to be up-to-date, and C, C++ and Fortran source files and relevant makefiles in a src directory are tested and converted to LF line-endings if necessary.
Run-time checks whether the package works correctly should be performed using R CMD check prior to invoking the final build procedure.
To exclude files from being put into the package, one can specify a list of exclude patterns in file .Rbuildignore in the top-level source directory. These patterns should be Perl-like regular expressions (see the help for regexp in R for the precise details), one per line, to be matched case-insensitively against the file and directory names relative to the top-level package source directory. In addition, directories from source control systems54 or from eclipse55, directories with names check, chm, or ending .Rcheck or Old or old and files GNUMakefile56, Read-and-delete-me or with base names starting with ‘.#’, or starting and ending with ‘#’, or ending in ‘~’, ‘.bak’ or ‘.swp’, are excluded by default57. In addition, same-package tarballs (from previous builds) and their binary forms will be excluded from the top-level directory, as well as those files in the R, demo and man directories which are flagged by R CMD check as having invalid names.
Use R CMD build --help to obtain more information about the usage of the R package builder.
Unless R CMD build is invoked with the --no-build-vignettes option (or the package’s DESCRIPTION contains ‘BuildVignettes: no’ or similar), it will attempt to (re)build the vignettes (see Writing package vignettes) in the package. To do so it installs the current package into a temporary library tree, but any dependent packages need to be installed in an available library tree (see the Note: at the top of this section).
Similarly, if the .Rd documentation files contain any \Sexpr macros (see Dynamic pages), the package will be temporarily installed to execute them. Post-execution binary copies of those pages containing build-time macros will be saved in build/partial.rdb. If there are any install-time or render-time macros, a .pdf version of the package manual will be built and installed in the build subdirectory. (This allows CRAN or other repositories to display the manual even if they are unable to install the package.) This can be suppressed by the option --no-manual or if package’s DESCRIPTION contains ‘BuildManual: no’ or similar.
One of the checks that R CMD build runs is for empty source directories. These are in most (but not all) cases unintentional, if they are intentional use the option --keep-empty-dirs (or set the environment variable _R_BUILD_KEEP_EMPTY_DIRS_ to ‘TRUE’, or have a ‘BuildKeepEmpty’ field with a true value in the DESCRIPTION file).
The --resave-data option allows saved images (.rda and .RData files) in the data directory to be optimized for size. It will also compress tabular files and convert .R files to saved images. It can take values no, gzip (the default if this option is not supplied, which can be changed by setting the environment variable _R_BUILD_RESAVE_DATA_) and best (equivalent to giving it without a value), which chooses the most effective compression. Using best adds a dependence on R (>= 2.10) to the DESCRIPTION file if bzip2 or xz compression is selected for any of the files. If this is thought undesirable, --resave-data=gzip (which is the default if that option is not supplied) will do what compression it can with gzip. A package can control how its data is resaved by supplying a ‘BuildResaveData’ field (with one of the values given earlier in this paragraph) in its DESCRIPTION file.
The --compact-vignettes option will run tools::compactPDF over the PDF files in inst/doc (and its subdirectories) to losslessly compress them. This is not enabled by default (it can be selected by environment variable _R_BUILD_COMPACT_VIGNETTES_) and needs qpdf (http://qpdf.sourceforge.net/) to be available.
It can be useful to run R CMD check --check-subdirs=yes on the built tarball as a final check on the contents.
Where a non-POSIX file system is in use which does not utilize execute permissions, some care is needed with permissions. This applies on Windows and to e.g. FAT-formatted drives and SMB-mounted file systems on other OSes. The ‘mode’ of the file recorded in the tarball will be whatever file.info() returns. On Windows this will record only directories as having execute permission and on other OSes it is likely that all files have reported ‘mode’ 0777. A particular issue is packages being built on Windows which are intended to contain executable scripts such as configure and cleanup: R CMD build ensures those two are recorded with execute permission.
Directory build of the package sources is reserved for use by R CMD build: it contains information which may not easily be created when the package is installed, including index information on the vignettes and, rarely, information on the help pages and perhaps a copy of the PDF reference manual (see above).
Previous: Building package tarballs, Up: Checking and building packages [Contents][Index]
1.3.3 Building binary packages
Binary packages are compressed copies of installed versions of packages. They contain compiled shared libraries rather than C, C++ or Fortran source code, and the R functions are included in their installed form. The format and filename are platform-specific; for example, a binary package for Windows is usually supplied as a .zip file, and for the macOS platform the default binary package file extension is .tgz.
The recommended method of building binary packages is to use
R CMD INSTALL --build pkg where pkg is either the name of a source tarball (in the usual .tar.gz format) or the location of the directory of the package source to be built. This operates by first installing the package and then packing the installed binaries into the appropriate binary package file for the particular platform.
By default, R CMD INSTALL --build will attempt to install the package into the default library tree for the local installation of R. This has two implications:
If the installation is successful, it will overwrite any existing installation of the same package.
The default library tree must have write permission; if not, the package will not install and the binary will not be created.
To prevent changes to the present working installation or to provide an install location with write access, create a suitably located directory with write access and use the -l option to build the package in the chosen location. The usage is then
R CMD INSTALL -l location --build pkg
where location is the chosen directory with write access. The package will be installed as a subdirectory of location, and the package binary will be created in the current directory.
Other options for R CMD INSTALL can be found using R CMD INSTALL --help, and platform-specific details for special cases are discussed in the platform-specific FAQs.
Finally, at least one web-based service is available for building binary packages from (checked) source code: WinBuilder (see https://win-builder.R-project.org/) is able to build Windows binaries. Note that this is intended for developers on other platforms who do not have access to Windows but wish to provide binaries for the Windows platform.
Next: Package namespaces, Previous: Checking and building packages, Up: Creating R packages [Contents][Index]
1.4 Writing package vignettes
In addition to the help files in Rd format, R packages allow the inclusion of documents in arbitrary other formats. The standard location for these is subdirectory inst/doc of a source package, the contents will be copied to subdirectory doc when the package is installed. Pointers from package help indices to the installed documents are automatically created. Documents in inst/doc can be in arbitrary format, however we strongly recommend providing them in PDF format, so users on almost all platforms can easily read them. To ensure that they can be accessed from a browser (as an HTML index is provided), the file names should start with an ASCII letter and be comprised entirely of ASCII letters or digits or hyphen or underscore.
A special case is package vignettes. Vignettes are documents in PDF or HTML format obtained from plain-text literate source files from which R knows how to extract R code and create output (in PDF/HTML or intermediate LaTeX). Vignette engines do this work, using “tangle” and “weave” functions respectively. Sweave, provided by the R distribution, is the default engine. Other vignette engines besides Sweave are supported; see Non-Sweave vignettes.
Package vignettes have their sources in subdirectory vignettes of the package sources. Note that the location of the vignette sources only affects R CMD build and R CMD check: the tarball built by R CMD build includes in inst/doc the components intended to be installed.
Sweave vignette sources are normally given the file extension .Rnw or .Rtex, but for historical reasons extensions58 .Snw and .Stex are also recognized. Sweave allows the integration of LaTeX documents: see the Sweave help page in R and the Sweave vignette in package utils for details on the source document format.
Package vignettes are tested by R CMD check by executing all R code chunks they contain (except those marked for non-evaluation, e.g., with option eval=FALSE for Sweave). The R working directory for all vignette tests in R CMD check is a copy of the vignette source directory. Make sure all files needed to run the R code in the vignette (data sets, …) are accessible by either placing them in the inst/doc hierarchy of the source package or by using calls to system.file(). All other files needed to re-make the vignettes (such as LaTeX style files, BibTeX input files and files for any figures not created by running the code in the vignette) must be in the vignette source directory. R CMD check will check that vignette production has succeeded by comparing modification times of output files in inst/doc with the source in vignettes.
R CMD build will automatically59 create the (PDF or HTML versions of the) vignettes in inst/doc for distribution with the package sources. By including the vignette outputs in the package sources it is not necessary that these can be re-built at install time, i.e., the package author can use private R packages, screen snapshots and LaTeX extensions which are only available on their machine.60
By default R CMD build will run Sweave on all Sweave vignette source files in vignettes. If Makefile is found in the vignette source directory, then R CMD build will try to run make after the Sweave runs, otherwise texi2pdf is run on each .tex file produced.
The first target in the Makefile should take care of both creation of PDF/HTML files and cleaning up afterwards (including after Sweave), i.e., delete all files that shall not appear in the final package archive. Note that if the make step runs R it needs to be careful to respect the environment values of R_LIBS and R_HOME61. Finally, if there is a Makefile and it has a ‘clean:’ target, make clean is run.
All the usual caveats about including a Makefile apply. It must be portable (no GNU extensions), use LF line endings and must work correctly with a parallel make: too many authors have written things like
## BAD EXAMPLE
all: pdf clean
pdf: ABC-intro.pdf ABC-details.pdf
%.pdf: %.tex
texi2dvi --pdf $*
clean:
rm *.tex ABC-details-*.pdf
which will start removing the source files whilst pdflatex is working.
Metadata lines can be placed in the source file, preferably in LaTeX comments in the preamble. One such is a \VignetteIndexEntry of the form
%\VignetteIndexEntry{Using Animal}
Others you may see are \VignettePackage (currently ignored), \VignetteDepends and \VignetteKeyword (which replaced \VignetteKeywords). These are processed at package installation time to create the saved data frame Meta/vignette.rds, but only the \VignetteIndexEntry and \VignetteKeyword statements are currently used. The \VignetteEngine statement is described in Non-Sweave vignettes.
At install time an HTML index for all vignettes in the package is automatically created from the \VignetteIndexEntry statements unless a file index.html exists in directory inst/doc. This index is linked from the HTML help index for the package. If you do supply a inst/doc/index.html file it should contain relative links only to files under the installed doc directory, or perhaps (not really an index) to HTML help files or to the DESCRIPTION file, and be valid HTML as confirmed via the W3C Markup Validation Service or Validator.nu.
Sweave/Stangle allows the document to specify the split=TRUE option to create a single R file for each code chunk: this will not work for vignettes where it is assumed that each vignette source generates a single file with the vignette extension replaced by .R.
Do watch that PDFs are not too large – one in a CRAN package was 72MB! This is usually caused by the inclusion of overly detailed figures, which will not render well in PDF viewers. Sometimes it is much better to generate fairly high resolution bitmap (PNG, JPEG) figures and include those in the PDF document.
When R CMD build builds the vignettes, it copies these and the vignette sources from directory vignettes to inst/doc. To install any other files from the vignettes directory, include a file vignettes/.install_extras which specifies these as Perl-like regular expressions on one or more lines. (See the description of the .Rinstignore file for full details.)
Encodings and vignettes
Non-Sweave vignettes
Next: Non-Sweave vignettes, Previous: Writing package vignettes, Up: Writing package vignettes [Contents][Index]
1.4.1 Encodings and vignettes
Vignettes will in general include descriptive text, R input, R output and figures, LaTeX include files and bibliographic references. As any of these may contain non-ASCII characters, the handling of encodings can become very complicated.
The vignette source file should be written in ASCII or contain a declaration of the encoding (see below). This applies even to comments within the source file, since vignette engines process comments to look for options and metadata lines. When an engine’s weave and tangle functions are called on the vignette source, it will be converted to the encoding of the current R session.
Stangle() will produce an R code file in the current locale’s encoding: for a non-ASCII vignette what that is is recorded in a comment at the top of the file.
Sweave() will produce a .tex file in the current encoding, or in UTF-8 if that is declared. Non-ASCII encodings need to be declared to LaTeX via a line like
\usepackage[utf8]{inputenc}
(It is also possible to use the more recent ‘inputenx’ LaTeX package.) For files where this line is not needed (e.g. chapters included within the body of a larger document, or non-Sweave vignettes), the encoding may be declared using a comment like
%\VignetteEncoding{UTF-8}
If the encoding is UTF-8, this can also be declared using the declaration
%\SweaveUTF8
If no declaration is given in the vignette, it will be assumed to be in the encoding declared for the package. If there is no encoding declared in either place, then it is an error to use non-ASCII characters in the vignette.
In any case, be aware that LaTeX may require the ‘usepackage’ declaration.
Sweave() will also parse and evaluate the R code in each chunk. The R output will also be in the current locale (or UTF-8 if so declared), and should be covered by the ‘inputenc’ declaration. One thing people often forget is that the R output may not be ASCII even for ASCII R sources, for many possible reasons. One common one is the use of ‘fancy’ quotes: see the R help on sQuote: note carefully that it is not portable to declare UTF-8 or CP1252 to cover such quotes, as their encoding will depend on the locale used to run Sweave(): this can be circumvented by setting options(useFancyQuotes="UTF-8") in the vignette.
The final issue is the encoding of figures – this applies only to PDF figures and not PNG etc. The PDF figures will contain declarations for their encoding, but the Sweave option pdf.encoding may need to be set appropriately: see the help for the pdf() graphics device.
As a real example of the complexities, consider the fortunes package version ‘1.4-0’. That package did not have a declared encoding, and its vignette was in ASCII. However, the data it displays are read from a UTF-8 CSV file and will be assumed to be in the current encoding, so fortunes.tex will be in UTF-8 in any locale. Had read.table been told the data were UTF-8, fortunes.tex would have been in the locale’s encoding.
Previous: Encodings and vignettes, Up: Writing package vignettes [Contents][Index]
1.4.2 Non-Sweave vignettes
Vignettes in formats other than Sweave are supported via “vignette engines”. For example knitr version 1.1 or later can create .tex files from a variation on Sweave format, and .html files from a variation on “markdown” format. These engines replace the Sweave() function with other functions to convert vignette source files into LaTeX files for processing into .pdf, or directly into .pdf or .html files. The Stangle() function is replaced with a function that extracts the R source from a vignette.
R recognizes non-Sweave vignettes using filename extensions specified by the engine. For example, the knitr package supports the extension .Rmd (standing for “R markdown”). The user indicates the vignette engine within the vignette source using a \VignetteEngine line, for example
%\VignetteEngine{knitr::knitr}
This specifies the name of a package and an engine to use in place of Sweave in processing the vignette. As Sweave is the only engine supplied with the R distribution, the package providing any other engine must be specified in the ‘VignetteBuilder’ field of the package DESCRIPTION file, and also specified in the ‘Suggests’, ‘Imports’ or ‘Depends’ field (since its namespace must be available to build or check your package). If more than one package is specified as a builder, they will be searched in the order given there. The utils package is always implicitly appended to the list of builder packages, but may be included earlier to change the search order.
Note that a package with non-Sweave vignettes should always have a ‘VignetteBuilder’ field in the DESCRIPTION file, since this is how R CMD check recognizes that there are vignettes to be checked: packages listed there are required when the package is checked.
The vignette engine can produce .tex, .pdf, or .html files as output. If it produces .tex files, R will call texi2pdf to convert them to .pdf for display to the user (unless there is a Makefile in the vignettes directory).
Package writers who would like to supply vignette engines need to register those engines in the package .onLoad function. For example, that function could make the call
tools::vignetteEngine("knitr", weave = vweave, tangle = vtangle,
pattern = "[.]Rmd$", package = "knitr")
(The actual registration in knitr is more complicated, because it supports other input formats.) See the ?tools::vignetteEngine help topic for details on engine registration.
Next: Writing portable packages, Previous: Writing package vignettes, Up: Creating R packages [Contents][Index]
1.5 Package namespaces
R has a namespace management system for code in packages. This system allows the package writer to specify which variables in the package should be exported to make them available to package users, and which variables should be imported from other packages.
The namespace for a package is specified by the NAMESPACE file in the top level package directory. This file contains namespace directives describing the imports and exports of the namespace. Additional directives register any shared objects to be loaded and any S3-style methods that are provided. Note that although the file looks like R code (and often has R-style comments) it is not processed as R code. Only very simple conditional processing of if statements is implemented.
Packages are loaded and attached to the search path by calling library or require. Only the exported variables are placed in the attached frame. Loading a package that imports variables from other packages will cause these other packages to be loaded as well (unless they have already been loaded), but they will not be placed on the search path by these implicit loads. Thus code in the package can only depend on objects in its own namespace and its imports (including the base namespace) being visible62.
Namespaces are sealed once they are loaded. Sealing means that imports and exports cannot be changed and that internal variable bindings cannot be changed. Sealing allows a simpler implementation strategy for the namespace mechanism and allows code analysis and compilation tools to accurately identify the definition corresponding to a global variable reference in a function body.
The namespace controls the search strategy for variables used by functions in the package. If not found locally, R searches the package namespace first, then the imports, then the base namespace and then the normal search path (so the base namespace precedes the normal search rather than being at the end of it).
Specifying imports and exports
Registering S3 methods
Load hooks
useDynLib
An example
Namespaces with S4 classes and methods
Next: Registering S3 methods, Previous: Package namespaces, Up: Package namespaces [Contents][Index]
1.5.1 Specifying imports and exports
Exports are specified using the export directive in the NAMESPACE file. A directive of the form
export(f, g)
specifies that the variables f and g are to be exported. (Note that variable names may be quoted, and reserved words and non-standard names such as [<-.fractions must be.)
For packages with many variables to export it may be more convenient to specify the names to export with a regular expression using exportPattern. The directive
exportPattern("^[^\\.]")
exports all variables that do not start with a period. However, such broad patterns are not recommended for production code: it is better to list all exports or use narrowly-defined groups. (This pattern applies to S4 classes.) Beware of patterns which include names starting with a period: some of these are internal-only variables and should never be exported, e.g. ‘.__S3MethodsTable__.’ (and loading excludes known cases).
Packages implicitly import the base namespace. Variables exported from other packages with namespaces need to be imported explicitly using the directives import and importFrom. The import directive imports all exported variables from the specified package(s). Thus the directives
import(foo, bar)
specifies that all exported variables in the packages foo and bar are to be imported. If only some of the exported variables from a package are needed, then they can be imported using importFrom. The directive
importFrom(foo, f, g)
specifies that the exported variables f and g of the package foo are to be imported. Using importFrom selectively rather than import is good practice and recommended notably when importing from packages with more than a dozen exports and especially from those written by others (so what they export can change in future).
To import every symbol from a package but for a few exceptions, pass the except argument to import. The directive
import(foo, except=c(bar, baz))
imports every symbol from foo except bar and baz. The value of except should evaluate to something coercible to a character vector, after substituting each symbol for its corresponding string.
It is possible to export variables from a namespace which it has imported from other namespaces: this has to be done explicitly and not via exportPattern.
If a package only needs a few objects from another package it can use a fully qualified variable reference in the code instead of a formal import. A fully qualified reference to the function f in package foo is of the form foo::f. This is slightly less efficient than a formal import and also loses the advantage of recording all dependencies in the NAMESPACE file (but they still need to be recorded in the DESCRIPTION file). Evaluating foo::f will cause package foo to be loaded, but not attached, if it was not loaded already—this can be an advantage in delaying the loading of a rarely used package.
Using foo:::f instead of foo::f allows access to unexported objects. This is generally not recommended, as the semantics of unexported objects may be changed by the package author in routine maintenance.
Next: Load hooks, Previous: Specifying imports and exports, Up: Package namespaces [Contents][Index]
1.5.2 Registering S3 methods
The standard method for S3-style UseMethod dispatching might fail to locate methods defined in a package that is imported but not attached to the search path. To ensure that these methods are available the packages defining the methods should ensure that the generics are imported and register the methods using S3method directives. If a package defines a function print.foo intended to be used as a print method for class foo, then the directive
S3method(print, foo)
ensures that the method is registered and available for UseMethod dispatch, and the function print.foo does not need to be exported. Since the generic print is defined in base it does not need to be imported explicitly.
(Note that function and class names may be quoted, and reserved words and non-standard names such as [<- and function must be.)
It is possible to specify a third argument to S3method, the function to be used as the method, for example
S3method(print, check_so_symbols, .print.via.format)
when print.check_so_symbols is not needed.
As from R 3.6.0 one can also use S3method() directives to perform delayed registration. With
if(getRversion() >= "3.6.0") {
S3method(pkg::gen, cls)
}
function gen.cls will get registered as an S3 method for class cls and generic gen from package pkg only when the namespace of pkg is loaded. This can be employed to deal with situations where the method is not “immediately” needed, and having to pre-load the namespace of pkg (and all its strong dependencies) in order to perform immediate registration is considered too onerous.
Next: useDynLib, Previous: Registering S3 methods, Up: Package namespaces [Contents][Index]
1.5.3 Load hooks
There are a number of hooks called as packages are loaded, attached, detached, and unloaded. See help(".onLoad") for more details.
Since loading and attaching are distinct operations, separate hooks are provided for each. These hook functions are called .onLoad and .onAttach. They both take arguments63 libname and pkgname; they should be defined in the namespace but not exported.
Packages can use a .onDetach or .Last.lib function (provided the latter is exported from the namespace) when detach is called on the package. It is called with a single argument, the full path to the installed package. There is also a hook .onUnload which is called when the namespace is unloaded (via a call to unloadNamespace, perhaps called by detach(unload = TRUE)) with argument the full path to the installed package’s directory. Functions .onUnload and .onDetach should be defined in the namespace and not exported, but .Last.lib does need to be exported.
Packages are not likely to need .onAttach (except perhaps for a start-up banner); code to set options and load shared objects should be placed in a .onLoad function, or use made of the useDynLib directive described next.
User-level hooks are also available: see the help on function setHook.
These hooks are often used incorrectly. People forget to export .Last.lib. Compiled code should be loaded in .onLoad (or via a useDynLb directive: see below) and unloaded in .onUnload. Do remember that a package’s namespace can be loaded without the namespace being attached (e.g. by pkgname::fun) and that a package can be detached and re-attached whilst its namespace remains loaded.
Next: An example, Previous: Load hooks, Up: Package namespaces [Contents][Index]
1.5.4 useDynLib
A NAMESPACE file can contain one or more useDynLib directives which allows shared objects that need to be loaded.64 The directive
useDynLib(foo)
registers the shared object foo65 for loading with library.dynam. Loading of registered object(s) occurs after the package code has been loaded and before running the load hook function. Packages that would only need a load hook function to load a shared object can use the useDynLib directive instead.
The useDynLib directive also accepts the names of the native routines that are to be used in R via the .C, .Call, .Fortran and .External interface functions. These are given as additional arguments to the directive, for example,
useDynLib(foo, myRoutine, myOtherRoutine)
By specifying these names in the useDynLib directive, the native symbols are resolved when the package is loaded and R variables identifying these symbols are added to the package’s namespace with these names. These can be used in the .C, .Call, .Fortran and .External calls in place of the name of the routine and the PACKAGE argument. For instance, we can call the routine myRoutine from R with the code
.Call(myRoutine, x, y)
rather than
.Call("myRoutine", x, y, PACKAGE = "foo")
There are at least two benefits to this approach. Firstly, the symbol lookup is done just once for each symbol rather than each time the routine is invoked. Secondly, this removes any ambiguity in resolving symbols that might be present in more than one DLL. However, this approach is nowadays deprecated in favour of supplying registration information (see below).
In some circumstances, there will already be an R variable in the package with the same name as a native symbol. For example, we may have an R function in the package named myRoutine. In this case, it is necessary to map the native symbol to a different R variable name. This can be done in the useDynLib directive by using named arguments. For instance, to map the native symbol name myRoutine to the R variable myRoutine_sym, we would use
useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)
We could then call that routine from R using the command
.Call(myRoutine_sym, x, y)
Symbols without explicit names are assigned to the R variable with that name.
In some cases, it may be preferable not to create R variables in the package’s namespace that identify the native routines. It may be too costly to compute these for many routines when the package is loaded if many of these routines are not likely to be used. In this case, one can still perform the symbol resolution correctly using the DLL, but do this each time the routine is called. Given a reference to the DLL as an R variable, say dll, we can call the routine myRoutine using the expression
.Call(dll$myRoutine, x, y)
The $ operator resolves the routine with the given name in the DLL using a call to getNativeSymbol. This is the same computation as above where we resolve the symbol when the package is loaded. The only difference is that this is done each time in the case of dll$myRoutine.
In order to use this dynamic approach (e.g., dll$myRoutine), one needs the reference to the DLL as an R variable in the package. The DLL can be assigned to a variable by using the variable = dllName format used above for mapping symbols to R variables. For example, if we wanted to assign the DLL reference for the DLL foo in the example above to the variable myDLL, we would use the following directive in the NAMESPACE file:
myDLL = useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)
Then, the R variable myDLL is in the package’s namespace and available for calls such as myDLL$dynRoutine to access routines that are not explicitly resolved at load time.
If the package has registration information (see Registering native routines), then we can use that directly rather than specifying the list of symbols again in the useDynLib directive in the NAMESPACE file. Each routine in the registration information is specified by giving a name by which the routine is to be specified along with the address of the routine and any information about the number and type of the parameters. Using the .registration argument of useDynLib, we can instruct the namespace mechanism to create R variables for these symbols. For example, suppose we have the following registration information for a DLL named myDLL:
static R_NativePrimitiveArgType foo_t[] = {
REALSXP, INTSXP, STRSXP, LGLSXP
};
static const R_CMethodDef cMethods[] = {
{"foo", (DL_FUNC) &foo, 4, foo_t},
{"bar_sym", (DL_FUNC) &bar, 0},
{NULL, NULL, 0, NULL}
};
static const R_CallMethodDef callMethods[] = {
{"R_call_sym", (DL_FUNC) &R_call, 4},
{"R_version_sym", (DL_FUNC) &R_version, 0},
{NULL, NULL, 0}
};
Then, the directive in the NAMESPACE file
useDynLib(myDLL, .registration = TRUE)
causes the DLL to be loaded and also for the R variables foo, bar_sym, R_call_sym and R_version_sym to be defined in the package’s namespace.
Note that the names for the R variables are taken from the entry in the registration information and do not need to be the same as the name of the native routine. This allows the creator of the registration information to map the native symbols to non-conflicting variable names in R, e.g. R_version to R_version_sym for use in an R function such as
R_version <- function()
{
.Call(R_version_sym)
}
Using argument .fixes allows an automatic prefix to be added to the registered symbols, which can be useful when working with an existing package. For example, package KernSmooth has
useDynLib(KernSmooth, .registration = TRUE, .fixes = "F_")
which makes the R variables corresponding to the Fortran symbols F_bkde and so on, and so avoid clashes with R code in the namespace.
NB: Using these arguments for a package which does not register native symbols merely slows down the package loading (although many CRAN packages have done so). Once symbols are registered, check that the corresponding R variables are not accidentally exported by a pattern in the NAMESPACE file.
Next: Namespaces with S4 classes and methods, Previous: useDynLib, Up: Package namespaces [Contents][Index]
1.5.5 An example
As an example consider two packages named foo and bar. The R code for package foo in file foo.R is
x <- 1
f <- function(y) c(x,y)
foo <- function(x) .Call("foo", x, PACKAGE="foo")
print.foo <- function(x, ...) cat("<a foo>\n")
Some C code defines a C function compiled into DLL foo (with an appropriate extension). The NAMESPACE file for this package is
useDynLib(foo)
export(f, foo)
S3method(print, foo)
The second package bar has code file bar.R
c <- function(...) sum(...)
g <- function(y) f(c(y, 7))
h <- function(y) y+9
and NAMESPACE file
import(foo)
export(g, h)
Calling library(bar) loads bar and attaches its exports to the search path. Package foo is also loaded but not attached to the search path. A call to g produces
> g(6)
[1] 1 13
This is consistent with the definitions of c in the two settings: in bar the function c is defined to be equivalent to sum, but in foo the variable c refers to the standard function c in base.
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1.5.6 Namespaces with S4 classes and methods
Some additional steps are needed for packages which make use of formal (S4-style) classes and methods (unless these are purely used internally). The package should have Depends: methods in its DESCRIPTION and import(methods) or importFrom(methods, ...) plus any classes and methods which are to be exported need to be declared in the NAMESPACE file. For example, the stats4 package has
export(mle) # exporting methods implicitly exports the generic
importFrom("stats", approx, optim, pchisq, predict, qchisq, qnorm, spline)
## For these, we define methods or (AIC, BIC, nobs) an implicit generic:
importFrom("stats", AIC, BIC, coef, confint, logLik, nobs, profile,
update, vcov)
exportClasses(mle, profile.mle, summary.mle)
## All methods for imported generics:
exportMethods(coef, confint, logLik, plot, profile, summary,
show, update, vcov)
## implicit generics which do not have any methods here
export(AIC, BIC, nobs)
All S4 classes to be used outside the package need to be listed in an exportClasses directive. Alternatively, they can be specified using exportClassPattern66 in the same style as for exportPattern. To export methods for generics from other packages an exportMethods directive can be used.
Note that exporting methods on a generic in the namespace will also export the generic, and exporting a generic in the namespace will also export its methods. If the generic function is not local to this package, either because it was imported as a generic function or because the non-generic version has been made generic solely to add S4 methods to it (as for functions such as coef in the example above), it can be declared via either or both of export or exportMethods, but the latter is clearer (and is used in the stats4 example above). In particular, for primitive functions there is no generic function, so export would export the primitive, which makes no sense. On the other hand, if the generic is local to this package, it is more natural to export the function itself using export(), and this must be done if an implicit generic is created without setting any methods for it (as is the case for AIC in stats4).
A non-local generic function is only exported to ensure that calls to the function will dispatch the methods from this package (and that is not done or required when the methods are for primitive functions). For this reason, you do not need to document such implicitly created generic functions, and undoc in package tools will not report them.
If a package uses S4 classes and methods exported from another package, but does not import the entire namespace of the other package67, it needs to import the classes and methods explicitly, with directives
importClassesFrom(package, ...)
importMethodsFrom(package, ...)
listing the classes and functions with methods respectively. Suppose we had two small packages A and B with B using A. Then they could have NAMESPACE files
export(f1, ng1)
exportMethods("[")
exportClasses(c1)
and
importFrom(A, ng1)
importClassesFrom(A, c1)
importMethodsFrom(A, f1)
export(f4, f5)
exportMethods(f6, "[")
exportClasses(c1, c2)
respectively.
Note that importMethodsFrom will also import any generics defined in the namespace on those methods.
It is important if you export S4 methods that the corresponding generics are available. You may for example need to import coef from stats to make visible a function to be converted into its implicit generic. But it is better practice to make use of the generics exported by stats4 as this enables multiple packages to unambiguously set methods on those generics.
Next: Diagnostic messages, Previous: Package namespaces, Up: Creating R packages [Contents][Index]
1.6 Writing portable packages
This section contains advice on writing packages to be used on multiple platforms or for distribution (for example to be submitted to a package repository such as CRAN).
Portable packages should have simple file names: use only alphanumeric ASCII characters and period (.), and avoid those names not allowed under Windows (see Package structure).
Many of the graphics devices are platform-specific: even X11() (aka x11()) which although emulated on Windows may not be available on a Unix-alike (and is not the preferred screen device on OS X). It is rarely necessary for package code or examples to open a new device, but if essential,68 use dev.new().
Use R CMD build to make the release .tar.gz file.
R CMD check provides a basic set of checks, but often further problems emerge when people try to install and use packages submitted to CRAN – many of these involve compiled code. Here are some further checks that you can do to make your package more portable.
If your package has a configure script, provide a configure.win script to be used on Windows (an empty file if no actions are needed).
If your package has a Makevars or Makefile file, make sure that you use only portable make features. Such files should be LF-terminated69 (including the final line of the file) and not make use of GNU extensions. (The POSIX specification is available at https://pubs.opengroup.org/onlinepubs/9699919799/utilities/make.html; anything not documented there should be regarded as an extension to be avoided. Further advice can be found at https://www.gnu.org/software/autoconf/manual/autoconf.html#Portable-Make. ) Commonly misused GNU extensions are conditional inclusions (ifeq and the like), ${shell ...}, ${wildcard ...} and similar, and the use of +=70 and :=. Also, the use of $< other than in implicit rules is a GNU extension, as is the $^ macro. As is the use of .PHONY (some other makes ignore it). Unfortunately makefiles which use GNU extensions often run on other platforms but do not have the intended results.
Note that the -C flag for make is not included in the POSIX specification and is not implemented by some of the makes used with R.
The use of ${shell ...} can be avoided by using backticks, e.g.
PKG_CPPFLAGS = `gsl-config --cflags`
which works in all versions of make known71 to be used with R.
If you really must require GNU make, declare it in the DESCRIPTION file by
SystemRequirements: GNU make
and ensure that you use the value of environment variable MAKE (and not just make) in your scripts. (On some platforms GNU make is available under a name such as gmake, and there SystemRequirements is used to set MAKE.)
If you only need GNU make for parts of the package which are rarely needed (for example to create bibliography files under vignettes), use a file called GNUmakefile rather than Makefile as GNU make (only) will use the former.
macOS has used GNU make for many years (it previously used BSD make), but the version has been frozen at 3.81 (from 2006).
Since the only viable make for Windows is GNU make, it is permissible to use GNU extensions in files Makevars.win or Makefile.win.
If you use src/Makevars to compile code in a subdirectory, ensure that you have followed all the advice above. In particular
Anticipate a parallel make. See Using Makevars.
Pass macros down to the makefile in the subdirectory, including all the needed compiler flags (including PIC and visibility flags). If they are used in the subdirectory’s Makefile, this includes macros ‘AR’ and ‘RANLIB’. See Compiling in sub-directories, which has a C example. A C++ example:
pkg/libpkg.a:
(cd pkg && $(MAKE) -f make_pkg libpkg.a \
CXX="$(CXX)" CXXFLAGS="$(CXXFLAGS) $(CXXPICFLAGS) $(C_VISIBILITY)" \
AR="$(AR)" RANLIB="$(RANLIB)")
Ensure that cleanup will be performed by R CMD build, for example in a cleanup script or a ‘clean’ target.
If your package uses a src/Makefile file to compile code to be linked into R, ensure that it uses exactly the same compiler and flag settings that R uses when compiling such code: people often forget ‘PIC’ flags. If R CMD config is used, this needs something like (for C++)
RBIN = `"${R_HOME}/bin/R"`
CXX = `"${RBIN}" CMD config CXX`
CXXFLAGS = `"${RBIN}" CMD config CXXFLAGS` `"${RBIN}" CMD config CXXPICFLAGS`
Names of source files including = (such as src/complex_Sig=gen.c) will confuse some make programs and should be avoided.
Bash extensions also need to be avoided in shell scripts, including expressions in Makefiles (which are passed to the shell for processing). Some R platforms use strict72 Bourne shells: an earlier R toolset on Windows73 and some Unix-alike OSes use ash (https://en.wikipedia.org/wiki/Almquist_shell), a ‘slim’ shell with few builtins or variants such as dash. Beware of assuming that all the POSIX command-line utilities are available, especially on Windows where only a subset (which has changed by version of Rtools) is provided for use with R. One particular issue is the use of echo, for which two behaviours are allowed (https://pubs.opengroup.org/onlinepubs/9699919799/utilities/echo.html) and both have occurred as defaults on R platforms: portable applications should use neither -n (as the first argument) nor escape sequences. The recommended replacement for echo -n is the command printf. Another common issue is the construction
export FOO=value
which is bash-specific (first set the variable then export it by name).
Using test -e (or [ -e ]) in shell scripts is not fully portable74: -f is normally what is intended. Flags -a and -o are nowadays declared obsolescent by POSIX and should not be used.
Use of ‘brace expansion’, e.g.,
rm -f src/*.{o,so,d}
is not portable.
The -o flag for set in shell scripts is optional in POSIX and not supported on all the platforms R is used on.
The variable ‘OSTYPE’ is shell-specific and its values are rather unpredictable and may include a version such as ‘darwin19.0’: `uname` is often what is intended (with common values ‘Darwin’, ‘Linux’ and ‘SunOS’).
On macOS which shell /bin/sh invokes is user- and platform-dependent: it might be bash version 3.2, dash or zsh (for new accounts it is zsh, for accounts ported from Mojave or earlier it is usually bash).
Make use of the abilities of your compilers to check the standards-conformance of your code. For example, gcc and gfortran75 can be used with options -Wall -pedantic to alert you to potential problems. This is particularly important for C++, where g++ -Wall -pedantic will alert you to the use of some of the GNU extensions which fail to compile on most other C++ compilers. If R was not configured accordingly, one can achieve this via personal Makevars files. See Customizing package compilation in R Installation and Administration ,
Portable C++ code needs to follow both the 2011 and 2014 standards or to specify C+11/14/17/20 where available (which is not the case on all R platforms). Currently C++17/20 support is patchy across R platforms.
If using Fortran with the GNU compiler, use the flags -std=f95 -Wall -pedantic which reject most GNU extensions and features from later standards. (Although R only requires Fortran 90, gfortran does not have a way to specify that standard.)
R has tested that DOUBLE COMPLEX works and so is preferred to COMPLEX*16. (One can also use something like COMPLEX(KIND=KIND(0.0D0)).)
The use of Fortran types such as REAL(KIND=8) is very far from portable. According of the standards this merely enumerates different supported types, so DOUBLE PRECISION might be REAL(KIND=3) (and is on an actual compiler). Even if for a particular compiler the value indicates the size in bytes, which values are supported is platform-specific — for example gfortran supports values of 4 and 8 on all current platforms and 10 and 16 on a few (but not for example on ‘arm’ CPUs).
Not all common R platforms conform to the expected standards, e.g. C99 for C code. One common area of problems is the *printf functions where Windows does not support %lld, %Lf and similar formats (and has its own formats such as %I64d for 64-bit integers). It is very rare to need to output such types, and 64-bit integers can usually be converted to doubles for output. However, the C11 standard (section 7.8.1) includes PRIxNN macros76 in C header inttypes.h (for example PRId64) so the portable approach is to test for these and if not available provide emulations in the package.
As from macOS 11 (late 2020), its C compiler sets the flag -Werror=implicit-function-declaration by default which forces stricter conformance to C99. This can be used on other platforms with gcc or clang. If your package has a (autoconf-generated) configure script, try installing it whilst using this flag, and read through the config.log file — compilation warnings and errors can lead to features which are present not being detected. (If possible do this on several platforms.)
R CMD check performs some checks for non-portable compiler/linker flags in src/Makevars. However, it cannot check the meaning of such flags, and some are commonly accepted but with compiler-specific meanings. There are other non-portable flags which are not checked, nor are src/Makefile files and makefiles in sub-directories. As a comment in the code says
It is hard to think of anything apart from -I* and -D* that is safe for general use …
although -pthread is pretty close to portable. (Option -U is portable but little use on the command line as it will only cancel built-in defines (not portable) and those defined earlier on the command line (R does not use any).)
People have used configure to customize src/Makevars, including for specific compilers. This is unsafe for several reasons. First, unintended compilers might meet the check—for example, several compilers other than GCC identify themselves as ‘GCC’ whilst being only partially conformant. Second, future versions of compilers may behave differently (including updates to quite old series) so for example -Werror (and specializations) can make a package non-installable under a future version. Third, using flags to suppress diagnostic messages can hide important information for debugging on a platform not tested by the package maintainer. (R CMD check can optionally report on unsafe flags which were used.)
Avoid the use of -march and especially -march=native. This allows the compiler to generate code that will only run on a particular class of CPUs (that of the compiling machine for ‘native’). People assume this is a ‘minimum’ CPU specification, but that is not how it is documented for gcc (it is accepted by clang but apparently it is undocumented what precisely it does, and it can be accepted and may be ignored for other compilers). (For personal use -mtune is safer, but still not portable enough to be used in a public package.) Not even gcc supports ‘native’ for all CPUs, and it can do surprising things if it finds a CPU released later than its version.
Do be very careful with passing arguments between R, C and Fortran code. In particular, long in C will be 32-bit on some R platforms (including 64-bit Windows), but 64-bit on most modern Unix and Linux platforms. It is rather unlikely that the use of long in C code has been thought through: if you need a longer type than int you should use a configure test for a C99/C++11 type such as int_fast64_t (and failing that, long long) and typedef your own type, or use another suitable type (such as size_t, but beware that is unsigned and ssize_t is not portable).
It is not safe to assume that long and pointer types are the same size, and they are not on 64-bit Windows. If you need to convert pointers to and from integers use the C99/C++11 integer types intptr_t and uintptr_t (in the headers <stdint.h> and <cstdint>: they are not required to be implemented by the standards but are used in C code by R itself).
Note that integer in Fortran corresponds to int in C on all R platforms.
Under no circumstances should your compiled code ever call abort or exit77: these terminate the user’s R process, quite possibly losing all unsaved work. One usage that could call abort is the assert macro in C or C++ functions, which should never be active in production code. The normal way to ensure that is to define the macro NDEBUG, and R CMD INSTALL does so as part of the compilation flags. If you wish to use assert during development. you can include -UNDEBUG in PKG_CPPFLAGS. Note that your own src/Makefile or makefiles in sub-directories may also need to define NDEBUG.
This applies not only to your own code but to any external software you compile in or link to.
Compiled code should not write to stdout or stderr and C++ and Fortran I/O should not be used. As with the previous item such calls may come from external software and may never be called, but package authors are often mistaken about that.
Compiled code should not call the system random number generators such as rand, drand48 and random78, but rather use the interfaces to R’s RNGs described in Random number generation. In particular, if more than one package initializes the system RNG (e.g. via srand), they will interfere with each other.
Nor should the C++11 random number library be used, nor any other third-party random number generators such as those in GSL.
Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. See Checking memory access for tools which can be used to look for this.
Many platforms will allow unsatisfied entry points in compiled code, but will crash the application (here R) if they are ever used. Some (notably Windows) will not. Looking at the output of
nm -pg mypkg.so
and checking if any of the symbols marked U is unexpected is a good way to avoid this.
Linkers have a lot of freedom in how to resolve entry points in dynamically-loaded code, so the results may differ by platform. One area that has caused grief is packages including copies of standard system software such as libz (especially those already linked into R). In the case in point, entry point gzgets was sometimes resolved against the old version compiled into the package, sometimes against the copy compiled into R and sometimes against the system dynamic library. The only safe solution is to rename the entry points in the copy in the package. We have even seen problems with entry point name myprintf, which is a system entry point79 on some Linux systems.
Conflicts between symbols in DLLs are handled in very platform-specific ways. Good ways to avoid trouble are to make as many symbols as possible static (check with nm -pg), and to use names which are clearly tied to your package (which also helps users if anything does go wrong). Note that symbol names starting with R_ are regarded as part of R’s namespace and should not be used in packages.
It is good practice for DLLs to register their symbols (see Registering native routines), restrict visibility (see Controlling visibility) and not allow symbol search (see Registering native routines). It should be possible for a DLL to have only one visible symbol, R_init_pkgname, on suitable platforms80, which would completely avoid symbol conflicts.
It is not portable to call compiled code in R or other packages via .Internal, .C, .Fortran, .Call or .External, since such interfaces are subject to change without notice and will probably result in your code terminating the R process.
Do not use (hard or symbolic) file links in your package sources. Where possible R CMD build will replace them by copies.
If you do not yourself have a Windows system, consider submitting your source package to WinBuilder (https://win-builder.r-project.org/) before distribution. If you need to check on an M1 Mac, there is a check service at https://mac.r-project.org/macbuilder/submit.html.
It is bad practice for package code to alter the search path using library, require or attach and this often does not work as intended. For alternatives, see Suggested packages and with.
Examples can be run interactively via example as well as in batch mode when checking. So they should behave appropriately in both scenarios, conditioning by interactive() the parts which need an operator or observer. For instance, progress bars81 are only appropriate in interactive use, as is displaying help pages or calling View() (see below).
Be careful with the order of entries in macros such as PKG_LIBS. Some linkers will re-order the entries, and behaviour can differ between dynamic and static libraries. Generally -L options should precede82 the libraries (typically specified by -l options) to be found from those directories, and libraries are searched once in the order they are specified. Not all linkers allow a space after -L .
Care is needed with the use of LinkingTo. This puts one or more directories on the include search path ahead of system headers but (prior to R 3.4.0) after those specified in the CPPFLAGS macro of the R build (which normally includes -I/usr/local/include, but most platforms ignore that and include it with the system headers).
Any confusion would be avoided by having LinkingTo headers in a directory named after the package. In any case, name conflicts of headers and directories under package include directories should be avoided, both between packages and between a package and system and third-party software.
The ar utility is often used in makefiles to make static libraries. Its modifier u is defined by POSIX but is disabled in GNU ar on some recent Linux distributions which use ‘deterministic mode’. The safest way to make a static library is to first remove any existing file of that name then use ar -cr and then ranlib if needed (which is system-dependent: on most systems83 ar always maintains a symbol table). The POSIX standard says options should be preceded by a hyphen (as in -cr), although most OSes accept them without. Note that on some systems ar -cr must have at least one file specified.
The strip utility is platform-specific (and CRAN prohibits removing debug symbols). For example the options --strip-debug and --strip-unneeded of the GNU version are not supported on macOS nor Solaris84: the POSIX standard for strip does not mention any options, and what calling it without options does is platform-dependent. Stripping a .so file could even prevent it being dynamically loaded into R on an untested platform.
ld -S invokes strip --strip-debug on GNU ld but is not portable: in particular on Solaris it does something completely different and takes an argument.
Some people have a need to set a locale. Locale names are not portable, and e.g. ‘fr_FR.utf8’ is commonly used on Linux but not accepted on either Solaris or macOS. ‘fr_FR.UTF-8’ is more portable, being accepted on recent Linux, AIX, FreeBSD, macOS and Solaris (at least). However, some Linux distributions micro-package, so locales defined by glibc (including these examples) may not be installed.
Avoid spaces in file names, not least as they can cause difficulties for external tools. An example was a package with a knitr vignette that used spaces in plot names: this caused some older versions of pandoc to fail with a baffling error message.
Non-ASCII filenames can also cause problems (particularly in non-UTF-8 locales).
Take care in naming LaTeX macros (also known as ‘commands’) in vignette sources: if these are also defined in a future version of one of the LaTeX packages used there will be a fatal error. One instance in 2021 was package ‘hyperref’ newly defining ‘\C’, ‘\F’, ‘\G’, ‘\U’ and ‘\textapprox’. If you are confident that your definitions will be the only ones relevant you can use ‘\renewcommand’ but it is better to use names clearly associated with your package.
Make sure that any version requirement for Java code is both declared in the ‘SystemRequirements’ field85 and tested at runtime (not least as the Java installation when the package is installed might not be the same as when the package is run and will not be for binary packages). Java 8 is available for fewer platforms than Java 7 was, and Java 11 for fewer still (at the time of writing, only ‘x86_64’ Linux, macOS, 64-bit Windows and 64-bit Solaris 11 from Oracle; Linux on several 64-bit CPUs, Intel-based macOS, 32- and 64-bit Windows and AIX from from https://adoptium.net/ and ‘arm64’ macOS from Zulu – several OSes provide builds of OpenJDK including FreeBSD and most Linux distributions).
When specifying a minimum Java version please use the official version names, which are (confusingly)
1.1 1.2 1.3 1.4 5.0 6 7 8 9 10 11 12 13 14 15 16 17 (announced 18 19 20)
and as from 2018 a year.month scheme such as ‘18.9’ is also in use. Fortunately only the integer values are likely to be relevant.
A suitable test for Java at least version 8 for packages using rJava would be something like
.jinit()
jv <- .jcall("java/lang/System", "S", "getProperty", "java.runtime.version")
if(substr(jv, 1L, 2L) == "1.") {
jvn <- as.numeric(paste0(strsplit(jv, "[.]")[[1L]][1:2], collapse = "."))
if(jvn < 1.8) stop("Java >= 8 is needed for this package but not available")
}
Java 9 changed the format of this string (which used to be something like ‘1.8.0_292-b10’); Java 11 gave jv as ‘11+28’ whereas Java 11.0.11 gives ‘11.0.11+9’. (https://openjdk.java.net/jeps/322 details the current scheme. Note that it is necessary to allow for pre-releases like ‘11-ea+22’.)
Note too that the compiler used to produce a jar can impose a minimum Java version, often resulting in an arcane message like
java.lang.UnsupportedClassVersionError: ... Unsupported major.minor version 52.0
(Where https://en.wikipedia.org/wiki/Java_class_file maps class-file version numbers to Java versions.) Compile with something like javac -target 1.6 to ensure this is avoided. (As from Java 8, javac defaults to compiling for Java 8. Versions as old as ‘1.6’ are already deprecated and will give a warning with Java 10’s javac.) Note this also applies to packages distributing (or even downloading) compiled Java code produced by others, so their requirements need to be checked (they are often not documented accurately) and accounted for. It should be possible to check the class-file version via command-line utility javap, if necessary after extracting the .class files from a .jar archive.
Some packages have stated a requirement on a particular JDK, but a package should only be requiring a JRE unless providing its own Java interface.
Java 8 is still in widespread use (and may remain so because of licence changes: although Oracle ended its support even for personal use at the end of 2020, OpenJDK has full support until 2022-03), but Java 7 was the latest provided by Oracle for several platforms.
A package with a hard-to-satisfy system requirement is by definition not portable, annoyingly so if this is not declared in the ‘SystemRequirements’ field. The most common example is the use of pandoc, which is only available for a very limited range of platforms (and has onerous requirements to install from source) and has capabilities86 that vary by build but are not documented. Several recent versions of pandoc for macOS did not work on R’s target of High Sierra (and this too was undocumented): at the time of writing ‘2.7.3’ and ‘2.14.0’ and later did. (Currently pandoc is only available from the official site for Intel macOS but that build works well enough on ‘arm64’ machines.) Another example is the Rust compilation system (cargo and rustc).
Usage of external commands should always be conditional on a test for presence (perhaps using Sys.which), as well as declared in the ‘SystemRequirements’ field. A package should pass its checks without warnings nor errors without the external command being present.
An external command can be a (possibly optional) requirement for an imported or suggested package but needed for examples, tests or vignettes in the package itself. Such usages should always be declared and conditional.
Interpreters for scripting languages such as Perl, Python and Ruby need to be declared as system requirements and used conditionally: for example macOS 10.16 was announced not to have them (but released as macOS 11 with them). Python 2 has passed end-of-life and been removed from many major distributions. Support for Rust cannot be assumed, and it does not even support all the R platforms used by CRAN.
Command cmake is not commonly installed, and where it is, it might not be on the path. In particular, the most common location on macOS is /Applications/CMake.app/Contents/bin/cmake and that should be looked for if cmake is not found on the path.
Be sure to use portable encoding names: none of utf8, mac and macroman is portable. See the help for file for more details.
Do not invoke R by plain R, Rscript or (on Windows) Rterm in your examples, tests, vignettes, makefiles or other scripts. As pointed out in several places earlier in this manual, use something like
"$(R_HOME)/bin/Rscript"
"$(R_HOME)/bin$(R_ARCH_BIN)/Rterm"
with appropriate quotes (as, although not recommended, R_HOME can contain spaces).
Do not use R_HOME in makefiles except when passing them to the shell. Specifically, do not use R_HOME in the argument to include, as R_HOME can contain spaces. Quoting the argument to include does not help. GNU make’s include accepts spaces when escaped using backslashes (GNU make syntax required):
## WARNING: requires GNU make (allowed on Windows)
sp =
sp +=
sq = $(subst $(sp),\ ,$1)
include $(call sq,${R_HOME}/etc${R_ARCH}/Makeconf)
A portable and the recommended way to avoid the problem of spaces in ${R_HOME} is using option -f of make. This is easy to do with recursive invocation of make, which is also the only usual situation when R_HOME is needed in the argument for include.
$(MAKE) -f "${R_HOME}/etc${R_ARCH}/Makeconf" -f Makefile.inner
Do be careful in what your tests (and examples) actually test. Bad practice seen in distributed packages include:
It is not reasonable to test the time taken by a command: you cannot know how fast or how heavily loaded an R platform might be. At best you can test a ratio of times, and even that is fraught with difficulties and not advisable: for example, the garbage collector may trigger at unpredictable times following heuristics that may change without notice.
Do not test the exact format of R messages (from R itself or from other packages): They change, and they can be translated.
Packages have even tested the exact format of system error messages, which are platform-dependent and perhaps locale-dependent. For example, in late 2021 libcurl changed its warning/error messages, including when URLs are not found.
If you use functions such as View, remember that in testing there is no one to look at the output. It is better to use something like one of
if(interactive()) View(obj) else print(head(obj))
if(interactive()) View(obj) else str(obj)
Be careful when comparing file paths. There can be multiple paths to a single file, and some of these can be very long character strings. If possible canonicalize paths before comparisons, but study ?normalizePath to be aware of the pitfalls.
Only test the accuracy of results if you have done a formal error analysis. Things such as checking that probabilities numerically sum to one are silly: numerical tests should always have a tolerance. That the tests on your platform achieve a particular tolerance says little about other platforms. R is configured by default to make use of long doubles where available, but they may not be available or be too slow for routine use. Most R platforms use ‘ix86’ or ‘x86_64’ CPUs: these may use extended precision registers on some but not all of their FPU instructions. Thus the achieved precision can depend on the compiler version and optimization flags—our experience is that 32-bit builds tend to be less precise than 64-bit ones. But not all platforms use those CPUs, and not all87 which use them configure them to allow the use of extended precision. In particular, current ARM CPUs do not have extended precision nor long doubles, and clang currently has long double the same as double on all ARM CPUs. On the other hand some CPUs have higher-precision modes which may be used for long double, notably 64-bit PowerPC and Sparc.
If you must try to establish a tolerance empirically, configure and build R with --disable-long-double and use appropriate compiler flags (such as -ffloat-store and -fexcess-precision=standard for gcc, depending on the CPU type88) to mitigate the effects of extended-precision calculations.
Tests which involve random inputs or non-deterministic algorithms should normally set a seed or be tested for many seeds.
Tests should use options(warn = 1) as reporting
There were 22 warnings (use warnings() to see them)
is pointless, especially for automated checking systems.
If your package uses dates/times, ensure that it works in all timezones, especially those near boundaries (problems have most often be seen in ‘Europe/London’ (zero offset in Winter) and ‘Pacific/Auckland’, near enough the International Date line) and with offsets not in whole hours (Adelaide, Chatham Islands, ...).
PDF size
Check timing
Encoding issues
Portable C and C++ code
Binary distribution
Next: Check timing, Previous: Writing portable packages, Up: Writing portable packages [Contents][Index]
1.6.1 PDF size
There are a several tools available to reduce the size of PDF files: often the size can be reduced substantially with no or minimal loss in quality. Not only do large files take up space: they can stress the PDF viewer and take many minutes to print (if they can be printed at all).
qpdf (http://qpdf.sourceforge.net/) can compress losslessly. It is fairly readily available (e.g. it has binaries for Windows and packages in Debian/Ubuntu/Fedora, and is installed as part of the CRAN macOS distribution of R). R CMD build has an option to run qpdf over PDF files under inst/doc and replace them if at least 10Kb and 10% is saved. The full path to the qpdf command can be supplied as environment variable R_QPDF (and is on the CRAN binary of R for macOS). It seems MiKTeX does not use PDF object compression and so qpdf can reduce considerably the files it outputs: MiKTeX’s defaults can be overridden by code in the preamble of an Sweave or LaTeX file — see how this is done for the R reference manual at https://svn.r-project.org/R/trunk/doc/manual/refman.top. (Although earlier versions of qpdf are supported, versions 6.0.0 and later in some cases achieve considerably better compression.)
Other tools can reduce the size of PDFs containing bitmap images at excessively high resolution. These are often best re-generated (for example Sweave defaults to 300 ppi, and 100–150 is more appropriate for a package manual). These tools include Adobe Acrobat (not Reader), Apple’s Preview89 and Ghostscript (which converts PDF to PDF by
ps2pdf options -dAutoRotatePages=/None -dPrinted=false in.pdf out.pdf
and suitable options might be
-dPDFSETTINGS=/ebook
-dPDFSETTINGS=/screen
See https://www.ghostscript.com/doc/current/VectorDevices.htm for more such and consider all the options for image downsampling). There have been examples in CRAN packages for which current versions of Ghostscript produced much bigger reductions than earlier ones (e.g. at the upgrade from 9.50 to 9.52 in March 2020).
We come across occasionally large PDF files containing excessively complicated figures using PDF vector graphics: such figures are often best redesigned or failing that, output as PNG files.
Option --compact-vignettes to R CMD build defaults to value ‘qpdf’: use ‘both’ to try harder to reduce the size, provided you have Ghostscript available (see the help for tools::compactPDF).
Next: Encoding issues, Previous: PDF size, Up: Writing portable packages [Contents][Index]
1.6.2 Check timing
There are several ways to find out where time is being spent in the check process. Start by setting the environment variable _R_CHECK_TIMINGS_ to ‘0’. This will report the total CPU times (not Windows) and elapsed times for installation and running examples, tests and vignettes, under each sub-architecture if appropriate. For tests and vignettes, it reports the time for each as well as the total.
Setting _R_CHECK_TIMINGS_ to a positive value sets a threshold (in seconds elapsed time) for reporting timings.
If you need to look in more detail at the timings for examples, use option --timings to R CMD check (this is set by --as-cran). This adds a summary to the check output for all the examples with CPU or elapsed time of more than 5 seconds. It produces a file mypkg.Rcheck/mypkg-Ex.timings containing timings for each help file: it is a tab-delimited file which can be read into R for further analysis.
Timings for the tests and vignette runs are given at the bottom of the corresponding log file: note that log files for successful vignette runs are only retained if environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value.
Next: Portable C and C++ code, Previous: Check timing, Up: Writing portable packages [Contents][Index]
1.6.3 Encoding issues
Care is needed if your package contains non-ASCII text, and in particular if it is intended to be used in more than one locale. It is possible to mark the encoding used in the DESCRIPTION file and in .Rd files, as discussed elsewhere in this manual.
First, consider carefully if you really need non-ASCII text. Many users of R will only be able to view correctly text in their native language group (e.g. Western European, Eastern European, Simplified Chinese) and ASCII.90. Other characters may not be rendered at all, rendered incorrectly, or cause your R code to give an error. For .Rd documentation, marking the encoding and including ASCII transliterations is likely to do a reasonable job. The set of characters which is commonly supported is wider than it used to be around 2000, but non-Latin alphabets (Greek, Russian, Georgian, …) are still often problematic and those with double-width characters (Chinese, Japanese, Korean, emoji) often need specialist fonts to render correctly.
Several CRAN packages have messages in their R code in French (and a few in German). A better way to tackle this is to use the internationalization facilities discussed elsewhere in this manual.
Function showNonASCIIfile in package tools can help in finding non-ASCII bytes in files.
There is a portable way to have arbitrary text in character strings (only) in your R code, which is to supply them in Unicode as ‘\uxxxx’ escapes. If there are any characters not in the current encoding the parser will encode the character string as UTF-8 and mark it as such. This applies also to character strings in datasets: they can be prepared using ‘\uxxxx’ escapes or encoded in UTF-8 in a UTF-8 locale, or even converted to UTF-8 via iconv(). If you do this, make sure you have ‘R (>= 2.10)’ (or later) in the ‘Depends’ field of the DESCRIPTION file.
R sessions running in non-UTF-8 locales will if possible re-encode such strings for display (and this is done by RGui on Windows, for example). Suitable fonts will need to be selected or made available91 both for the console/terminal and graphics devices such as ‘X11()’ and ‘windows()’. Using ‘postscript’ or ‘pdf’ will choose a default 8-bit encoding depending on the language of the UTF-8 locale, and your users would need to be told how to select the ‘encoding’ argument.
Note that the previous two paragraphs only apply to character strings in R code. Non-ASCII characters are particularly prevalent in comments (in the R code of the package, in examples, tests, vignettes and even in the NAMESPACE file) but should be avoided there. Most commonly people use the Windows extensions to Latin-1 (often directional single and double quotes, ellipsis, bullet and en and em dashes) which are not supported in strict Latin-1 locales nor in CJK locales on Windows. A surprisingly common misuse is to use a right quote in ‘don't’ instead of the correct apostrophe.
If you want to run R CMD check on a Unix-alike over a package that sets a package encoding in its DESCRIPTION file and do not use a UTF-8 locale you may need to specify a suitable locale via environment variable R_ENCODING_LOCALES. The default is equivalent to the value
"latin1=en_US:latin2=pl_PL:UTF-8=en_US.UTF-8:latin9=fr_FR.iso885915@euro"
(which is appropriate for a system based on glibc: macOS requires latin9=fr_FR.ISO8859-15) except that if the current locale is UTF-8 then the package code is translated to UTF-8 for syntax checking, so it is strongly recommended to check in a UTF-8 locale.
Next: Binary distribution, Previous: Encoding issues, Up: Writing portable packages [Contents][Index]
1.6.4 Portable C and C++ code
Writing portable C and C++ code is mainly a matter of observing the standards (C99, C++11 or where declared C++14/17/20) and testing that extensions (such as POSIX functions) are supported.
C++ standards: From version 3.6.0 (3.6.2 on Windows), R defaulted to C++11 where available92; R 4.1.0 defaults to C++14 (where available). However, in earlier versions the default standard was that of the compiler used, often C++98 or C++14, and the default is likely to change in future. For maximal portability a package should either specify a standard (see Using C++ code) or be tested under all of C++11, C++98 and C++14. (Specifying C++14 will limit portability.)
Note that the ‘TR1’ C++ extensions are not part of any of these standards and the <tr1/name> headers are not supplied by some of the compilers used for R, including on macOS. (Use the C++11 versions instead.)
As noted elsewhere, the C++17 standard was finalized only recently (Dec 2017) and support for it is patchy or absent on several platforms used for R. So portable code should not require C++17 (let alone C++20).
A common error is to assume recent versions of compilers or OSes. In production environments ‘long term support’ versions of OSes may be in use for many years,93 and their compilers may not be updated during that time. For example, GCC 4.8 was still in use in 2021 and could be (in RHEL 7) until 2028: that supports neither C++14 nor C++17.
The POSIX standards only require recently-defined functions to be declared if certain macros are defined with large enough values, and on some compiler/OS combinations94 they are not declared otherwise. So you may need to include something like one of 95
#define _XOPEN_SOURCE 600
or
#ifdef __GLIBC__
# define _POSIX_C_SOURCE 200809L
#endif
before any headers. (strdup, strncasecmp and strnlen are such functions – there are several older platforms which do not have the POSIX 2008 function strnlen.)
However, some common errors are worth pointing out here. It can be helpful to look up functions at https://www.cplusplus.com/reference/ or https://en.cppreference.com/w/ and compare what is defined in the various standards.
More care is needed for functions such as mallinfo which are not specified by any of these standards—hopefully the man page on your system will tell you so. Searching online for such pages for various OSes (preferably at least Linux, macOS and Solaris, and the FreeBSD manual pages at https://www.freebsd.org/cgi/man.cgi allow you to select many OSes96) should reveal useful information but a configure script is likely to be needed to check availability and functionality.
Both the compiler and OS (via system header files, which may differ by architecture even for nominally the same OS) affect the compilability of C/C++ code. Compilers from the GCC, clang, Intel and Oracle Developer Studio suites are routinely used with R, and both clang and Oracle have more than one implementation of C++ headers and library. The range of possibilities makes comprehensive empirical checking impossible, and regrettably compilers are patchy at best on warning about non-standard code.
Mathematical functions such as sqrt are defined in C++11 for floating-point arguments: float, double, long double and possibly more. The standard specifies what happens with an argument of integer type but this is not always implemented, resulting in a report of ‘overloading ambiguity’: this is commonly seen on Solaris, but for pow also seen on macOS (and other platforms using clang++).
A not-uncommonly-seen problem is to mistakenly call floor(x/y) or ceil(x/y) for int arguments x and y. Since x/y does integer division, the result is of type int and ‘overloading ambiguity’ may be reported. Some people have (pointlessly) called floor and ceil on arguments of integer type, which may have an ‘overloading ambiguity’.
A surprising common misuse is things like pow(10, -3): this should be the constant 1e-3. Note that there are constants such as M_SQRT2 defined in Rmath.h97 for sqrt(2.0), frequently mis-coded as sqrt(2).
Function fabs is defined only for floating-point types, except in C++11 which has overloads for std::fabs in <cmath> for integer types. Function abs is defined in C99’s <stdlib.h> for int and in C++’s <cstdlib> for integer types, overloaded in <cmath> for floating-point types. C++11 has additional overloads for std::abs in <cmath> for integer types. The effect of calling abs with a floating-point type is implementation-specific: it may truncate to an integer. For clarity and to avoid compiler warnings, use abs for integer types and fabs for double values, and when using C++11 include <cmath> and use the std:: prefix.
It is an error (and make little sense, although has been seen) to call macros/functions isnan, isinf and isfinite for integer arguments: a few compilers give a compilation error. Function finite is obsolete, and some compilers will warn about its use.
The GNU C/C++ compilers support a large number of non-portable extensions. For example, INFINITY (which is a float value in C99 and C++11), for which R provides the portable double value R_PosInf (and R_NegInf for -INFINITY). And NAN98 is just one NaN float value: for use with R, NA_REAL is often what is intended, but R_NaN is also available.
Some (but not all) extensions are listed at https://gcc.gnu.org/onlinedocs/gcc/C-Extensions.html and https://gcc.gnu.org/onlinedocs/gcc/C_002b_002b-Extensions.html.
Other GNU extensions which have bitten package writers are the use of non-portable characters such as ‘$’ in identifiers and use of C++ headers under ext.
The GNU Fortran compiler also supports a large number of non-portable extensions, the most commonly encountered one being ISNAN99. Some are listed at https://gcc.gnu.org/onlinedocs/gfortran/Extensions-implemented-in-GNU-Fortran.html. One that frequently catches package writers is that it allows out-of-order declarations: in standard-conformant Fortran variables must be declared (explicitly or implicitly) before use in other declarations such as dimensions.
GNU Fortran 10 and later give a compilation error for the previously widespread practice of passing a Fortran array element where an array is expected, or a scalar instead of a length-one array. See https://gcc.gnu.org/gcc-10/porting_to.html.
Including C-style headers in C++ code is not portable. Including the legacy header100 math.h in C++ code may conflict with cmath which may be included by other headers. In C++11, functions like sqrt and isnan are defined for double arguments in math.h and for a range of types including double in cmath. Similar issues have been seen for stdlib.h and cstdlib. Including the C++ header first used to be a sufficient workaround but for some 2016 compilers only one could be included.
Be careful to include the headers which define the functions you use. Some compilers/OSes include other system headers in their headers which are not required by the standards, and so code may compile on such systems and not on others. (A prominent example is the C++ header <random> which is indirectly included by <algorithm> by g++. Another issue is the C header <time.h> which is included by other headers on Linux and Windows but not macOS nor Solaris.) g++ 11 often needs explicit inclusion of the C++ headers <limits> (for numeric_limits) or <exception> (for set_terminate and similar), whereas earlier versions included these in other headers.
Note that malloc, calloc, realloc and free are defined by C99 in the header stdlib.h and (in the std:: namespace) by C++ header cstdlib. Some earlier implementations used a header malloc.h, but that is not portable and does not exist on macOS.
This also applies to types such as ssize_t. The POSIX standards say that is declared in headers unistd.h and sys/types.h, and the latter is often included indirectly by other headers on some but not all systems.
Similarly for constants: for example SIZE_MAX is defined in stdint.h alongside size_t.
Some headers are not portable: we have just mentioned malloc.h and often CRAN submissions attempt to use endian.h. The latter is a glibc extension: some OSs have machine/endian.h or sys/endian.h but some have neither.
Use #include "my.h" not #include <my.h> for headers in your package. The second form is intended for system headers and the search order for such headers is platform-dependent (and may not include the current directory). For extra safety, name headers in a way that cannot be confused with a system header so not, for example, types.h.
For C++ code, be careful to specify namespaces where needed. Many functions are defined by the standards to be in the std namespace, but g++ puts many such also in the C++ main namespace. One way to do so is to use declarations such as
using std::floor;
but it is usually preferable to use explicit namespace prefixes in the code.
Examples seen in CRAN packages include
abs acos atan bind calloc ceil div exp fabs floor fmod free log malloc
memcpy memset pow printf qsort round sin sprintf sqrt strcmp strcpy
strerror strlen strncmp strtol tan trunc
This problem is less common than it used to be, but in 2019 clang did not have bind in the main namespace. Also seen has been type size_t defined only in the std namespace.
Some C++ compilers refuse to compile constructs such as
if(ptr > 0) { ....}
which compares a pointer to the integer 0. This could just use if(ptr) (pointer addresses cannot be negative) but if needed pointers can be tested against nullptr (C++11) or NULL.
Macros defined by the compiler/OS can cause problems. Identifiers starting with an underscore followed by an upper-case letter or another underscore are reserved for system macros and should not be used in portable code (including not as guards in C/C++ headers). Other macros, typically upper-case, may be defined by the compiler or system headers and can cause problems. The most common issue involves the names of the Intel CPU registers such as CS, DS, ES, FS, GS and SS (and more with longer abbreviations101) defined on i586/x64 Solaris in <sys/regset.h> and often included indirectly by <stdlib.h> and other core headers. Further examples are ERR, VERSION, LITTLE_ENDIAN, zero and I (which is defined in Solaris’ <complex.h> as a compiler intrinsic for the imaginary unit). Some of these can be avoided by defining _POSIX_C_SOURCE before including any system headers, but it is better to only use all-upper-case names which have a unique prefix such as the package name.
typedefs in OS headers can conflict with those in the package: examples include ulong on several OSes, index_t and single on Solaris and thread using clang++ from version 9. (Note that these may conflict with other uses as identifiers, e.g. defining a C++ function called single.) The POSIX standard reserves (in §2.2.2) all identifiers ending in _t.
Some compilers do not allow a space between -D and the macro to be defined. Similarly for -U.
If you use OpenMP, check carefully that you have followed the advice in the subsection on OpenMP support. In particular, any use of OpenMP in C/C++ code will need to use
#ifdef _OPENMP
# include <omp.h>
#endif
Any use of OpenMP functions, e.g. omp_set_num_threads, also needs to be conditioned. To avoid incessant warnings such as
warning: ignoring #pragma omp parallel [-Wunknown-pragmas]
uses of such pragmas should also be conditioned (or commented out if they are used in code in a package not enabling OpenMP on any platform).
Do not hardcode -lgomp: not only is that specific to the GCC family of compilers, using the correct linker flag often sets up the run-time path to the library.
Package authors commonly assume things are part of C/C++ when they are not: the most common example is POSIX function strdup. The most common C library on Linux, glibc, will hide the declarations of such extensions unless a ‘feature-test macro’ is defined before (almost) any system header is included. So for strdup you need
#define _POSIX_C_SOURCE 200809L
...
#include <string.h>
...
strdup call(s)
where the appropriate value can be found by man strdup on Linux. (Use of strncasecmp is similar.)
However, modes of gcc with ‘GNU EXTENSIONS’ (which are the default, either -std=gnu99 or -std=gnu11) declare enough macros to ensure that missing declarations are rarely seen.
This applies also to constants such as M_PI and M_LN2, which are part of the X/Open standard: to use these define _XOPEN_SOURCE before including any headers, or include the R header Rmath.h.
Using alloca portably is tricky: it is neither an ISO C/C++ nor a POSIX function. An adequately portable preamble is
#ifdef __GNUC__
/* Includes GCC, clang and Intel compilers */
# undef alloca
# define alloca(x) __builtin_alloca((x))
#elif defined(__sun) || defined(_AIX)
/* this is necessary (and sufficient) for Solaris 10 and AIX 6: */
# include <alloca.h>
#endif
Compiler writers feel free to implement features from later standards than the one specified, so for example they may implement or warn on C++14/17/20 features. Portable code will not use such features – it can be hard to know what they are but the most common warnings are
'register' storage class specifier is deprecated and incompatible with C++17
ISO C++11 does not allow conversion from string literal to ‘char *’
(where conversion should be to const char *). Keyword register was not mentioned in C++98, deprecated in C++11 and removed in C++17.
There are quite a lot of other C++98 features deprecated in C++11 and removed in C++17, and clang 9 and later warn about them. Examples include bind1st/bind2nd (use std::bind or lambdas102) std::auto_ptr (replaced by std::unique_ptr), std:;mem_fun_ref and std::ptr_fun.
Be careful about including C headers in C++ code. Issues include
Use of the register storage class specifier (see the previous item).
The C99 keyword restrict is not part of103 any C++ standard and is rejected by some C++ compilers.
Inclusion by such headers of C-style headers such as math.h (see above).
The most portable way to interface to other software with a C API is to use C code (which can normally be mixed with C++ code in a package).
reinterpret_cast in C++ is not safe for pointers: for example the types may have different alignment requirements. Use memcpy to copy the contents to a fresh variable of the destination type.
Avoid platform-specific code if at all possible, but if you need to test for a platform ensure that all platforms are covered. For example, __unix__ is not defined on all Unix-alikes, in particular not on macOS. A reasonably portable way to condition code for a Unix-alike is
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#endif
but
#ifdef _WIN32
// Windows-specific code
#else
// Unix-alike code
#endif
would be better. For a Unix-alike it is much better to use configure to test for the functionality needed than make assumptions about OSes (and people all too frequently forget R is used on platforms other than Linux, Windows and macOS — and some forget macOS).
Headers in subdirectories are often not portable. For C++, this includes bits/, tr1/ and tr2/, none of which exist on macOS (and ext/ exists there but with different content from g++-based platforms). Header bits/stdc++.h is both not portable and not recommended for end-user code even on platforms which include it.
Some additional information for C++ is available at https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Plummer.pdf by Martyn Plummer.
Common symbols
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1.6.4.1 Common symbols
Most OSes (including all those commonly used for R) have the concept of ‘tentative definitions’ where global C variables are defined without an initializer. Traditionally the linker resolves all tentative definitions of the same variable in different object files to the same object, or to a non-tentative definition. However, gcc 10104 and LLVM clang 11105 have changed their default so that tentative definitions cannot be merged and the linker will give an error if the same variable is defined in more than one object file. To avoid this, all but one of the C source files should declare the variable extern — which means that any such variables included in header files need to be declared extern. A commonly used idiom (including by R itself) is to define all global variables as extern in a header, say globals.h (and nowhere else), and then in one (and one only) source file use
#define extern
# include "globals.h"
#undef extern
A cleaner approach is not to have global variables at all, but to place in a single file common variables (declared static) followed by all the functions which make use of them: this may result in more efficient code.
The ‘modern’ behaviour can be seen106 by using compiler flag -fno-common as part of ‘CFLAGS’ in earlier versions of gcc and clang.
-fno-common is said to be particularly beneficial for ARM cpus.
This is not pertinent to C++ which does not permit tentative definitions.
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1.6.5 Binary distribution
If you want to distribute a binary version of a package on Windows or macOS, there are further checks you need to do to check it is portable: it is all too easy to depend on external software on your own machine that other users will not have.
For Windows, check what other DLLs your package’s DLL depends on (‘imports’ from in the DLL tools’ parlance). A convenient GUI-based tool to do so is ‘Dependency Walker’ (https://www.dependencywalker.com/) for both 32-bit and 64-bit DLLs – note that this will report as missing links to R’s own DLLs such as R.dll and Rblas.dll. The command-line tool objdump in the appropriate toolchain will also reveal what DLLs are imported from. If you use a toolchain other than one provided by the R developers or use your own makefiles, watch out in particular for dependencies on the toolchain’s runtime DLLs such as libgfortran, libstdc++ and libgcc_s.
For macOS, using R CMD otool -L on the package’s shared object(s) in the libs directory will show what they depend on: watch for any dependencies in /usr/local/lib or /usr/local/gfortran/lib, notably libgfortran.?.dylib and libquadmath.0.dylib. (For ways to fix these, see Building binary-packages in R Installation and Administration .)
Many people (including the CRAN package repository) will not accept source packages containing binary files as the latter are a security risk. If you want to distribute a source package which needs external software on Windows or macOS, options include
To arrange for installation of the package to download the additional software from a URL, as e.g. package Cairo does.
(For CRAN.) To negotiate with Uwe Ligges to host the additional components on WinBuilder, and write a configure.win file to install them.
Be aware that license requirements you may require you to supply the sources for the additional components (and will if your package has a GPL-like license).
Next: Internationalization, Previous: Writing portable packages, Up: Creating R packages [Contents][Index]
1.7 Diagnostic messages
Diagnostic messages can be made available for translation, so it is important to write them in a consistent style. Using the tools described in the next section to extract all the messages can give a useful overview of your consistency (or lack of it). Some guidelines follow.
Messages are sentence fragments, and not viewed in isolation. So it is conventional not to capitalize the first word and not to end with a period (or other punctuation).
Try not to split up messages into small pieces. In C error messages use a single format string containing all English words in the messages.
In R error messages do not construct a message with paste (such messages will not be translated) but via multiple arguments to stop or warning, or via gettextf.
Do not use colloquialisms such as “can’t” and “don’t”.
Conventionally single quotation marks are used for quotations such as
'ord' must be a positive integer, at most the number of knots
and double quotation marks when referring to an R character string or a class, such as
'format' must be "normal" or "short" - using "normal"
Since ASCII does not contain directional quotation marks, it is best to use ‘'’ and let the translator (including automatic translation) use directional quotations where available. The range of quotation styles is immense: unfortunately we cannot reproduce them in a portable texinfo document. But as a taster, some languages use ‘up’ and ‘down’ (comma) quotes rather than left or right quotes, and some use guillemets (and some use what Adobe calls ‘guillemotleft’ to start and others use it to end).
In R messages it is also possible to use sQuote or dQuote as in
stop(gettextf("object must be of class %s or %s",
dQuote("manova"), dQuote("maov")),
domain = NA)
Occasionally messages need to be singular or plural (and in other languages there may be no such concept or several plural forms – Slovenian has four). So avoid constructions such as was once used in library
if((length(nopkgs) > 0) && !missing(lib.loc)) {
if(length(nopkgs) > 1)
warning("libraries ",
paste(sQuote(nopkgs), collapse = ", "),
" contain no packages")
else
warning("library ", paste(sQuote(nopkgs)),
" contains no package")
}
and was replaced by
if((length(nopkgs) > 0) && !missing(lib.loc)) {
pkglist <- paste(sQuote(nopkgs), collapse = ", ")
msg <- sprintf(ngettext(length(nopkgs),
"library %s contains no packages",
"libraries %s contain no packages",
domain = "R-base"),
pkglist)
warning(msg, domain=NA)
}
Note that it is much better to have complete clauses as here, since in another language one might need to say ‘There is no package in library %s’ or ‘There are no packages in libraries %s’.
Next: CITATION files, Previous: Diagnostic messages, Up: Creating R packages [Contents][Index]
1.8 Internationalization
There are mechanisms to translate the R- and C-level error and warning messages. There are only available if R is compiled with NLS support (which is requested by configure option --enable-nls, the default).
The procedures make use of msgfmt and xgettext which are part of GNU gettext and this will need to be installed: Windows users can find pre-compiled binaries at https://www.stats.ox.ac.uk/pub/Rtools/goodies/gettext-tools.zip.
C-level messages
R messages
Preparing translations
Next: R messages, Previous: Internationalization, Up: Internationalization [Contents][Index]
1.8.1 C-level messages
The process of enabling translations is
In a header file that will be included in all the C (or C++ or Objective C/C++) files containing messages that should be translated, declare
#include <R.h> /* to include Rconfig.h */
#ifdef ENABLE_NLS
#include <libintl.h>
#define _(String) dgettext ("pkg", String)
/* replace pkg as appropriate */
#else
#define _(String) (String)
#endif
For each message that should be translated, wrap it in _(...), for example
error(_("'ord' must be a positive integer"));
If you want to use different messages for singular and plural forms, you need to add
#ifndef ENABLE_NLS
#define dngettext(pkg, String, StringP, N) (N > 1 ? StringP : String)
#endif
and mark strings by
dngettext("pkg", <singular string>, <plural string>, n)
In the package’s src directory run
xgettext --keyword=_ -o pkg.pot *.c
The file src/pkg.pot is the template file, and conventionally this is shipped as po/pkg.pot.
Next: Preparing translations, Previous: C-level messages, Up: Internationalization [Contents][Index]
1.8.2 R messages
Mechanisms are also available to support the automatic translation of R stop, warning and message messages. They make use of message catalogs in the same way as C-level messages, but using domain R-pkg rather than pkg. Translation of character strings inside stop, warning and message calls is automatically enabled, as well as other messages enclosed in calls to gettext or gettextf. (To suppress this, use argument domain=NA.)
Tools to prepare the R-pkg.pot file are provided in package tools: xgettext2pot will prepare a file from all strings occurring inside gettext/gettextf, stop, warning and message calls. Some of these are likely to be spurious and so the file is likely to need manual editing. xgettext extracts the actual calls and so is more useful when tidying up error messages.
The R function ngettext provides an interface to the C function of the same name: see example in the previous section. It is safest to use domain="R-pkg" explicitly in calls to ngettext, and necessary for earlier versions of R unless they are calls directly from a function in the package.
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1.8.3 Preparing translations
Once the template files have been created, translations can be made. Conventional translations have file extension .po and are placed in the po subdirectory of the package with a name that is either ‘ll.po’ or ‘R-ll.po’ for translations of the C and R messages respectively to language with code ‘ll’.
See Localization of messages in R Installation and Administration , for details of language codes.
There is an R function, update_pkg_po in package tools, to automate much of the maintenance of message translations. See its help for what it does in detail.
If this is called on a package with no existing translations, it creates the directory pkgdir/po, creates a template file of R messages, pkgdir/po/R-pkg.pot, within it, creates the ‘en@quot’ translation and installs that. (The ‘en@quot’ pseudo-language interprets quotes in their directional forms in suitable (e.g. UTF-8) locales.)
If the package has C source files in its src directory that are marked for translation, use
touch pkgdir/po/pkg.pot
to create a dummy template file, then call update_pkg_po again (this can also be done before it is called for the first time).
When translations to new languages are added in the pkgdir/po directory, running the same command will check and then install the translations.
If the package sources are updated, the same command will update the template files, merge the changes into the translation .po files and then installed the updated translations. You will often see that merging marks translations as ‘fuzzy’ and this is reported in the coverage statistics. As fuzzy translations are not used, this is an indication that the translation files need human attention.
The merged translations are run through tools::checkPofile to check that C-style formats are used correctly: if not the mismatches are reported and the broken translations are not installed.
This function needs the GNU gettext-tools installed and on the path: see its help page.
Next: Package types, Previous: Internationalization, Up: Creating R packages [Contents][Index]
1.9 CITATION files
An installed file named CITATION will be used by the citation() function. (It should be in the inst subdirectory of the package sources.)
The CITATION file is parsed as R code (in the package’s declared encoding, or in ASCII if none is declared). If no such file is present, citation auto-generates citation information from the package DESCRIPTION metadata, and an example of what that would look like as a CITATION file can be seen in recommended package nlme (see below): recommended packages boot, cluster and mgcv have further examples.
A CITATION file will contain calls to function bibentry.
Here is that for nlme:
year <- sub("-.*", "", meta$Date)
note <- sprintf("R package version %s", meta$Version)
bibentry(bibtype = "Manual",
title = "{nlme}: Linear and Nonlinear Mixed Effects Models",
author = c(person("Jose", "Pinheiro"),
person("Douglas", "Bates"),
person("Saikat", "DebRoy"),
person("Deepayan", "Sarkar"),
person("R Core Team")),
year = year,
note = note,
url = "https://CRAN.R-project.org/package=nlme")
Note the way that information that may need to be updated is picked up from object meta, a parsed version of the DESCRIPTION file – it is tempting to hardcode such information, but it normally then gets outdated. See ?bibentry for further details of the information which can be provided.
In case a bibentry contains LaTeX markup (e.g., for accented characters or mathematical symbols), it may be necessary to provide a text representation to be used for printing via the textVersion argument to bibentry. E.g., earlier versions of nlme additionally used
textVersion =
paste0("Jose Pinheiro, Douglas Bates, Saikat DebRoy,",
"Deepayan Sarkar and the R Core Team (",
year,
"). nlme: Linear and Nonlinear Mixed Effects Models. ",
note, ".")
The CITATION file should itself produce no output when source-d.
It is desirable (and essential for CRAN) that the CITATION file does not contain calls to functions such as packageDescription which assume the package is installed in a library tree on the package search path.
Next: Services, Previous: CITATION files, Up: Creating R packages [Contents][Index]
1.10 Package types
The DESCRIPTION file has an optional field Type which if missing is assumed to be ‘Package’, the sort of extension discussed so far in this chapter. Currently one other type is recognized; there used also to be a ‘Translation’ type.
Frontend
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1.10.1 Frontend
This is a rather general mechanism, designed for adding new front-ends such as the former gnomeGUI package (see the Archive area on CRAN). If a configure file is found in the top-level directory of the package it is executed, and then if a Makefile is found (often generated by configure), make is called. If R CMD INSTALL --clean is used make clean is called. No other action is taken.
R CMD build can package up this type of extension, but R CMD check will check the type and skip it.
Many packages of this type need write permission for the R installation directory.
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1.11 Services
Several members of the R project have set up services to assist those writing R packages, particularly those intended for public distribution.
win-builder.r-project.org offers the automated preparation of (32/64-bit) Windows binaries from well-tested source packages.
R-Forge (R-Forge.r-project.org) and RForge (www.rforge.net) are similar services with similar names. Both provide source-code management through SVN, daily building and checking, mailing lists and a repository that can be accessed via install.packages (they can be selected by setRepositories and the GUI menus that use it). Package developers have the opportunity to present their work on the basis of project websites or news announcements. Mailing lists, forums or wikis provide useRs with convenient instruments for discussions and for exchanging information between developers and/or interested useRs.
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2 Writing R documentation files
Rd format
Sectioning
Marking text
Lists and tables
Cross-references
Mathematics
Figures
Insertions
Indices
Platform-specific documentation
Conditional text
Dynamic pages
User-defined macros
Encoding
Processing documentation files
Editing Rd files
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2.1 Rd format
R objects are documented in files written in “R documentation” (Rd) format, a simple markup language much of which closely resembles (La)TeX, which can be processed into a variety of formats, including LaTeX, HTML and plain text. The translation is carried out by functions in the tools package called by the script Rdconv in R_HOME/bin and by the installation scripts for packages.
The R distribution contains more than 1300 such files which can be found in the src/library/pkg/man directories of the R source tree, where pkg stands for one of the standard packages which are included in the R distribution.
As an example, let us look at a simplified version of src/library/base/man/load.Rd which documents the R function load.
% File src/library/base/man/load.Rd
\name{load}
\alias{load}
\title{Reload Saved Datasets}
\description{
Reload the datasets written to a file with the function
\code{save}.
}
\usage{
load(file, envir = parent.frame())
}
\arguments{
\item{file}{a connection or a character string giving the
name of the file to load.}
\item{envir}{the environment where the data should be
loaded.}
}
\seealso{
\code{\link{save}}.
}
\examples{
## save all data
save(list = ls(), file= "all.RData")
## restore the saved values to the current environment
load("all.RData")
## restore the saved values to the workspace
load("all.RData", .GlobalEnv)
}
\keyword{file}
An Rd file consists of three parts. The header gives basic information about the name of the file, the topics documented, a title, a short textual description and R usage information for the objects documented. The body gives further information (for example, on the function’s arguments and return value, as in the above example). Finally, there is an optional footer with keyword information. The header is mandatory.
Information is given within a series of sections with standard names (and user-defined sections are also allowed). Unless otherwise specified107 these should occur only once in an Rd file (in any order), and the processing software will retain only the first occurrence of a standard section in the file, with a warning.
See “Guidelines for Rd files” for guidelines for writing documentation in Rd format which should be useful for package writers. The R generic function prompt is used to construct a bare-bones Rd file ready for manual editing. Methods are defined for documenting functions (which fill in the proper function and argument names) and data frames. There are also functions promptData, promptPackage, promptClass, and promptMethods for other types of Rd file.
The general syntax of Rd files is summarized below. For a detailed technical discussion of current Rd syntax, see “Parsing Rd files”.
Rd files consist of four types of text input. The most common is LaTeX-like, with the backslash used as a prefix on markup (e.g. \alias), and braces used to indicate arguments (e.g. {load}). The least common type of text is ‘verbatim’ text, where no markup other than the comment marker (%) is processed. There is also a rare variant of ‘verbatim’ text (used in \eqn, \deqn, \figure, and \newcommand) where comment markers need not be escaped. The final type is R-like, intended for R code, but allowing some embedded macros. Quoted strings within R-like text are handled specially: regular character escapes such as \n may be entered as-is. Only markup starting with \l (e.g. \link) or \v (e.g. \var) will be recognized within quoted strings. The rarely used vertical tab \v must be entered as \\v.
Each macro defines the input type for its argument. For example, the file initially uses LaTeX-like syntax, and this is also used in the \description section, but the \usage section uses R-like syntax, and the \alias macro uses ‘verbatim’ syntax. Comments run from a percent symbol % to the end of the line in all types of text except the rare ‘verbatim’ variant (as on the first line of the load example).
Because backslashes, braces and percent symbols have special meaning, to enter them into text sometimes requires escapes using a backslash. In general balanced braces do not need to be escaped, but percent symbols always do, except in the ‘verbatim’ variant. For the complete list of macros and rules for escapes, see “Parsing Rd files”.
Documenting functions
Documenting data sets
Documenting S4 classes and methods
Documenting packages
Next: Documenting data sets, Previous: Rd format, Up: Rd format [Contents][Index]
2.1.1 Documenting functions
The basic markup commands used for documenting R objects (in particular, functions) are given in this subsection.
\name{name} ¶
name typically108 is the basename of the Rd file containing the documentation. It is the “name” of the Rd object represented by the file and has to be unique in a package. To avoid problems with indexing the package manual, it may not contain ‘!’ ‘|’ nor ‘@’, and to avoid possible problems with the HTML help system it should not contain ‘/’ nor a space. (LaTeX special characters are allowed, but may not be collated correctly in the index.) There can only be one \name entry in a file, and it must not contain any markup. Entries in the package manual will be in alphabetic109 order of the \name entries.
\alias{topic} ¶
The \alias sections specify all “topics” the file documents. This information is collected into index data bases for lookup by the on-line (plain text and HTML) help systems. The topic can contain spaces, but (for historical reasons) leading and trailing spaces will be stripped. Percent and left brace need to be escaped by a backslash.
There may be several \alias entries. Quite often it is convenient to document several R objects in one file. For example, file Normal.Rd documents the density, distribution function, quantile function and generation of random variates for the normal distribution, and hence starts with
\name{Normal}
\alias{Normal}
\alias{dnorm}
\alias{pnorm}
\alias{qnorm}
\alias{rnorm}
Also, it is often convenient to have several different ways to refer to an R object, and an \alias does not need to be the name of an object.
Note that the \name is not necessarily a topic documented, and if so desired it needs to have an explicit \alias entry (as in this example).
\title{Title} ¶
Title information for the Rd file. This should be capitalized and not end in a period; try to limit its length to at most 65 characters for widest compatibility.
Markup is supported in the text, but use of characters other than English text and punctuation (e.g., ‘<’) may limit portability.
There must be one (and only one) \title section in a help file.
\description{…} ¶
A short description of what the function(s) do(es) (one paragraph, a few lines only). (If a description is too long and cannot easily be shortened, the file probably tries to document too much at once.) This is mandatory except for package-overview files.
\usage{fun(arg1, arg2, …)} ¶
One or more lines showing the synopsis of the function(s) and variables documented in the file. These are set in typewriter font. This is an R-like command.
The usage information specified should match the function definition exactly (such that automatic checking for consistency between code and documentation is possible).
To indicate that a function can be used in several different ways, depending on the named arguments specified, use section \details. E.g., abline.Rd contains
\details{
Typical usages are
\preformatted{abline(a, b, ...)
......
}
Use \method{generic}{class} to indicate the name of an S3 method for the generic function generic for objects inheriting from class "class". In the printed versions, this will come out as generic (reflecting the understanding that methods should not be invoked directly but via method dispatch), but codoc() and other QC tools always have access to the full name.
For example, print.ts.Rd contains
\usage{
\method{print}{ts}(x, calendar, \dots)
}
which will print as
Usage:
## S3 method for class ‘ts’:
print(x, calendar, ...)
Usage for replacement functions should be given in the style of dim(x) <- value rather than explicitly indicating the name of the replacement function ("dim<-" in the above). Similarly, one can use \method{generic}{class}(arglist) <- value to indicate the usage of an S3 replacement method for the generic replacement function "generic<-" for objects inheriting from class "class".
Usage for S3 methods for extracting or replacing parts of an object, S3 methods for members of the Ops group, and S3 methods for user-defined (binary) infix operators (‘%xxx%’) follows the above rules, using the appropriate function names. E.g., Extract.factor.Rd contains
\usage{
\method{[}{factor}(x, \dots, drop = FALSE)
\method{[[}{factor}(x, \dots)
\method{[}{factor}(x, \dots) <- value
}
which will print as
Usage:
## S3 method for class ‘factor’:
x[..., drop = FALSE]
## S3 method for class ‘factor’:
x[[...]]
## S3 replacement method for class ‘factor’:
x[...] <- value
\S3method is accepted as an alternative to \method.
\arguments{…} ¶
Description of the function’s arguments, using an entry of the form
\item{arg_i}{Description of arg_i.}
for each element of the argument list. (Note that there is no whitespace between the three parts of the entry.) There may be optional text outside the \item entries, for example to give general information about groups of parameters.
\details{…} ¶
A detailed if possible precise description of the functionality provided, extending the basic information in the \description slot.
\value{…} ¶
Description of the function’s return value.
If a list with multiple values is returned, you can use entries of the form
\item{comp_i}{Description of comp_i.}
for each component of the list returned. Optional text may precede110 this list (see for example the help for rle). Note that \value is implicitly a \describe environment, so that environment should not be used for listing components, just individual \item{}{} entries.
\references{…} ¶
A section with references to the literature. Use \url{} or \href{}{} for web pointers, and \doi{} for DOIs (this needs R >= 3.3, see User-defined macros for more info).
\note{...} ¶
Use this for a special note you want to have pointed out. Multiple \note sections are allowed, but might be confusing to the end users.
For example, pie.Rd contains
\note{
Pie charts are a very bad way of displaying information.
The eye is good at judging linear measures and bad at
judging relative areas.
......
}
\author{…} ¶
Information about the author(s) of the Rd file. Use \email{} without extra delimiters (such as ‘( )’ or ‘< >’) to specify email addresses, or \url{} or \href{}{} for web pointers.
\seealso{…} ¶
Pointers to related R objects, using \code{\link{...}} to refer to them (\code is the correct markup for R object names, and \link produces hyperlinks in output formats which support this. See Marking text, and Cross-references).
\examples{…}
Examples of how to use the function. Code in this section is set in typewriter font without reformatting and is run by example() unless marked otherwise (see below).
Examples are not only useful for documentation purposes, but also provide test code used for diagnostic checking of R code. By default, text inside \examples{} will be displayed in the output of the help page and run by example() and by R CMD check. You can use \dontrun{} for text that should only be shown, but not run, and \dontshow{} for extra commands for testing that should not be shown to users, but will be run by example(). (Previously this was called \testonly, and that is still accepted.)
Text inside \dontrun{} is ‘verbatim’, but the other parts of the \examples section are R-like text.
For example,
x <- runif(10) # Shown and run.
\dontrun{plot(x)} # Only shown.
\dontshow{log(x)} # Only run.
Thus, example code not included in \dontrun must be executable! In addition, it should not use any system-specific features or require special facilities (such as Internet access or write permission to specific directories). Text included in \dontrun is indicated by comments in the processed help files: it need not be valid R code but the escapes must still be used for %, \ and unpaired braces as in other ‘verbatim’ text.
Example code must be capable of being run by example, which uses source. This means that it should not access stdin, e.g. to scan() data from the example file.
Data needed for making the examples executable can be obtained by random number generation (for example, x <- rnorm(100)), or by using standard data sets listed by data() (see ?data for more info).
Finally, there is \donttest, used (at the beginning of a separate line) to mark code that should be run by example() but not by R CMD check (by default: the option --run-donttest can be used). This should be needed only occasionally but can be used for code which might fail in circumstances that are hard to test for, for example in some locales. (Use e.g. capabilities() or nzchar(Sys.which("someprogram")) to test for features needed in the examples wherever possible, and you can also use try() or tryCatch(). Use interactive() to condition examples which need someone to interact with.) Note that code included in \donttest must be correct R code, and any packages used should be declared in the DESCRIPTION file. It is good practice to include a comment in the \donttest section explaining why it is needed.
Output from code between comments
## IGNORE_RDIFF_BEGIN
## IGNORE_RDIFF_END
is ignored when comparing check output to reference output (a -Ex.Rout.save file). This markup can also be used for scripts under tests.
\keyword{key}
There can be zero or more \keyword sections per file. Each \keyword section should specify a single keyword, preferably one of the standard keywords as listed in file KEYWORDS in the R documentation directory (default R_HOME/doc). Use e.g. RShowDoc("KEYWORDS") to inspect the standard keywords from within R. There can be more than one \keyword entry if the R object being documented falls into more than one category, or none.
Do strongly consider using \concept (see Indices) instead of \keyword if you are about to use more than very few non-standard keywords.
The special keyword ‘internal’ marks a page of internal objects that are not part of the package’s API. If the help page for object foo has keyword ‘internal’, then help(foo) gives this help page, but foo is excluded from several object indices, including the alphabetical list of objects in the HTML help system.
help.search() can search by keyword, including user-defined values: however the ‘Search Engine & Keywords’ HTML page accessed via help.start() provides single-click access only to a pre-defined list of keywords.
Next: Documenting S4 classes and methods, Previous: Documenting functions, Up: Rd format [Contents][Index]
2.1.2 Documenting data sets
The structure of Rd files which document R data sets is slightly different. Sections such as \arguments and \value are not needed but the format and source of the data should be explained.
As an example, let us look at src/library/datasets/man/rivers.Rd which documents the standard R data set rivers.
\name{rivers}
\docType{data}
\alias{rivers}
\title{Lengths of Major North American Rivers}
\description{
This data set gives the lengths (in miles) of 141 \dQuote{major}
rivers in North America, as compiled by the US Geological
Survey.
}
\usage{rivers}
\format{A vector containing 141 observations.}
\source{World Almanac and Book of Facts, 1975, page 406.}
\references{
McNeil, D. R. (1977) \emph{Interactive Data Analysis}.
New York: Wiley.
}
\keyword{datasets}
This uses the following additional markup commands.
\docType{…}
Indicates the “type” of the documentation object. Always ‘data’ for data sets, and ‘package’ for pkg-package.Rd overview files. Documentation for S4 methods and classes uses ‘methods’ (from promptMethods()) and ‘class’ (from promptClass()).
\format{…} ¶
A description of the format of the data set (as a vector, matrix, data frame, time series, …). For matrices and data frames this should give a description of each column, preferably as a list or table. See Lists and tables, for more information.
\source{…} ¶
Details of the original source (a reference or URL, see Specifying URLs). In addition, section \references could give secondary sources and usages.
Note also that when documenting data set bar,
The \usage entry is always bar or (for packages which do not use lazy-loading of data) data(bar). (In particular, only document a single data object per Rd file.)
The \keyword entry should always be ‘datasets’.
If bar is a data frame, documenting it as a data set can be initiated via prompt(bar). Otherwise, the promptData function may be used.
Next: Documenting packages, Previous: Documenting data sets, Up: Rd format [Contents][Index]
2.1.3 Documenting S4 classes and methods
There are special ways to use the ‘?’ operator, namely ‘class?topic’ and ‘methods?topic’, to access documentation for S4 classes and methods, respectively. This mechanism depends on conventions for the topic names used in \alias entries. The topic names for S4 classes and methods respectively are of the form
class-class
generic,signature_list-method
where signature_list contains the names of the classes in the signature of the method (without quotes) separated by ‘,’ (without whitespace), with ‘ANY’ used for arguments without an explicit specification. E.g., ‘genericFunction-class’ is the topic name for documentation for the S4 class "genericFunction", and ‘coerce,ANY,NULL-method’ is the topic name for documentation for the S4 method for coerce for signature c("ANY", "NULL").
Skeletons of documentation for S4 classes and methods can be generated by using the functions promptClass() and promptMethods() from package methods. If it is necessary or desired to provide an explicit function declaration (in a \usage section) for an S4 method (e.g., if it has “surprising arguments” to be mentioned explicitly), one can use the special markup
\S4method{generic}{signature_list}(argument_list)
(e.g., ‘\S4method{coerce}{ANY,NULL}(from, to)’).
To make full use of the potential of the on-line documentation system, all user-visible S4 classes and methods in a package should at least have a suitable \alias entry in one of the package’s Rd files. If a package has methods for a function defined originally somewhere else, and does not change the underlying default method for the function, the package is responsible for documenting the methods it creates, but not for the function itself or the default method.
An S4 replacement method is documented in the same way as an S3 one: see the description of \method in Documenting functions.
See help("Documentation", package = "methods") for more information on using and creating on-line documentation for S4 classes and methods.
Previous: Documenting S4 classes and methods, Up: Rd format [Contents][Index]
2.1.4 Documenting packages
Packages may have an overview help page with an \alias pkgname-package, e.g. ‘utils-package’ for the utils package, when package?pkgname will open that help page. If a topic named pkgname does not exist in another Rd file, it is helpful to use this as an additional \alias.
Skeletons of documentation for a package can be generated using the function promptPackage(). If the final = LIBS argument is used, then the Rd file will be generated in final form, containing the information that would be produced up to library(help = pkgname). Otherwise (the default) comments will be inserted giving suggestions for content.
Apart from the mandatory \name and \title and the pkgname-package alias, the only requirement for the package overview page is that it include a \docType{package} statement. All other content is optional. We suggest that it should be a short overview, to give a reader unfamiliar with the package enough information to get started. More extensive documentation is better placed into a package vignette (see Writing package vignettes) and referenced from this page, or into individual man pages for the functions, datasets, or classes.
Next: Marking text, Previous: Rd format, Up: Writing R documentation files [Contents][Index]
2.2 Sectioning
To begin a new paragraph or leave a blank line in an example, just insert an empty line (as in (La)TeX). To break a line, use \cr.
In addition to the predefined sections (such as \description{}, \value{}, etc.), you can “define” arbitrary ones by \section{section_title}{…}. For example
\section{Warning}{
You must not call this function unless …
}
For consistency with the pre-assigned sections, the section name (the first argument to \section) should be capitalized (but not all upper case). Whitespace between the first and second braced expressions is not allowed. Markup (e.g. \code) within the section title may cause problems with the latex conversion (depending on the version of macro packages such as ‘hyperref’) and so should be avoided.
The \subsection macro takes arguments in the same format as \section, but is used within a section, so it may be used to nest subsections within sections or other subsections. There is no predefined limit on the nesting level, but formatting is not designed for more than 3 levels (i.e. subsections within subsections within sections).
Note that additional named sections are always inserted at a fixed position in the output (before \note, \seealso and the examples), no matter where they appear in the input (but in the same order amongst themselves as in the input).
Next: Lists and tables, Previous: Sectioning, Up: Writing R documentation files [Contents][Index]
2.3 Marking text
The following logical markup commands are available for emphasizing or quoting text.
\emph{text} ¶
\strong{text}
Emphasize text using italic and bold font if possible; \strong is regarded as stronger (more emphatic).
\bold{text} ¶
Set text in bold font where possible.
\sQuote{text} ¶
\dQuote{text}
Portably single or double quote text (without hard-wiring the characters used for quotation marks).
Each of the above commands takes LaTeX-like input, so other macros may be used within text.
The following logical markup commands are available for indicating specific kinds of text. Except as noted, these take ‘verbatim’ text input, and so other macros may not be used within them. Some characters will need to be escaped (see Insertions).
\code{text} ¶
Indicate text that is a literal example of a piece of an R program, e.g., a fragment of R code or the name of an R object. Text is entered in R-like syntax, and displayed using typewriter font where possible. Macros \var and \link are interpreted within text.
\preformatted{text} ¶
Indicate text that is a literal example of a piece of a program. Text is displayed using typewriter font where possible. Formatting, e.g. line breaks, is preserved. (Note that this includes a line break after the initial {, so typically text should start on the same line as the command.)
Due to limitations in LaTeX as of this writing, this macro may not be nested within other markup macros other than \dQuote and \sQuote, as errors or bad formatting may result.
\kbd{keyboard-characters} ¶
Indicate keyboard input, using slanted typewriter font if possible, so users can distinguish the characters they are supposed to type from computer output. Text is entered ‘verbatim’.
\samp{text} ¶
Indicate text that is a literal example of a sequence of characters, entered ‘verbatim’. No wrapping or reformatting will occur. Displayed using typewriter font where possible.
\verb{text} ¶
Indicate text that is a literal example of a sequence of characters, with no interpretation of e.g. \var, but which will be included within word-wrapped text. Displayed using typewriter font if possible.
\pkg{package_name} ¶
Indicate the name of an R package. LaTeX-like.
\file{file_name} ¶
Indicate the name of a file. Text is LaTeX-like, so backslash needs to be escaped. Displayed using a distinct font where possible.
\email{email_address} ¶
Indicate an electronic mail address. LaTeX-like, will be rendered as a hyperlink in HTML and PDF conversion. Displayed using typewriter font where possible.
\url{uniform_resource_locator} ¶
Indicate a uniform resource locator (URL) for the World Wide Web. The argument is handled as ‘verbatim’ text (with percent and braces escaped by backslash), and rendered as a hyperlink in HTML and PDF conversion. Linefeeds are removed, and leading and trailing whitespace111 is removed. See Specifying URLs.
Displayed using typewriter font where possible.
\href{uniform_resource_locator}{text} ¶
Indicate a hyperlink to the World Wide Web. The first argument is handled as ‘verbatim’ text (with percent and braces escaped by backslash) and is used as the URL in the hyperlink, with the second argument of LaTeX-like text displayed to the user. Linefeeds are removed from the first argument, and leading and trailing whitespace is removed.
Note that RFC3986-encoded URLs (e.g. using ‘%28VS.85%29’ in place of ‘(VS.85)’) may not work correctly in versions of R before 3.1.3 and are best avoided—use URLdecode() to decode them.
\var{metasyntactic_variable} ¶
Indicate a metasyntactic variable. In some cases this will be rendered distinctly, e.g. in italic, but not in all112. LaTeX-like.
\env{environment_variable} ¶
Indicate an environment variable. ‘Verbatim’. Displayed using typewriter font where possible
\option{option} ¶
Indicate a command-line option. ‘Verbatim’. Displayed using typewriter font where possible.
\command{command_name} ¶
Indicate the name of a command. LaTeX-like, so \var is interpreted. Displayed using typewriter font where possible.
\dfn{term} ¶
Indicate the introductory or defining use of a term. LaTeX-like.
\cite{reference} ¶
Indicate a reference without a direct cross-reference via \link (see Cross-references), such as the name of a book. LaTeX-like.
\acronym{acronym} ¶
Indicate an acronym (an abbreviation written in all capital letters), such as GNU. LaTeX-like.
Next: Cross-references, Previous: Marking text, Up: Writing R documentation files [Contents][Index]
2.4 Lists and tables
The \itemize and \enumerate commands take a single argument, within which there may be one or more \item commands. The text following each \item is formatted as one or more paragraphs, suitably indented and with the first paragraph marked with a bullet point (\itemize) or a number (\enumerate).
Note that unlike argument lists, \item in these formats is followed by a space and the text (not enclosed in braces). For example
\enumerate{
\item A database consists of one or more records, each with one or
more named fields.
\item Regular lines start with a non-whitespace character.
\item Records are separated by one or more empty lines.
}
\itemize and \enumerate commands may be nested.
The \describe command is similar to \itemize but allows initial labels to be specified. Each \item takes two arguments, the label and the body of the item, in exactly the same way as an argument or value \item. \describe commands are mapped to <DL> lists in HTML and \description lists in LaTeX.
Using these without any \items may cause problems with some conversions and makes little sense.
The \tabular command takes two arguments. The first gives for each of the columns the required alignment (‘l’ for left-justification, ‘r’ for right-justification or ‘c’ for centring.) The second argument consists of an arbitrary number of lines separated by \cr, and with fields separated by \tab. For example:
\tabular{rlll}{
[,1] \tab Ozone \tab numeric \tab Ozone (ppb)\cr
[,2] \tab Solar.R \tab numeric \tab Solar R (lang)\cr
[,3] \tab Wind \tab numeric \tab Wind (mph)\cr
[,4] \tab Temp \tab numeric \tab Temperature (degrees F)\cr
[,5] \tab Month \tab numeric \tab Month (1--12)\cr
[,6] \tab Day \tab numeric \tab Day of month (1--31)
}
There must be the same number of fields on each line as there are alignments in the first argument, and they must be non-empty (but can contain only spaces). (There is no whitespace between \tabular and the first argument, nor between the two arguments.)
Next: Mathematics, Previous: Lists and tables, Up: Writing R documentation files [Contents][Index]
2.5 Cross-references
The markup \link{foo} (usually in the combination \code{\link{foo}}) produces a hyperlink to the help for foo. Here foo is a topic, that is the argument of \alias markup in another Rd file (possibly in another package). Hyperlinks are supported in some of the formats to which Rd files are converted, for example HTML and PDF, but ignored in others, e.g. the text format.
One main usage of \link is in the \seealso section of the help page, see Rd format.
Note that whereas leading and trailing spaces are stripped when extracting a topic from a \alias, they are not stripped when looking up the topic of a \link.
You can specify a link to a different topic than its name by \link[=dest]{name} which links to topic dest with name name. This can be used to refer to the documentation for S3/4 classes, for example \code{"\link[=abc-class]{abc}"} would be a way to refer to the documentation of an S4 class "abc" defined in your package, and \code{"\link[=terms.object]{terms}"} to the S3 "terms" class (in package stats). To make these easy to read in the source file, \code{"\linkS4class{abc}"} expands to the form given above.
There are two other forms with an optional argument, specified as \link[pkg]{foo} and \link[pkg:bar]{foo}, to link to topics foo and bar respectively in the package pkg. They are currently only used in HTML help (and ignored for hyperlinks in LaTeX conversions of help pages). One should be careful about topics containing special characters (such as arithmetic operators) as they may result in unresolvable links, and preferably use a safer alias in the same help page.
Historically (before R version 4.1.0), links of the form \link[pkg]{foo} and \link[pkg:bar]{foo} used to be interpreted as links to files foo.html and bar.html in package pkg, respectively. For this reason, the HTML help system looks for file foo.html in package pkg if it does not find topic foo, and then searches for the topic in other installed packages. To test that links work both with both old and new systems, the pre-4.1.0 behaviour can be restored by setting the environment variable _R_HELP_LINKS_TO_TOPICS_=false.
Packages referred to by these ‘other forms’ should be declared in the DESCRIPTION file, in the ‘Depends’, ‘Imports’, ‘Suggests’ or ‘Enhances’ fields.
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2.6 Mathematics
Mathematical formulae should be set beautifully for printed documentation yet we still want something useful for text and HTML online help. To this end, the two commands \eqn{latex}{ascii} and \deqn{latex}{ascii} are used. Whereas \eqn is used for “inline” formulae (corresponding to TeX’s $…$), \deqn gives “displayed equations” (as in LaTeX’s displaymath environment, or TeX’s $$…$$). Both arguments are treated as ‘verbatim’ text.
Both commands can also be used as \eqn{latexascii} (only one argument) which then is used for both latex and ascii. No whitespace is allowed between command and the first argument, nor between the first and second arguments.
The following example is from Poisson.Rd:
\deqn{p(x) = \frac{\lambda^x e^{-\lambda}}{x!}}{%
p(x) = \lambda^x exp(-\lambda)/x!}
for \eqn{x = 0, 1, 2, \ldots}.
For text on-line help we get
p(x) = lambda^x exp(-lambda)/x!
for x = 0, 1, 2, ....
Greek letters (both cases) will be rendered in HTML if preceded by a backslash, \dots and \ldots will be rendered as ellipses and \sqrt, \ge and \le as mathematical symbols.
Note that only basic LaTeX can be used, there being no provision to specify LaTeX style files such as the AMS extensions.
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2.7 Figures
To include figures in help pages, use the \figure markup. There are three forms.
The two commonly used simple forms are \figure{filename} and \figure{filename}{alternate text}. This will include a copy of the figure in either HTML or LaTeX output. In text output, the alternate text will be displayed instead. (When the second argument is omitted, the filename will be used.) Both the filename and the alternate text will be parsed verbatim, and should not include special characters that are significant in HTML or LaTeX.
The expert form is \figure{filename}{options: string}. (The word ‘options:’ must be typed exactly as shown and followed by at least one space.) In this form, the string is copied into the HTML img tag as attributes following the src attribute, or into the second argument of the \Figure macro in LaTeX, which by default is used as options to an \includegraphics call. As it is unlikely that any single string would suffice for both display modes, the expert form would normally be wrapped in conditionals. It is up to the author to make sure that legal HTML/LaTeX is used. For example, to include a logo in both HTML (using the simple form) and LaTeX (using the expert form), the following could be used:
\if{html}{\figure{Rlogo.svg}{options: width=100 alt="R logo"}}
\if{latex}{\figure{Rlogo.pdf}{options: width=0.5in}}
The files containing the figures should be stored in the directory man/figures. Files with extensions .jpg, .jpeg, .pdf, .png and .svg from that directory will be copied to the help/figures directory at install time. (Figures in PDF format will not display in most HTML browsers, but might be the best choice in reference manuals.) Specify the filename relative to man/figures in the \figure directive.
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2.8 Insertions
Use \R for the R system itself. Use \dots for the dots in function argument lists ‘…’, and \ldots for ellipsis dots in ordinary text.113 These can be followed by {}, and should be unless followed by whitespace.
After an unescaped ‘%’, you can put your own comments regarding the help text. The rest of the line (but not the newline at the end) will be completely disregarded. Therefore, you can also use it to make part of the “help” invisible.
You can produce a backslash (‘\’) by escaping it by another backslash. (Note that \cr is used for generating line breaks.)
The “comment” character ‘%’ and unpaired braces114 almost always need to be escaped by ‘\’, and ‘\\’ can be used for backslash and needs to be when there are two or more adjacent backslashes. In R-like code quoted strings are handled slightly differently; see “Parsing Rd files” for details – in particular braces should not be escaped in quoted strings.
All of ‘% { } \’ should be escaped in LaTeX-like text.
Text which might need to be represented differently in different encodings should be marked by \enc, e.g. \enc{Jöreskog}{Joreskog} (with no whitespace between the braces) where the first argument will be used where encodings are allowed and the second should be ASCII (and is used for e.g. the text conversion in locales that cannot represent the encoded form). (This is intended to be used for individual words, not whole sentences or paragraphs.)
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2.9 Indices
The \alias command (see Documenting functions) is used to specify the “topics” documented, which should include all R objects in a package such as functions and variables, data sets, and S4 classes and methods (see Documenting S4 classes and methods). The on-line help system searches the index data base consisting of all alias topics.
In addition, it is possible to provide “concept index entries” using \concept, which can be used for help.search() lookups. E.g., file cor.test.Rd in the standard package stats contains
\concept{Kendall correlation coefficient}
\concept{Pearson correlation coefficient}
\concept{Spearman correlation coefficient}
so that e.g. ??Spearman will succeed in finding the help page for the test for association between paired samples using Spearman’s rho.
(Note that help.search() only uses “sections” of documentation objects with no additional markup.)
Each \concept entry should give a single index term (word or phrase), and not use any Rd markup.
If you want to cross reference such items from other help files via \link, you need to use \alias and not \concept.
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2.10 Platform-specific documentation
Sometimes the documentation needs to differ by platform. Currently two OS-specific options are available, ‘unix’ and ‘windows’, and lines in the help source file can be enclosed in
#ifdef OS
...
#endif
or
#ifndef OS
...
#endif
for OS-specific inclusion or exclusion. Such blocks should not be nested, and should be entirely within a block (that, is between the opening and closing brace of a section or item), or at top-level contain one or more complete sections.
If the differences between platforms are extensive or the R objects documented are only relevant to one platform, platform-specific Rd files can be put in a unix or windows subdirectory.
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2.11 Conditional text
Occasionally the best content for one output format is different from the best content for another. For this situation, the \if{format}{text} or \ifelse{format}{text}{alternate} markup is used. Here format is a comma separated list of formats in which the text should be rendered. The alternate will be rendered if the format does not match. Both text and alternate may be any sequence of text and markup.
Currently the following formats are recognized: example, html, latex and text. These select output for the corresponding targets. (Note that example refers to extracted example code rather than the displayed example in some other format.) Also accepted are TRUE (matching all formats) and FALSE (matching no formats). These could be the output of the \Sexpr macro (see Dynamic pages).
The \out{literal} macro would usually be used within the text part of \if{format}{text}. It causes the renderer to output the literal text exactly, with no attempt to escape special characters. For example, use the following to output the markup necessary to display the Greek letter in LaTeX or HTML, and the text string alpha in other formats:
\ifelse{latex}{\out{$\alpha$}}{\ifelse{html}{\out{α}}{alpha}}
Next: User-defined macros, Previous: Conditional text, Up: Writing R documentation files [Contents][Index]
2.12 Dynamic pages
Two macros supporting dynamically generated man pages are \Sexpr and \RdOpts. These are modelled after Sweave, and are intended to contain executable R expressions in the Rd file.
The main argument to \Sexpr must be valid R code that can be executed. It may also take options in square brackets before the main argument. Depending on the options, the code may be executed at package build time, package install time, or man page rendering time.
The options follow the same format as in Sweave, but different options are supported. Currently the allowed options and their defaults are:
eval=TRUE Whether the R code should be evaluated.
echo=FALSE Whether the R code should be echoed. If TRUE, a display will be given in a preformatted block. For example, \Sexpr[echo=TRUE]{ x <- 1 } will be displayed as
> x <- 1
keep.source=TRUE Whether to keep the author’s formatting when displaying the code, or throw it away and use a deparsed version.
results=text How should the results be displayed? The possibilities are:
- results=text Apply as.character() to the result of the code, and insert it as a text element.
- results=verbatim Print the results of the code just as if it was executed at the console, and include the printed results verbatim. (Invisible results will not print.)
- results=rd The result is assumed to be a character vector containing markup to be passed to parse_Rd(), with the result inserted in place. This could be used to insert computed aliases, for instance. parse_Rd() is called first with fragment = FALSE to allow a single Rd section macro to be inserted. If that fails, it is called again with fragment = TRUE, the older behavior.
- results=hide Insert no output.
strip.white=TRUE Remove leading and trailing white space from each line of output if strip.white=TRUE. With strip.white=all, also remove blank lines.
stage=install Control when this macro is run. Possible values are
- stage=build The macro is run when building a source tarball.
- stage=install The macro is run when installing from source.
- stage=render The macro is run when displaying the help page.
Conditionals such as #ifdef (see Platform-specific documentation) are applied after the build macros but before the install macros. In some situations (e.g. installing directly from a source directory without a tarball, or building a binary package) the above description is not literally accurate, but authors can rely on the sequence being build, #ifdef, install, render, with all stages executed.
Code is only run once in each stage, so a \Sexpr[results=rd] macro can output an \Sexpr macro designed for a later stage, but not for the current one or any earlier stage.
width, height, fig These options are currently allowed but ignored.
The \RdOpts macro is used to set new defaults for options to apply to following uses of \Sexpr.
For more details, see the online document “Parsing Rd files”.
Next: Encoding, Previous: Dynamic pages, Up: Writing R documentation files [Contents][Index]
2.13 User-defined macros
The \newcommand and \renewcommand macros allow new macros to be defined within an Rd file. These are similar but not identical to the same-named LaTeX macros.
They each take two arguments which are parsed verbatim. The first is the name of the new macro including the initial backslash, and the second is the macro definition. As in LaTeX, \newcommand requires that the new macro not have been previously defined, whereas \renewcommand allows existing macros (including all built-in ones) to be replaced. (This test is disabled by default, but may be enabled by setting the environment variable _WARN_DUPLICATE_RD_MACROS_ to a true value.)
Also as in LaTeX, the new macro may be defined to take arguments, and numeric placeholders such as #1 are used in the macro definition. However, unlike LaTeX, the number of arguments is determined automatically from the highest placeholder number seen in the macro definition. For example, a macro definition containing #1 and #3 (but no other placeholders) will define a three argument macro (whose second argument will be ignored). As in LaTeX, at most 9 arguments may be defined. If the # character is followed by a non-digit it will have no special significance. All arguments to user-defined macros will be parsed as verbatim text, and simple text-substitution will be used to replace the place-holders, after which the replacement text will be parsed.
A number of macros are defined in the file share/Rd/macros/system.Rd of the R source or home directory, and these will normally be available in all .Rd files. For example, that file contains the definition
\newcommand{\PR}{\Sexpr[results=rd]{tools:::Rd_expr_PR(#1)}}
which defines \PR to be a single argument macro; then code (typically used in the NEWS.Rd file) like
\PR{1234}
will expand to
\Sexpr[results=rd]{tools:::Rd_expr_PR(1234)}
when parsed.
Some macros that might be of general use are:
\CRANpkg{pkg} ¶
A package on CRAN
\sspace ¶
A single space (used after a period that does not end a sentence).
\doi{numbers} ¶
A digital object identifier (DOI).
See the system.Rd file in share/Rd/macros for more details and macro definitions, including macros \packageTitle, \packageDescription, \packageAuthor, \packageMaintainer, \packageDESCRIPTION and \packageIndices.
Packages may also define their own common macros; these would be stored in an .Rd file in man/macros in the package source and will be installed into help/macros when the package is installed. A package may also use the macros from a different package by listing the other package in the ‘RdMacros’ field in the DESCRIPTION file.
Next: Processing documentation files, Previous: User-defined macros, Up: Writing R documentation files [Contents][Index]
2.14 Encoding
Rd files are text files and so it is impossible to deduce the encoding they are written in unless ASCII: files with 8-bit characters could be UTF-8, Latin-1, Latin-9, KOI8-R, EUC-JP, etc. So an \encoding{} section must be used to specify the encoding if it is not ASCII. (The \encoding{} section must be on a line by itself, and in particular one containing no non-ASCII characters. The encoding declared in the DESCRIPTION file will be used if none is declared in the file.) The Rd files are converted to UTF-8 before parsing and so the preferred encoding for the files themselves is now UTF-8.
Wherever possible, avoid non-ASCII chars in Rd files, and even symbols such as ‘<’, ‘>’, ‘$’, ‘^’, ‘&’, ‘|’, ‘@’, ‘~’, and ‘*’ outside ‘verbatim’ environments (since they may disappear in fonts designed to render text). (Function showNonASCIIfile in package tools can help in finding non-ASCII bytes in the files.)
For convenience, encoding names ‘latin1’ and ‘latin2’ are always recognized: these and ‘UTF-8’ are likely to work fairly widely. However, this does not mean that all characters in UTF-8 will be recognized, and the coverage of non-Latin characters115 is fairly low. Using LaTeX inputenx (see ?Rd2pdf in R) will give greater coverage of UTF-8.
The \enc command (see Insertions) can be used to provide transliterations which will be used in conversions that do not support the declared encoding.
The LaTeX conversion converts the file to UTF-8 from the declared encoding, and includes a
\inputencoding{utf8}
command, and this needs to be matched by a suitable invocation of the \usepackage{inputenc} command. The R utility R CMD Rd2pdf looks at the converted code and includes the encodings used: it might for example use
\usepackage[utf8]{inputenc}
(Use of utf8 as an encoding requires LaTeX dated 2003/12/01 or later. Also, the use of Cyrillic characters in ‘UTF-8’ appears to also need ‘\usepackage[T2A]{fontenc}’, and R CMD Rd2pdf includes this conditionally on the file t2aenc.def being present and environment variable _R_CYRILLIC_TEX_ being set.)
Note that this mechanism works best with Latin letters: the coverage of UTF-8 in LaTeX is quite low.
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2.15 Processing documentation files
There are several commands to process Rd files from the system command line.
Using R CMD Rdconv one can convert R documentation format to other formats, or extract the executable examples for run-time testing. The currently supported conversions are to plain text, HTML and LaTeX as well as extraction of the examples.
R CMD Rd2pdf generates PDF output from documentation in Rd files, which can be specified either explicitly or by the path to a directory with the sources of a package. In the latter case, a reference manual for all documented objects in the package is created, including the information in the DESCRIPTION files.
R CMD Sweave and R CMD Stangle process vignette-like documentation files (e.g. Sweave vignettes with extension ‘.Snw’ or ‘.Rnw’, or other non-Sweave vignettes). R CMD Stangle is used to extract the R code fragments.
The exact usage and a detailed list of available options for all of these commands can be obtained by running R CMD command --help, e.g., R CMD Rdconv --help. All available commands can be listed using R --help (or Rcmd --help under Windows).
All of these work under Windows. You may need to have installed the the tools to build packages from source as described in the “R Installation and Administration” manual, although typically all that is needed is a LaTeX installation.
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2.16 Editing Rd files
It can be very helpful to prepare .Rd files using a editor which knows about their syntax and will highlight commands, indent to show the structure and detect mis-matched braces, and so on.
The system most commonly used for this is some version of Emacs (including XEmacs) with the ESS package (https://ESS.R-project.org/: it is often is installed with Emacs but may need to be loaded, or even installed, separately).
Another is the Eclipse IDE with the Stat-ET plugin (https://projects.eclipse.org/projects/science.statet), and (on Windows only) Tinn-R (https://sourceforge.net/projects/tinn-r/).
People have also used LaTeX mode in a editor, as .Rd files are rather similar to LaTeX files.
Some R front-ends provide editing support for .Rd files, for example RStudio (https://www.rstudio.com/).
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3 Tidying and profiling R code
R code which is worth preserving in a package and perhaps making available for others to use is worth documenting, tidying up and perhaps optimizing. The last two of these activities are the subject of this chapter.
Tidying R code
Profiling R code for speed
Profiling R code for memory use
Profiling compiled code
Next: Profiling R code for speed, Previous: Tidying and profiling R code, Up: Tidying and profiling R code [Contents][Index]
3.1 Tidying R code
R treats function code loaded from packages and code entered by users differently. By default code entered by users has the source code stored internally, and when the function is listed, the original source is reproduced. Loading code from a package (by default) discards the source code, and the function listing is re-created from the parse tree of the function.
Normally keeping the source code is a good idea, and in particular it avoids comments being removed from the source. However, we can make use of the ability to re-create a function listing from its parse tree to produce a tidy version of the function, for example with consistent indentation and spaces around operators. If the original source does not follow the standard format this tidied version can be much easier to read.
We can subvert the keeping of source in two ways.
The option keep.source can be set to FALSE before the code is loaded into R.
The stored source code can be removed by calling the removeSource() function, for example by
myfun <- removeSource(myfun)
In each case if we then list the function we will get the standard layout.
Suppose we have a file of functions myfuns.R that we want to tidy up. Create a file tidy.R containing
source("myfuns.R", keep.source = FALSE)
dump(ls(all.names = TRUE), file = "new.myfuns.R")
and run R with this as the source file, for example by R --vanilla < tidy.R or by pasting into an R session. Then the file new.myfuns.R will contain the functions in alphabetical order in the standard layout. Warning: comments in your functions will be lost.
The standard format provides a good starting point for further tidying. Although the deparsing cannot do so, we recommend the consistent use of the preferred assignment operator ‘<-’ (rather than ‘=’) for assignment. Many package authors use a version of Emacs (on a Unix-alike or Windows) to edit R code, using the ESS[S] mode of the ESS Emacs package. See R coding standards in R Internals for style options within the ESS[S] mode recommended for the source code of R itself.
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3.2 Profiling R code for speed
It is possible to profile R code on Windows and most116 Unix-alike versions of R.
The command Rprof is used to control profiling, and its help page can be consulted for full details. Profiling works by recording at fixed intervals117 (by default every 20 msecs) which line in which R function is being used, and recording the results in a file (default Rprof.out in the working directory). Then the function summaryRprof or the command-line utility R CMD Rprof Rprof.out can be used to summarize the activity.
As an example, consider the following code (from Venables & Ripley, 2002, pp. 225–6).
library(MASS); library(boot)
storm.fm <- nls(Time ~ b*Viscosity/(Wt - c), stormer,
start = c(b=30.401, c=2.2183))
st <- cbind(stormer, fit=fitted(storm.fm))
storm.bf <- function(rs, i) {
st$Time <- st$fit + rs[i]
tmp <- nls(Time ~ (b * Viscosity)/(Wt - c), st,
start = coef(storm.fm))
tmp$m$getAllPars()
}
rs <- scale(resid(storm.fm), scale = FALSE) # remove the mean
Rprof("boot.out")
storm.boot <- boot(rs, storm.bf, R = 4999) # slow enough to profile
Rprof(NULL)
Having run this we can summarize the results by
R CMD Rprof boot.out
Each sample represents 0.02 seconds.
Total run time: 22.52 seconds.
Total seconds: time spent in function and callees.
Self seconds: time spent in function alone.
% total % self
total seconds self seconds name
100.0 25.22 0.2 0.04 "boot"
99.8 25.18 0.6 0.16 "statistic"
96.3 24.30 4.0 1.02 "nls"
33.9 8.56 2.2 0.56 "<Anonymous>"
32.4 8.18 1.4 0.36 "eval"
31.8 8.02 1.4 0.34 ".Call"
28.6 7.22 0.0 0.00 "eval.parent"
28.5 7.18 0.3 0.08 "model.frame"
28.1 7.10 3.5 0.88 "model.frame.default"
17.4 4.38 0.7 0.18 "sapply"
15.0 3.78 3.2 0.80 "nlsModel"
12.5 3.16 1.8 0.46 "lapply"
12.3 3.10 2.7 0.68 "assign"
...
% self % total
self seconds total seconds name
5.7 1.44 7.5 1.88 "inherits"
4.0 1.02 96.3 24.30 "nls"
3.6 0.92 3.6 0.92 "$"
3.5 0.88 28.1 7.10 "model.frame.default"
3.2 0.80 15.0 3.78 "nlsModel"
2.8 0.70 9.8 2.46 "qr.coef"
2.7 0.68 12.3 3.10 "assign"
2.5 0.64 2.5 0.64 ".Fortran"
2.5 0.62 7.1 1.80 "qr.default"
2.2 0.56 33.9 8.56 "<Anonymous>"
2.1 0.54 5.9 1.48 "unlist"
2.1 0.52 7.9 2.00 "FUN"
...
This often produces surprising results and can be used to identify bottlenecks or pieces of R code that could benefit from being replaced by compiled code.
Two warnings: profiling does impose a small performance penalty, and the output files can be very large if long runs are profiled at the default sampling interval.
Profiling short runs can sometimes give misleading results. R from time to time performs garbage collection to reclaim unused memory, and this takes an appreciable amount of time which profiling will charge to whichever function happens to provoke it. It may be useful to compare profiling code immediately after a call to gc() with a profiling run without a preceding call to gc.
More detailed analysis of the output can be achieved by the tools in the CRAN packages proftools and profr: in particular these allow call graphs to be studied.
Next: Profiling compiled code, Previous: Profiling R code for speed, Up: Tidying and profiling R code [Contents][Index]
3.3 Profiling R code for memory use
Measuring memory use in R code is useful either when the code takes more memory than is conveniently available or when memory allocation and copying of objects is responsible for slow code. There are three ways to profile memory use over time in R code. The second and third require R to have been compiled with --enable-memory-profiling, which is not the default, but is currently used for the macOS and Windows binary distributions. All can be misleading, for different reasons.
In understanding the memory profiles it is useful to know a little more about R’s memory allocation. Looking at the results of gc() shows a division of memory into Vcells used to store the contents of vectors and Ncells used to store everything else, including all the administrative overhead for vectors such as type and length information. In fact the vector contents are divided into two pools. Memory for small vectors (by default 128 bytes or less) is obtained in large chunks and then parcelled out by R; memory for larger vectors is obtained directly from the operating system.
Some memory allocation is obvious in interpreted code, for example,
y <- x + 1
allocates memory for a new vector y. Other memory allocation is less obvious and occurs because R is forced to make good on its promise of ‘call-by-value’ argument passing. When an argument is passed to a function it is not immediately copied. Copying occurs (if necessary) only when the argument is modified. This can lead to surprising memory use. For example, in the ‘survey’ package we have
print.svycoxph <- function (x, ...)
{
print(x$survey.design, varnames = FALSE, design.summaries = FALSE, ...)
x$call <- x$printcall
NextMethod()
}
It may not be obvious that the assignment to x$call will cause the entire object x to be copied. This copying to preserve the call-by-value illusion is usually done by the internal C function duplicate.
The main reason that memory-use profiling is difficult is garbage collection. Memory is allocated at well-defined times in an R program, but is freed whenever the garbage collector happens to run.
Memory statistics from Rprof
Tracking memory allocations
Tracing copies of an object
Next: Tracking memory allocations, Previous: Profiling R code for memory use, Up: Profiling R code for memory use [Contents][Index]
3.3.1 Memory statistics from Rprof
The sampling profiler Rprof described in the previous section can be given the option memory.profiling=TRUE. It then writes out the total R memory allocation in small vectors, large vectors, and cons cells or nodes at each sampling interval. It also writes out the number of calls to the internal function duplicate, which is called to copy R objects. summaryRprof provides summaries of this information. The main reason that this can be misleading is that the memory use is attributed to the function running at the end of the sampling interval. A second reason is that garbage collection can make the amount of memory in use decrease, so a function appears to use little memory. Running under gctorture helps with both problems: it slows down the code to effectively increase the sampling frequency and it makes each garbage collection release a smaller amount of memory.
Next: Tracing copies of an object, Previous: Memory statistics from Rprof, Up: Profiling R code for memory use [Contents][Index]
3.3.2 Tracking memory allocations
The second method of memory profiling uses a memory-allocation profiler, Rprofmem(), which writes out a stack trace to an output file every time a large vector is allocated (with a user-specified threshold for ‘large’) or a new page of memory is allocated for the R heap. Summary functions for this output are still being designed.
Running the example from the previous section with
> Rprofmem("boot.memprof",threshold=1000)
> storm.boot <- boot(rs, storm.bf, R = 4999)
> Rprofmem(NULL)
shows that apart from some initial and final work in boot there are no vector allocations over 1000 bytes.
Previous: Tracking memory allocations, Up: Profiling R code for memory use [Contents][Index]
3.3.3 Tracing copies of an object
The third method of memory profiling involves tracing copies made of a specific (presumably large) R object. Calling tracemem on an object marks it so that a message is printed to standard output when the object is copied via duplicate or coercion to another type, or when a new object of the same size is created in arithmetic operations. The main reason that this can be misleading is that copying of subsets or components of an object is not tracked. It may be helpful to use tracemem on these components.
In the example above we can run tracemem on the data frame st
> tracemem(st)
[1] "<0x9abd5e0>"
> storm.boot <- boot(rs, storm.bf, R = 4)
memtrace[0x9abd5e0->0x92a6d08]: statistic boot
memtrace[0x92a6d08->0x92a6d80]: $<-.data.frame $<- statistic boot
memtrace[0x92a6d80->0x92a6df8]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x9271318]: statistic boot
memtrace[0x9271318->0x9271390]: $<-.data.frame $<- statistic boot
memtrace[0x9271390->0x9271408]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x914f558]: statistic boot
memtrace[0x914f558->0x914f5f8]: $<-.data.frame $<- statistic boot
memtrace[0x914f5f8->0x914f670]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x972cbf0]: statistic boot
memtrace[0x972cbf0->0x972cc68]: $<-.data.frame $<- statistic boot
memtrace[0x972cc68->0x972cd08]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x98ead98]: statistic boot
memtrace[0x98ead98->0x98eae10]: $<-.data.frame $<- statistic boot
memtrace[0x98eae10->0x98eae88]: $<-.data.frame $<- statistic boot
The object is duplicated fifteen times, three times for each of the R+1 calls to storm.bf. This is surprising, since none of the duplications happen inside nls. Stepping through storm.bf in the debugger shows that all three happen in the line
st$Time <- st$fit + rs[i]
Data frames are slower than matrices and this is an example of why. Using tracemem(st$Viscosity) does not reveal any additional copying.
Previous: Profiling R code for memory use, Up: Tidying and profiling R code [Contents][Index]
3.4 Profiling compiled code
Profiling compiled code is highly system-specific, but this section contains some hints gleaned from various R users. Some methods need to be different for a compiled executable and for dynamic/shared libraries/objects as used by R packages. We know of no good way to profile DLLs on Windows.
This chapter is based on reports from users and the information may not be current.
Linux
Solaris
macOS
Next: Solaris, Previous: Profiling compiled code, Up: Profiling compiled code [Contents][Index]
3.4.1 Linux
Options include using sprof for a shared object, and oprofile (see https://oprofile.sourceforge.io/news/) and perf (see https://perf.wiki.kernel.org/index.php/Tutorial) for any executable or shared object.
sprof
oprofile and operf
3.4.1.1 sprof
You can select shared objects to be profiled with sprof by setting the environment variable LD_PROFILE. For example
% setenv LD_PROFILE /path/to/R_HOME/library/stats/libs/stats.so
R
... run the boot example
% sprof /path/to/R_HOME/library/stats/libs/stats.so \
/var/tmp/path/to/R_HOME/library/stats/libs/stats.so.profile
Flat profile:
Each sample counts as 0.01 seconds.
% cumulative self self total
time seconds seconds calls us/call us/call name
76.19 0.32 0.32 0 0.00 numeric_deriv
16.67 0.39 0.07 0 0.00 nls_iter
7.14 0.42 0.03 0 0.00 getListElement
rm /var/tmp/path/to/R_HOME/library/stats/libs/stats.so.profile
... to clean up ...
It is possible that root access is needed to create the directories used for the profile data.
3.4.1.2 oprofile and operf
The oprofile project has two modes of operation. In what is now called ‘legacy’ mode, it is uses a daemon to collect information on a process (see below). Since version 0.9.8 (August 2012), the preferred mode is to use operf, so we discuss that first. The modes differ in how the profiling data is collected: it is analysed by tools such as opreport and oppannote in both.
Here is an example on x86_64 Linux using R 3.0.2. File pvec.R contains the part of the examples from pvec in package parallel:
library(parallel)
N <- 1e6
dates <- sprintf('%04d-%02d-%02d', as.integer(2000+rnorm(N)),
as.integer(runif(N, 1, 12)), as.integer(runif(N, 1, 28)))
system.time(a <- as.POSIXct(dates, format = "%Y-%m-%d"))
with timings from the final step
user system elapsed
0.371 0.237 0.612
R-level profiling by Rprof shows
self.time self.pct total.time total.pct
"strptime" 1.70 41.06 1.70 41.06
"as.POSIXct.POSIXlt" 1.40 33.82 1.42 34.30
"sprintf" 0.74 17.87 0.98 23.67
...
so the conversion from character to POSIXlt takes most of the time.
This can be run under operf and analysed by
operf R -f pvec.R
opreport
opreport -l /path/to/R_HOME/bin/exec/R
opannotate --source /path/to/R_HOME/bin/exec/R
## And for the system time
opreport -l /lib64/libc.so.6
The first report shows where (which library etc) the time was spent:
CPU_CLK_UNHALT...|
samples| %|
------------------
166761 99.9161 Rdev
CPU_CLK_UNHALT...|
samples| %|
------------------
70586 42.3276 no-vmlinux
56963 34.1585 libc-2.16.so
36922 22.1407 R
1584 0.9499 stats.so
624 0.3742 libm-2.16.so
...
The rest of the output is voluminous, and only extracts are shown below.
Most of the time within R is spent in
samples % image name symbol name
10397 28.5123 R R_gc_internal
5683 15.5848 R do_sprintf
3036 8.3258 R do_asPOSIXct
2427 6.6557 R do_strptime
2421 6.6392 R Rf_mkCharLenCE
1480 4.0587 R w_strptime_internal
1202 3.2963 R Rf_qnorm5
1165 3.1948 R unif_rand
675 1.8511 R mktime0
617 1.6920 R makelt
617 1.6920 R validate_tm
584 1.6015 R day_of_the_week
...
opannotate shows that 31% of the time in R is spent in memory.c, 21% in datetime.c and 7% in Rstrptime.h. The analysis for libc showed that calls to wcsftime dominated, so those calls were cached for R 3.0.3: the time spent in no-vmlinux (the kernel) was reduced dramatically.
On platforms which support it, call graphs can be produced by opcontrol --callgraph if collected via operf --callgraph.
The profiling data is by default stored in sub-directory oprofile_data of the current directory, which can be removed at the end of the session.
Another example, from sm version 2.2-5.4. The example for sm.variogram took a long time:
system.time(example(sm.variogram))
...
user system elapsed
5.543 3.202 8.785
including a lot of system time. Profiling just the slow part, the second plot, showed
samples| %|
------------------
381845 99.9885 R
CPU_CLK_UNHALT...|
samples| %|
------------------
187484 49.0995 sm.so
169627 44.4230 no-vmlinux
12636 3.3092 libgfortran.so.3.0.0
6455 1.6905 R
so the system time was almost all in the Linux kernel. It is possible to dig deeper if you have a matching uncompressed kernel with debug symbols to specify via --vmlinux: we did not.
In ‘legacy’ mode oprofile works by running a daemon which collects information. The daemon must be started as root, e.g.
% su
% opcontrol --no-vmlinux
% (optional, some platforms) opcontrol --callgraph=5
% opcontrol --start
% exit
Then as a user
% R
... run the boot example
% opcontrol --dump
% opreport -l /path/to/R_HOME/library/stats/libs/stats.so
...
samples % symbol name
1623 75.5939 anonymous symbol from section .plt
349 16.2552 numeric_deriv
113 5.2632 nls_iter
62 2.8878 getListElement
% opreport -l /path/to/R_HOME/bin/exec/R
...
samples % symbol name
76052 11.9912 Rf_eval
54670 8.6198 Rf_findVarInFrame3
37814 5.9622 Rf_allocVector
31489 4.9649 Rf_duplicate
28221 4.4496 Rf_protect
26485 4.1759 Rf_cons
23650 3.7289 Rf_matchArgs
21088 3.3250 Rf_findFun
19995 3.1526 findVarLocInFrame
14871 2.3447 Rf_evalList
13794 2.1749 R_Newhashpjw
13522 2.1320 R_gc_internal
...
Shutting down the profiler and clearing the records needs to be done as root.
Next: macOS, Previous: Linux, Up: Profiling compiled code [Contents][Index]
3.4.2 Solaris
On 64-bit (only) Solaris, the standard profiling tool gprof collects information from shared objects compiled with -pg.
Previous: Solaris, Up: Profiling compiled code [Contents][Index]
3.4.3 macOS
Developers have recommended sample (or Sampler.app, which is a GUI version), Shark (in version of Xcode up to those for Snow Leopard), and Instruments (part of Xcode, see https://help.apple.com/instruments/mac/current/).
Next: System and foreign language interfaces, Previous: Tidying and profiling R code, Up: Writing R Extensions [Contents][Index]
4 Debugging
This chapter covers the debugging of R extensions, starting with the ways to get useful error information and moving on to how to deal with errors that crash R.
Browsing
Debugging R code
Checking memory access
Debugging compiled code
Using Link-time Optimization
Next: Debugging R code, Previous: Debugging, Up: Debugging [Contents][Index]
4.1 Browsing
Most of the R-level debugging facilities are based around the built-in browser. This can be used directly by inserting a call to browser() into the code of a function (for example, using fix(my_function) ). When code execution reaches that point in the function, control returns to the R console with a special prompt. For example
> fix(summary.data.frame) ## insert browser() call after for() loop
> summary(women)
Called from: summary.data.frame(women)
Browse[1]> ls()
[1] "digits" "i" "lbs" "lw" "maxsum" "nm" "nr" "nv"
[9] "object" "sms" "z"
Browse[1]> maxsum
[1] 7
Browse[1]>
height weight
Min. :58.0 Min. :115.0
1st Qu.:61.5 1st Qu.:124.5
Median :65.0 Median :135.0
Mean :65.0 Mean :136.7
3rd Qu.:68.5 3rd Qu.:148.0
Max. :72.0 Max. :164.0
> rm(summary.data.frame)
At the browser prompt one can enter any R expression, so for example ls() lists the objects in the current frame, and entering the name of an object will118 print it. The following commands are also accepted
n
Enter ‘step-through’ mode. In this mode, hitting return executes the next line of code (more precisely one line and any continuation lines). Typing c will continue to the end of the current context, e.g. to the end of the current loop or function.
c
In normal mode, this quits the browser and continues execution, and just return works in the same way. cont is a synonym.
where
This prints the call stack. For example
> summary(women)
Called from: summary.data.frame(women)
Browse[1]> where
where 1: summary.data.frame(women)
where 2: summary(women)
Browse[1]>
Q
Quit both the browser and the current expression, and return to the top-level prompt.
Errors in code executed at the browser prompt will normally return control to the browser prompt. Objects can be altered by assignment, and will keep their changed values when the browser is exited. If really necessary, objects can be assigned to the workspace from the browser prompt (by using <<- if the name is not already in scope).
Next: Checking memory access, Previous: Browsing, Up: Debugging [Contents][Index]
4.2 Debugging R code
Suppose your R program gives an error message. The first thing to find out is what R was doing at the time of the error, and the most useful tool is traceback(). We suggest that this is run whenever the cause of the error is not immediately obvious. Errors are often reported to the R mailing lists as being in some package when traceback() would show that the error was being reported by some other package or base R. Here is an example from the regression suite.
> success <- c(13,12,11,14,14,11,13,11,12)
> failure <- c(0,0,0,0,0,0,0,2,2)
> resp <- cbind(success, failure)
> predictor <- c(0, 5^(0:7))
> glm(resp ~ 0+predictor, family = binomial(link="log"))
Error: no valid set of coefficients has been found: please supply starting values
> traceback()
3: stop("no valid set of coefficients has been found: please supply
starting values", call. = FALSE)
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
mustart = mustart, offset = offset, family = family, control = control,
intercept = attr(mt, "intercept") > 0)
1: glm(resp ~ 0 + predictor, family = binomial(link ="log"))
The calls to the active frames are given in reverse order (starting with the innermost). So we see the error message comes from an explicit check in glm.fit. (traceback() shows you all the lines of the function calls, which can be limited by setting option "deparse.max.lines".)
Sometimes the traceback will indicate that the error was detected inside compiled code, for example (from ?nls)
Error in nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE) :
step factor 0.000488281 reduced below ‘minFactor’ of 0.000976563
> traceback()
2: .Call(R_nls_iter, m, ctrl, trace)
1: nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE)
This will be the case if the innermost call is to .C, .Fortran, .Call, .External or .Internal, but as it is also possible for such code to evaluate R expressions, this need not be the innermost call, as in
> traceback()
9: gm(a, b, x)
8: .Call(R_numeric_deriv, expr, theta, rho, dir)
7: numericDeriv(form[[3]], names(ind), env)
6: getRHS()
5: assign("rhs", getRHS(), envir = thisEnv)
4: assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),
envir = thisEnv)
3: function (newPars)
{
setPars(newPars)
assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),
envir = thisEnv)
assign("dev", sum(resid^2), envir = thisEnv)
assign("QR", qr(.swts * attr(rhs, "gradient")), envir = thisEnv)
return(QR$rank < min(dim(QR$qr)))
}(c(-0.00760232418963883, 1.00119632515036))
2: .Call(R_nls_iter, m, ctrl, trace)
1: nls(yeps ~ gm(a, b, x), start = list(a = 0.12345, b = 0.54321))
Occasionally traceback() does not help, and this can be the case if S4 method dispatch is involved. Consider the following example
> xyd <- new("xyloc", x=runif(20), y=runif(20))
Error in as.environment(pkg) : no item called "package:S4nswv"
on the search list
Error in initialize(value, ...) : S language method selection got
an error when called from internal dispatch for function ‘initialize’
> traceback()
2: initialize(value, ...)
1: new("xyloc", x = runif(20), y = runif(20))
which does not help much, as there is no call to as.environment in initialize (and the note “called from internal dispatch” tells us so). In this case we searched the R sources for the quoted call, which occurred in only one place, methods:::.asEnvironmentPackage. So now we knew where the error was occurring. (This was an unusually opaque example.)
The error message
evaluation nested too deeply: infinite recursion / options(expressions=)?
can be hard to handle with the default value (5000). Unless you know that there actually is deep recursion going on, it can help to set something like
options(expressions=500)
and re-run the example showing the error.
Sometimes there is warning that clearly is the precursor to some later error, but it is not obvious where it is coming from. Setting options(warn = 2) (which turns warnings into errors) can help here.
Once we have located the error, we have some choices. One way to proceed is to find out more about what was happening at the time of the crash by looking a post-mortem dump. To do so, set options(error=dump.frames) and run the code again. Then invoke debugger() and explore the dump. Continuing our example:
> options(error = dump.frames)
> glm(resp ~ 0 + predictor, family = binomial(link ="log"))
Error: no valid set of coefficients has been found: please supply starting values
which is the same as before, but an object called last.dump has appeared in the workspace. (Such objects can be large, so remove it when it is no longer needed.) We can examine this at a later time by calling the function debugger.
> debugger()
Message: Error: no valid set of coefficients has been found: please supply starting values
Available environments had calls:
1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, mus
3: stop("no valid set of coefficients has been found: please supply starting values
Enter an environment number, or 0 to exit Selection:
which gives the same sequence of calls as traceback, but in outer-first order and with only the first line of the call, truncated to the current width. However, we can now examine in more detail what was happening at the time of the error. Selecting an environment opens the browser in that frame. So we select the function call which spawned the error message, and explore some of the variables (and execute two function calls).
Enter an environment number, or 0 to exit Selection: 2
Browsing in the environment with call:
glm.fit(x = X, y = Y, weights = weights, start = start, etas
Called from: debugger.look(ind)
Browse[1]> ls()
[1] "aic" "boundary" "coefold" "control" "conv"
[6] "dev" "dev.resids" "devold" "EMPTY" "eta"
[11] "etastart" "family" "fit" "good" "intercept"
[16] "iter" "linkinv" "mu" "mu.eta" "mu.eta.val"
[21] "mustart" "n" "ngoodobs" "nobs" "nvars"
[26] "offset" "start" "valideta" "validmu" "variance"
[31] "varmu" "w" "weights" "x" "xnames"
[36] "y" "ynames" "z"
Browse[1]> eta
1 2 3 4 5
0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04
6 7 8 9
-1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01
Browse[1]> valideta(eta)
[1] TRUE
Browse[1]> mu
1 2 3 4 5 6 7 8
1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755
9
0.8397616
Browse[1]> validmu(mu)
[1] FALSE
Browse[1]> c
Available environments had calls:
1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart
3: stop("no valid set of coefficients has been found: please supply starting v
Enter an environment number, or 0 to exit Selection: 0
> rm(last.dump)
Because last.dump can be looked at later or even in another R session, post-mortem debugging is possible even for batch usage of R. We do need to arrange for the dump to be saved: this can be done either using the command-line flag --save to save the workspace at the end of the run, or via a setting such as
> options(error = quote({dump.frames(to.file=TRUE); q()}))
See the help on dump.frames for further options and a worked example.
An alternative error action is to use the function recover():
> options(error = recover)
> glm(resp ~ 0 + predictor, family = binomial(link = "log"))
Error: no valid set of coefficients has been found: please supply starting values
Enter a frame number, or 0 to exit
1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart
Selection:
which is very similar to dump.frames. However, we can examine the state of the program directly, without dumping and re-loading the dump. As its help page says, recover can be routinely used as the error action in place of dump.calls and dump.frames, since it behaves like dump.frames in non-interactive use.
Post-mortem debugging is good for finding out exactly what went wrong, but not necessarily why. An alternative approach is to take a closer look at what was happening just before the error, and a good way to do that is to use debug. This inserts a call to the browser at the beginning of the function, starting in step-through mode. So in our example we could use
> debug(glm.fit)
> glm(resp ~ 0 + predictor, family = binomial(link ="log"))
debugging in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
mustart = mustart, offset = offset, family = family, control = control,
intercept = attr(mt, "intercept") > 0)
debug: {
## lists the whole function
Browse[1]>
debug: x <- as.matrix(x)
...
Browse[1]> start
[1] -2.235357e-06
debug: eta <- drop(x %*% start)
Browse[1]> eta
1 2 3 4 5
0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04
6 7 8 9
-1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01
Browse[1]>
debug: mu <- linkinv(eta <- eta + offset)
Browse[1]> mu
1 2 3 4 5 6 7 8
1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755
9
0.8397616
(The prompt Browse[1]> indicates that this is the first level of browsing: it is possible to step into another function that is itself being debugged or contains a call to browser().)
debug can be used for hidden functions and S3 methods by e.g. debug(stats:::predict.Arima). (It cannot be used for S4 methods, but an alternative is given on the help page for debug.) Sometimes you want to debug a function defined inside another function, e.g. the function arimafn defined inside arima. To do so, set debug on the outer function (here arima) and step through it until the inner function has been defined. Then call debug on the inner function (and use c to get out of step-through mode in the outer function).
To remove debugging of a function, call undebug with the argument previously given to debug; debugging otherwise lasts for the rest of the R session (or until the function is edited or otherwise replaced).
trace can be used to temporarily insert debugging code into a function, for example to insert a call to browser() just before the point of the error. To return to our running example
## first get a numbered listing of the expressions of the function
> page(as.list(body(glm.fit)), method="print")
> trace(glm.fit, browser, at=22)
Tracing function "glm.fit" in package "stats"
[1] "glm.fit"
> glm(resp ~ 0 + predictor, family = binomial(link ="log"))
Tracing glm.fit(x = X, y = Y, weights = weights, start = start,
etastart = etastart, .... step 22
Called from: eval(expr, envir, enclos)
Browse[1]> n
## and single-step from here.
> untrace(glm.fit)
For your own functions, it may be as easy to use fix to insert temporary code, but trace can help with functions in a namespace (as can fixInNamespace). Alternatively, use trace(,edit=TRUE) to insert code visually.
Next: Debugging compiled code, Previous: Debugging R code, Up: Debugging [Contents][Index]
4.3 Checking memory access
Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. Often the crash appears long after the invalid memory access: in particular damage to the structures which R itself has allocated may only become apparent at the next garbage collection (or even at later garbage collections after objects have been deleted).
Note that memory access errors may be seen with LAPACK, BLAS, OpenMP and Java-using packages: some at least of these seem to be intentional, and some are related to passing characters to Fortran.
Some of these tools can detect mismatched allocation and deallocation. C++ programmers should note that memory allocated by new [] must be freed by delete [], other uses of new by delete, and memory allocated by malloc, calloc and realloc by free. Some platforms will tolerate mismatches (perhaps with memory leaks) but others will segfault.
Using gctorture
Using valgrind
Using the Address Sanitizer
Using the Undefined Behaviour Sanitizer
Other analyses with ‘clang’
Other analyses with ‘gcc’
Using ‘Dr. Memory’
Fortran array bounds checking
Next: Using valgrind, Previous: Checking memory access, Up: Checking memory access [Contents][Index]
4.3.1 Using gctorture
We can help to detect memory problems in R objects earlier by running garbage collection as often as possible. This is achieved by gctorture(TRUE), which as described on its help page
Provokes garbage collection on (nearly) every memory allocation. Intended to ferret out memory protection bugs. Also makes R run very slowly, unfortunately.
The reference to ‘memory protection’ is to missing C-level calls to PROTECT/UNPROTECT (see Handling the effects of garbage collection) which if missing allow R objects to be garbage-collected when they are still in use. But it can also help with other memory-related errors.
Normally running under gctorture(TRUE) will just produce a crash earlier in the R program, hopefully close to the actual cause. See the next section for how to decipher such crashes.
It is possible to run all the examples, tests and vignettes covered by R CMD check under gctorture(TRUE) by using the option --use-gct.
The function gctorture2 provides more refined control over the GC torture process. Its arguments step, wait and inhibit_release are documented on its help page. Environment variables can also be used at the start of the R session to turn on GC torture: R_GCTORTURE corresponds to the step argument to gctorture2, R_GCTORTURE_WAIT to wait, and R_GCTORTURE_INHIBIT_RELEASE to inhibit_release.
If R is configured with --enable-strict-barrier then a variety of tests for the integrity of the write barrier are enabled. In addition tests to help detect protect issues are enabled:
All GCs are full GCs.
New nodes in small node pages are marked as NEWSXP on creation.
After a GC all free nodes that are not of type NEWSXP are marked as type FREESXP and their previous type is recorded.
Most calls to accessor functions check their SEXP inputs and SEXP outputs and signal an error if a FREESXP is found. The address of the node and the old type are included in the error message.
R CMD check --use-gct can be set to use gctorture2(n) rather than gctorture(TRUE) by setting environment variable _R_CHECK_GCT_N_ to a positive integer value to be used as n.
Used with a debugger and with gctorture or gctorture2 this mechanism can be helpful in isolating memory protect problems.
Next: Using the Address Sanitizer, Previous: Using gctorture, Up: Checking memory access [Contents][Index]
4.3.2 Using valgrind
If you have access to Linux on a common CPU type or supported versions of macOS or Solaris you can use valgrind (https://www.valgrind.org/, pronounced to rhyme with ‘tinned’) to check for possible problems. To run some examples under valgrind use something like
R -d valgrind --vanilla < mypkg-Ex.R
R -d "valgrind --tool=memcheck --leak-check=full" --vanilla < mypkg-Ex.R
where mypkg-Ex.R is a set of examples, e.g. the file created in mypkg.Rcheck by R CMD check. Occasionally this reports memory reads of ‘uninitialised values’ that are the result of compiler optimization, so can be worth checking under an unoptimized compile: for maximal information use a build with debugging symbols. We know there will be some small memory leaks from readline and R itself — these are memory areas that are in use right up to the end of the R session. Expect this to run around 20x slower than without valgrind, and in some cases much slower than that. Several versions of valgrind were not happy with some optimized BLASes that use CPU-specific instructions so you may need to build a version of R specifically to use with valgrind.
On platforms where valgrind is installed you can build a version of R with extra instrumentation to help valgrind detect errors in the use of memory allocated from the R heap. The configure option is --with-valgrind-instrumentation=level, where level is 0, 1 or 2. Level 0 is the default and does not add anything. Level 1 will detect some uses119 of uninitialised memory and has little impact on speed (compared to level 0). Level 2 will detect many other memory-use bugs120 but make R much slower when running under valgrind. Using this in conjunction with gctorture can be even more effective (and even slower).
An example of valgrind output is
==12539== Invalid read of size 4
==12539== at 0x1CDF6CBE: csc_compTr (Mutils.c:273)
==12539== by 0x1CE07E1E: tsc_transpose (dtCMatrix.c:25)
==12539== by 0x80A67A7: do_dotcall (dotcode.c:858)
==12539== by 0x80CACE2: Rf_eval (eval.c:400)
==12539== by 0x80CB5AF: R_execClosure (eval.c:658)
==12539== by 0x80CB98E: R_execMethod (eval.c:760)
==12539== by 0x1B93DEFA: R_standardGeneric (methods_list_dispatch.c:624)
==12539== by 0x810262E: do_standardGeneric (objects.c:1012)
==12539== by 0x80CAD23: Rf_eval (eval.c:403)
==12539== by 0x80CB2F0: Rf_applyClosure (eval.c:573)
==12539== by 0x80CADCC: Rf_eval (eval.c:414)
==12539== by 0x80CAA03: Rf_eval (eval.c:362)
==12539== Address 0x1C0D2EA8 is 280 bytes inside a block of size 1996 alloc'd
==12539== at 0x1B9008D1: malloc (vg_replace_malloc.c:149)
==12539== by 0x80F1B34: GetNewPage (memory.c:610)
==12539== by 0x80F7515: Rf_allocVector (memory.c:1915)
...
This example is from an instrumented version of R, while tracking down a bug in the Matrix package in 2006. The first line indicates that R has tried to read 4 bytes from a memory address that it does not have access to. This is followed by a C stack trace showing where the error occurred. Next is a description of the memory that was accessed. It is inside a block allocated by malloc, called from GetNewPage, that is, in the internal R heap. Since this memory all belongs to R, valgrind would not (and did not) detect the problem in an uninstrumented build of R. In this example the stack trace was enough to isolate and fix the bug, which was in tsc_transpose, and in this example running under gctorture() did not provide any additional information.
valgrind is good at spotting the use of uninitialized values: use option --track-origins=yes to show where these originated from. What it cannot detect is the misuse of arrays allocated on the stack: this includes C automatic variables and some121 Fortran arrays.
It is possible to run all the examples, tests and vignettes covered by R CMD check under valgrind by using the option --use-valgrind. If you do this you will need to select the valgrind options some other way, for example by having a ~/.valgrindrc file containing
--leak-check=full
--track-origins=yes
or setting the environment variable VALGRIND_OPTS.
On macOS you may need to ensure that debugging symbols are made available (so valgrind reports line numbers in files). This can usually be done with the valgrind option --dsymutil=yes to ask for the symbols to be dumped when the .so file is loaded. This will not work where packages are installed into a system area (such as the R.framework) and can be slow. Installing packages with R CMD INSTALL --dsym installs the dumped symbols. (This can also be done by setting environment variable PKG_MAKE_DSYM to a non-empty value before the INSTALL.)
This section has described the use of memtest, the default (and most useful) of valgrind’s tools. There are others described in its documentation: helgrind can be useful for threaded programs.
Next: Using the Undefined Behaviour Sanitizer, Previous: Using valgrind, Up: Checking memory access [Contents][Index]
4.3.3 Using the Address Sanitizer
AddressSanitizer (‘ASan’) is a tool with similar aims to the memory checker in valgrind. It is available with suitable builds122 of gcc and clang on common Linux and macOS platforms. See https://clang.llvm.org/docs/UsersManual.html#controlling-code-generation, https://clang.llvm.org/docs/AddressSanitizer.html and https://github.com/google/sanitizers.
More thorough checks of C++ code are done if the C++ library has been ‘annotated’: at the time of writing this applied to std::vector in libc++ for use with clang and gives rise to ‘container-overflow’123 reports.
It requires code to have been compiled and linked with -fsanitize=address and compiling with -fno-omit-frame-pointer will give more legible reports. It has a runtime penalty of 2–3x, extended compilation times and uses substantially more memory, often 1–2GB, at run time. On 64-bit platforms it reserves (but does not allocate) 16–20TB of virtual memory: restrictive shell settings can cause problems.
By comparison with valgrind, ASan can detect misuse of stack and global variables but not the use of uninitialized memory.
Recent versions return symbolic addresses for the location of the error provided llvm-symbolizer124 is on the path: if it is available but not on the path or has been renamed125, one can use an environment variable, e.g.
ASAN_SYMBOLIZER_PATH=/path/to/llvm-symbolizer
An alternative is to pipe the output through asan_symbolize.py126 and perhaps then (for compiled C++ code) c++filt. (On macOS, you may need to run dsymutil to get line-number reports.)
The simplest way to make use of this is to build a version of R with something like
CC="gcc -std=gnu99 -fsanitize=address"
CFLAGS="-fno-omit-frame-pointer -g -O2 -Wall -pedantic -mtune=native"
which will ensure that the libasan run-time library is compiled into the R executable. However this check can be enabled on a per-package basis by using a ~/.R/Makevars file like
CC = gcc -std=gnu99 -fsanitize=address -fno-omit-frame-pointer
CXX = g++ -fsanitize=address -fno-omit-frame-pointer
FC = gfortran -fsanitize=address
(Note that -fsanitize=address has to be part of the compiler specification to ensure it is used for linking. These settings will not be honoured by packages which ignore ~/.R/Makevars.) It will be necessary to build R with
MAIN_LDFLAGS = -fsanitize=address
to link the runtime libraries into the R executable if it was not specified as part of ‘CC’ when R was built. (For some builds without OpenMP, -pthread is also required.)
For options available via the environment variable ASAN_OPTIONS see https://github.com/google/sanitizers/wiki/AddressSanitizerFlags. With gcc additional control is available via the --param flag: see its man page.
For more detailed information on an error, R can be run under a debugger with a breakpoint set before the address sanitizer report is produced: for gdb or lldb you could use
break __asan_report_error
(See https://github.com/google/sanitizers/wiki/AddressSanitizerAndDebugger.)
More recent versions127 added the flag -fsanitize-address-use-after-scope: see https://github.com/google/sanitizers/wiki/AddressSanitizerUseAfterScope.
One of the checks done by ASan is that malloc/free and in C++ new/delete and new[]/delete[] are used consistently (rather than say free being used to dealloc memory allocated by new[]). This matters on some systems but not all: unfortunately on some of those where it does not matter, system libraries128 are not consistent. The check can be suppressed by including ‘alloc_dealloc_mismatch=0’ in ASAN_OPTIONS.
ASan also checks system calls and sometimes reports can refer to problems in the system software and not the package nor R. A couple of reports have been of ‘heap-use-after-free’ errors in the X11 libraries called from Tcl/Tk.
Using the Leak Sanitizer
Previous: Using the Address Sanitizer, Up: Using the Address Sanitizer [Contents][Index]
4.3.3.1 Using the Leak Sanitizer
For x86_64 Linux there is a leak sanitizer, ‘LSan’: see https://github.com/google/sanitizers/wiki/AddressSanitizerLeakSanitizer. This is available on recent versions of gcc and clang, and where available is compiled in as part of ASan.
One way to invoke this from an ASan-enabled build is by the environment variable
ASAN_OPTIONS='detect_leaks=1'
However, this was made the default as from clang 3.5 and gcc 5.1.0.
When LSan is enabled, leaks give the process a failure error status (by default 23). For an R package this means the R process, and as the parser retains some memory to the end of the process, if R itself was built against ASan all runs will have a failure error status (which may include running R as part of building R itself).
To disable this, allocation-mismatch checking and some strict C++ checking use
setenv ASAN_OPTIONS ‘alloc_dealloc_mismatch=0:detect_leaks=0:detect_odr_violation=0’
LSan also has a ‘stand-alone’ mode where it is compiled in using -fsanitize=leak and avoids the run-time overhead of ASan.
Next: Other analyses with ‘clang’, Previous: Using the Address Sanitizer, Up: Checking memory access [Contents][Index]
4.3.4 Using the Undefined Behaviour Sanitizer
‘Undefined behaviour’ is where the language standard does not require particular behaviour from the compiler. Examples include division by zero (where for doubles R requires the ISO/IEC 60559 behaviour but C/C++ do not), use of zero-length arrays, shifts too far for signed types (e.g. int x, y; y = x << 31;), out-of-range coercion, invalid C++ casts and mis-alignment. Not uncommon examples of out-of-range coercion in R packages are attempts to coerce a NaN or infinity to type int or NA_INTEGER to an unsigned type such as size_t. Also common is y[x - 1] forgetting that x might be NA_INTEGER.
‘UBSanitizer’ is a tool for C/C++ source code selected by -fsanitize=undefined in suitable builds129 of clang and GCC. Its (main) runtime library is linked into each package’s DLL, so it is less often needed to be included in MAIN_LDFLAGS. Platforms supported by clang are listed at https://clang.llvm.org/docs/UndefinedBehaviorSanitizer.html#supported-platforms: CRAN uses it for C/C++ with both GCC and clang on ‘x86_64’ Linux: the two toolchains often highlight different things with more reports from clang than GCC.
This sanitizer can be combined with the Address Sanitizer by -fsanitize=undefined,address (where both are supported).
Finer control of what is checked can be achieved by other options.
For clang see https://clang.llvm.org/docs/UndefinedBehaviorSanitizer.html#ubsan-checks. The current set is (on a single line):
-fsanitize=alignment,bool,bounds,builtin,enum,float-cast-overflow,
float-divide-by-zero,function,implicit-unsigned-integer-truncation,
implicit-signed-integer-truncation,implicit-integer-sign-change,
integer-divide-by-zero,nonnull-attribute,null,object-size,
pointer-overflow,return,returns-nonnull-attribute,shift,
signed-integer-overflow,unreachable,unsigned-integer-overflow,
unsigned-shift-base,vla-bound,vptr
(plus the more specific versions shift-base and shift-exponent) a subset of which could be combined with address, or use something like
-fsanitize=undefined -fno-sanitize=float-divide-by-zero
Options function, return and vptr apply only to C++: to use vptr its run-time library needs to be linked into the main R executable by building the latter with something like
MAIN_LD="clang++ -fsanitize=undefined"
Option float-divide-by-zero is undesirable for use with R which allow such divisions as part of IEC 60559 arithmetic, and in versions of clang since June 2019 it is no longer part of -fsanitize=undefined.
For GCC see https://gcc.gnu.org/onlinedocs/gcc/Instrumentation-Options.html (or the manual for your version of GCC, installed or via https://gcc.gnu.org/onlinedocs/: look for ‘Program Instrumentation Options’) for the options supported by GCC: 10 supported
-fsanitize=alignment,bool,bounds,builtin,enum,integer-divide-by-zero,
nonnull-attribute,null,object-size,pointer-overflow,return,
returns-nonnull-attribute,shift,signed-integer-overflow,
unreachable,vla-bound,vptr
plus the more specific versions shift-base and shift-exponent and non-default options
bound-strict,float-cast-overflow,float-divide-by-zero
where float-divide-by-zero is not desirable for R uses and bounds-strict is an extension of bounds.
Other useful flags include
-no-fsanitize-recover
which causes the first report to be fatal (it always is for the unreachable and return suboptions). For more detailed information on where the runtime error occurs, using
setenv UBSAN_OPTIONS ‘print_stacktrace=1’
will include a traceback in the report. Beyond that, R can be run under a debugger with a breakpoint set before the sanitizer report is produced: for gdb or lldb you could use
break __ubsan_handle_float_cast_overflow
break __ubsan_handle_float_cast_overflow_abort
or similar (there are handlers for each type of undefined behaviour).
There are also the compiler flags -fcatch-undefined-behavior and -ftrapv, said to be more reliable in clang than gcc.
For more details on the topic see https://blog.regehr.org/archives/213 and https://blog.llvm.org/2011/05/what-every-c-programmer-should-know.html (which has 3 parts).
It may or may not be possible to build R itself with -fsanitize=undefined: problems have been seen with OpenMP-using code with gcc but there has been success with clang.
Next: Other analyses with ‘gcc’, Previous: Using the Undefined Behaviour Sanitizer, Up: Checking memory access [Contents][Index]
4.3.5 Other analyses with ‘clang’
Recent versions of clang on ‘x86_64’ Linux have ‘ThreadSanitizer’ (https://github.com/google/sanitizers/wiki#threadsanitizer), a ‘data race detector for C/C++ programs’, and ‘MemorySanitizer’ (https://clang.llvm.org/docs/MemorySanitizer.html, https://github.com/google/sanitizers) for the detection of uninitialized memory. Both are based on and provide similar functionality to tools in valgrind.
clang has a ‘Static Analyzer’ which can be run on the source files during compilation: see https://clang-analyzer.llvm.org/.
Next: Using ‘Dr. Memory’, Previous: Other analyses with ‘clang’, Up: Checking memory access [Contents][Index]
4.3.6 Other analyses with ‘gcc’
GCC 10 introduced a new flag -fanalyzer which does static analysis during compilation, currently for C code. It is regarded as experimental and it may slow down computation considerably when problems are found (and use many GB of resident memory). There is some overlap with problems detected by the Undefined Behaviour sanitizer, but some issues are only reported by this tool and as it is a static analysis, it does not rely on code paths being exercised.
See https://gcc.gnu.org/onlinedocs/gcc-10.1.0/gcc/Static-Analyzer-Options.html (or the documentation for your version of gcc if later) and https://developers.redhat.com/blog/2020/03/26/static-analysis-in-gcc-10
Next: Fortran array bounds checking, Previous: Other analyses with ‘gcc’, Up: Checking memory access [Contents][Index]
4.3.7 Using ‘Dr. Memory’
‘Dr. Memory’ from https://drmemory.org/ is a memory checker for (currently) 32-bit Windows, Linux and macOS with similar aims to valgrind. It works with unmodified executables130 and detects memory access errors, uninitialized reads and memory leaks.
Previous: Using ‘Dr. Memory’, Up: Checking memory access [Contents][Index]
4.3.8 Fortran array bounds checking
Most of the Fortran compilers used with R allow code to be compiled with checking of array bounds: for example gfortran has option -fbounds-check and Oracle Developer Studio has -C. This will give an error when the upper or lower bound is exceeded, e.g.
At line 97 of file .../src/appl/dqrdc2.f
Fortran runtime error: Index ‘1’ of dimension 1 of array ‘x’ above upper bound of 0
One does need to be aware that lazy programmers often specify Fortran dimensions as 1 rather than * or a real bound and these will be reported (as may * dimensions)
It is easy to arrange to use this check on just the code in your package: add to ~/.R/Makevars something like (for gfortran)
FFLAGS = -g -O2 -mtune=native -fbounds-check
when you run R CMD check.
This may report errors with the way that Fortran character variables are passed, particularly when Fortran subroutines are called from C code and character lengths are not passed (see Fortran character strings).
Next: Using Link-time Optimization, Previous: Checking memory access, Up: Debugging [Contents][Index]
4.4 Debugging compiled code
Sooner or later programmers will be faced with the need to debug compiled code loaded into R. This section is geared to platforms using gdb with code compiled by gcc, but similar things are possible with other debuggers such as lldb (https://lldb.llvm.org/, used on macOS) and Sun’s dbx: some debuggers have graphical front-ends available.
Consider first ‘crashes’, that is when R terminated unexpectedly with an illegal memory access (a ‘segfault’ or ‘bus error’), illegal instruction or similar. Unix-alike versions of R use a signal handler which aims to give some basic information. For example
*** caught segfault ***
address 0x20000028, cause ‘memory not mapped’
Traceback:
1: .identC(class1[[1]], class2)
2: possibleExtends(class(sloti), classi, ClassDef2 = getClassDef(classi,
where = where))
3: validObject(t(cu))
4: stopifnot(validObject(cu <- as(tu, "dtCMatrix")), validObject(t(cu)),
validObject(t(tu)))
Possible actions:
1: abort (with core dump)
2: normal R exit
3: exit R without saving workspace
4: exit R saving workspace
Selection: 3
Since the R process may be damaged, the only really safe options are the first or third. (Note that a core dump is only produced where enabled: a common default in a shell is to limit its size to 0, thereby disabling it.)
A fairly common cause of such crashes is a package which uses .C or .Fortran and writes beyond (at either end) one of the arguments it is passed. There is a good way to detect this: using options(CBoundsCheck = TRUE) (which can be selected via the environment variable R_C_BOUNDS_CHECK=yes) changes the way .C and .Fortran work to check if the compiled code writes in the 64 bytes at either end of an argument.
Another cause of a ‘crash’ is to overrun the C stack. R tries to track that in its own code, but it may happen in third-party compiled code. For modern POSIX-compliant OSes R can safely catch that and return to the top-level prompt, so one gets something like
> .C("aaa")
Error: segfault from C stack overflow
>
However, C stack overflows are fatal under Windows and normally defeat attempts at debugging on that platform. Further, the size of the stack is set when R is compiled on Windows, whereas on POSIX OSes it can be set in the shell from which R is launched.
If you have a crash which gives a core dump you can use something like
gdb /path/to/R/bin/exec/R core.12345
to examine the core dump. If core dumps are disabled or to catch errors that do not generate a dump one can run R directly under a debugger by for example
$ R -d gdb --vanilla
...
gdb> run
at which point R will run normally, and hopefully the debugger will catch the error and return to its prompt. This can also be used to catch infinite loops or interrupt very long-running code. For a simple example
> for(i in 1:1e7) x <- rnorm(100)
[hit Ctrl-C]
Program received signal SIGINT, Interrupt.
0x00397682 in _int_free () from /lib/tls/libc.so.6
(gdb) where
#0 0x00397682 in _int_free () from /lib/tls/libc.so.6
#1 0x00397eba in free () from /lib/tls/libc.so.6
#2 0xb7cf2551 in R_gc_internal (size_needed=313)
at /users/ripley/R/svn/R-devel/src/main/memory.c:743
#3 0xb7cf3617 in Rf_allocVector (type=13, length=626)
at /users/ripley/R/svn/R-devel/src/main/memory.c:1906
#4 0xb7c3f6d3 in PutRNGstate ()
at /users/ripley/R/svn/R-devel/src/main/RNG.c:351
#5 0xb7d6c0a5 in do_random2 (call=0x94bf7d4, op=0x92580e8, args=0x9698f98,
rho=0x9698f28) at /users/ripley/R/svn/R-devel/src/main/random.c:183
...
In many cases it is possible to attach a debugger to a running process: this is helpful if an alternative front-end is in use or to investigate a task that seems to be taking far too long. This is done by something like
gdb -p pid
where pid is the id of the R executable or front-end process and can be found from within a running R process by calling Sys.getpid() or from a process monitor. This stops the process so its state can be examined: use continue to resume execution.
Some “tricks” worth knowing follow:
Finding entry points in dynamically loaded code
Inspecting R objects when debugging
Debugging on macOS
Next: Inspecting R objects when debugging, Previous: Debugging compiled code, Up: Debugging compiled code [Contents][Index]
4.4.1 Finding entry points in dynamically loaded code
Under most compilation environments, compiled code dynamically loaded into R cannot have breakpoints set within it until it is loaded. To use a symbolic debugger on such dynamically loaded code under Unix-alikes use
Call the debugger on the R executable, for example by R -d gdb.
Start R.
At the R prompt, use dyn.load or library to load your shared object.
Send an interrupt signal. This will put you back to the debugger prompt.
Set the breakpoints in your code.
Continue execution of R by typing signal 0RET.
Under Windows signals may not be able to be used, and if so the procedure is more complicated. See the rw-FAQ.
Next: Debugging on macOS, Previous: Finding entry points in dynamically loaded code, Up: Debugging compiled code [Contents][Index]
4.4.2 Inspecting R objects when debugging
The key to inspecting R objects from compiled code is the function PrintValue(SEXP s) which uses the normal R printing mechanisms to print the R object pointed to by s, or the safer version R_PV(SEXP s) which will only print ‘objects’.
One way to make use of PrintValue is to insert suitable calls into the code to be debugged.
Another way is to call R_PV from the symbolic debugger. (PrintValue is hidden as Rf_PrintValue.) For example, from gdb we can use
(gdb) p R_PV(ab)
using the object ab from the convolution example, if we have placed a suitable breakpoint in the convolution C code.
To examine an arbitrary R object we need to work a little harder. For example, let
R> DF <- data.frame(a = 1:3, b = 4:6)
By setting a breakpoint at do_get and typing get("DF") at the R prompt, one can find out the address in memory of DF, for example
Value returned is $1 = (SEXPREC *) 0x40583e1c
(gdb) p *$1
$2 = {
sxpinfo = {type = 19, obj = 1, named = 1, gp = 0,
mark = 0, debug = 0, trace = 0, = 0},
attrib = 0x40583e80,
u = {
vecsxp = {
length = 2,
type = {c = 0x40634700 "0>X@D>X@0>X@", i = 0x40634700,
f = 0x40634700, z = 0x40634700, s = 0x40634700},
truelength = 1075851272,
},
primsxp = {offset = 2},
symsxp = {pname = 0x2, value = 0x40634700, internal = 0x40203008},
listsxp = {carval = 0x2, cdrval = 0x40634700, tagval = 0x40203008},
envsxp = {frame = 0x2, enclos = 0x40634700},
closxp = {formals = 0x2, body = 0x40634700, env = 0x40203008},
promsxp = {value = 0x2, expr = 0x40634700, env = 0x40203008}
}
}
(Debugger output reformatted for better legibility).
Using R_PV() one can “inspect” the values of the various elements of the SEXP, for example,
(gdb) p R_PV($1->attrib)
$names
[1] "a" "b"
$row.names
[1] "1" "2" "3"
$class
[1] "data.frame"
$3 = void
To find out where exactly the corresponding information is stored, one needs to go “deeper”:
(gdb) set $a = $1->attrib
(gdb) p $a->u.listsxp.tagval->u.symsxp.pname->u.vecsxp.type.c
$4 = 0x405d40e8 "names"
(gdb) p $a->u.listsxp.carval->u.vecsxp.type.s[1]->u.vecsxp.type.c
$5 = 0x40634378 "b"
(gdb) p $1->u.vecsxp.type.s[0]->u.vecsxp.type.i[0]
$6 = 1
(gdb) p $1->u.vecsxp.type.s[1]->u.vecsxp.type.i[1]
$7 = 5
Another alternative is the R_inspect function which shows the low-level structure of the objects recursively (addresses differ from the above as this example is created on another machine):
(gdb) p R_inspect($1)
@100954d18 19 VECSXP g0c2 [OBJ,NAM(2),ATT] (len=2, tl=0)
@100954d50 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 1,2,3
@100954d88 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 4,5,6
ATTRIB:
@102a70140 02 LISTSXP g0c0 []
TAG: @10083c478 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "names"
@100954dc0 16 STRSXP g0c2 [NAM(2)] (len=2, tl=0)
@10099df28 09 CHARSXP g0c1 [MARK,gp=0x21] "a"
@10095e518 09 CHARSXP g0c1 [MARK,gp=0x21] "b"
TAG: @100859e60 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "row.names"
@102a6f868 13 INTSXP g0c1 [NAM(2)] (len=2, tl=1) -2147483648,-3
TAG: @10083c948 01 SYMSXP g0c0 [MARK,gp=0x4000] "class"
@102a6f838 16 STRSXP g0c1 [NAM(2)] (len=1, tl=1)
@1008c6d48 09 CHARSXP g0c2 [MARK,gp=0x21,ATT] "data.frame"
In general the representation of each object follows the format:
@<address> <type-nr> <type-name> <gc-info> [<flags>] ...
For a more fine-grained control over the depth of the recursion and the output of vectors R_inspect3 takes additional two character() parameters: maximum depth and the maximal number of elements that will be printed for scalar vectors. The defaults in R_inspect are currently -1 (no limit) and 5 respectively.
Previous: Inspecting R objects when debugging, Up: Debugging compiled code [Contents][Index]
4.4.3 Debugging on macOS
To debug code in a package it is easiest to unpack it in a directory and install it with
R CMD INSTALL --dsym pkgname
as macOS does not store debugging symbols in the .so file. (It is not necessary to have R built with debugging symbols, although compiling the package should be done including -g in CFLAGS / CXXFLAGS / FCFLAGS as appropriate.)
Security measures may prevent running a CRAN binary distribution of R under lldb or attaching this as a debugger (https://cran.r-project.org/bin/macosx/RMacOSX-FAQ.html#I-cannot-attach-debugger-to-R), although both were possible on High Sierra in mid-2020. This can also affect locally compiled builds, where attaching to an interactive R session under Big Sur worked in late 2020 after giving permission via a popup-up. (To debug in what Apple deems a non-interactive session, e.g. logged in remotely, see man DevToolsSecurity.)
Debugging a local build of R on macOS can raise additional hurdles as environment variables such as DYLD_FALLBACK_LIBRARY_PATH are not usually passed through131 the lldb process, resulting in messages like
R -d lldb
...
(lldb) run
Process 16828 launched: ‘/path/to/bin/exec/R’ (x86_64)
dyld: Library not loaded: libR.dylib
Referenced from: /path/to/bin/exec/R
A quick workaround is to symlink the dylibs under R_HOME/lib to somewhere where they will be found such as the current working directory. It would be possible to do as the distribution does132 and use install_name_tool, but that would have to be done for all the dylibs including those in packages.
It may be simplest to attach the debugger to a running process (see above). Specifically, run R and when it is at the prompt just before a command that is to be debugged, at a terminal
pw -ef | grep exec/R
# identify the pid pid for the next command
lldb -p pid
(lldb) continue
and then return to the R console.
For non-interactive use, one may need lldb --batch.
Previous: Debugging compiled code, Up: Debugging [Contents][Index]
4.5 Using Link-time Optimization
Where supported, link time optimization provides a comprehensive way to check the consistency of calls between Fortran files or between C and Fortran. Use this via R CMD INSTALL --use-LTO.
To set up support on a Unix-alike, See Link-Time Optimization in R Installation and Administration . On Linux using GCC without building R with LTO support, it should suffice to set
LTO_OPT = -flto
LTO_FC_OPT = -flto
AR = gcc-ar
NM = gcc-nm
in a personal (or site) Makevars file: See Customizing package compilation in R Installation and Administration ,
For Windows, first edit file etc/${R_ARCH}/Makeconf to give LTO_OPT the value -flto or do so in a personal/site Makevars file; see also file src/gnuwin32/README.compilation in the sources.
For example:
boot.f:61: warning: type of ‘ddot’ does not match original declaration [-Wlto-type-mismatch]
y(j,i)=ddot(p,x(j,1),n,b(1,j,i),1)
crq.f:1023: note: return value type mismatch
where the package author forgot to declare
double precision ddot
external ddot
in boot.f. That package had its own copy of ddot: to detect misuse of the one in R’s BLAS library would have needed R configured with --enable-lto=check.
Further examples:
rkpk2.f:77:5: warning: type of ‘dstup’ does not match original declaration [-Wlto-type-mismatch]
*info, wk)
rkpk1.f:2565:5: note: type mismatch in parameter 14
subroutine dstup (s, lds, nobs, nnull, qraux, jpvt, y, q, ldqr,
rkpk1.f:2565:5: note: ‘dstup’ was previously declared here
where the fourteenth argument dum was missing in the call.
reg.f:78:33: warning: type of ‘dqrdc’ does not match original declaration [-Wlto-type-mismatch]
call dqrdc (sr, nobs, nobs, nnull, wk, dum, dum, 0)
dstup.f:20: note: ‘dqrdc’ was previously declared here
call dqrdc (s, lds, nobs, nnull, qraux, jpvt, work, 1)
dqrdc is a LINPACK routine from R, jpvt is an integer array and work is a double precision one so dum cannot match both. (If --enable-lto=check had been used the comparison would have been with the definition in R.)
For Fortran files all in the package, most inconsistencies can be detected by concatenating the Fortran files and compiling the result, sometimes with clearer diagnostics than provided by LTO. For our last two examples this gives
all.f:2966:72:
*info, work1)
1
Warning: Missing actual argument for argument ‘dum’ at (1)
and
all.f:1663:72:
*ipvtwk), wk(ikwk), wk(iwork1), wk(iwork2), info)
1
Warning: Type mismatch in argument ‘jpvt’ at (1); passed REAL(8) to INTEGER(4)
Next: The R API: entry points for C code, Previous: Debugging, Up: Writing R Extensions [Contents][Index]
5 System and foreign language interfaces
Operating system access
Interface functions .C and .Fortran
dyn.load and dyn.unload
Registering native routines
Creating shared objects
Interfacing C++ code
Fortran I/O
Linking to other packages
Handling R objects in C
Interface functions .Call and .External
Evaluating R expressions from C
Parsing R code from C
External pointers and weak references
Vector accessor functions
Character encoding issues
Next: Interface functions .C and .Fortran, Previous: System and foreign language interfaces, Up: System and foreign language interfaces [Contents][Index]
5.1 Operating system access
Access to operating system functions is via the R functions system and system2. The details will differ by platform (see the on-line help), and about all that can safely be assumed is that the first argument will be a string command that will be passed for execution (not necessarily by a shell) and the second argument to system will be internal which if true will collect the output of the command into an R character vector.
On POSIX-compliant OSes these commands pass a command-line to a shell: Windows is not POSIX-compliant and there is a separate function shell to do so.
The function system.time is available for timing. Timing on child processes is only available on Unix-alikes, and may not be reliable there.
Next: dyn.load and dyn.unload, Previous: Operating system access, Up: System and foreign language interfaces [Contents][Index]
5.2 Interface functions .C and .Fortran
These two functions provide an interface to compiled code that has been linked into R, either at build time or via dyn.load (see dyn.load and dyn.unload). They are primarily intended for compiled C and Fortran code respectively, but the .C function can be used with other languages which can generate C interfaces, for example C++ (see Interfacing C++ code).
The first argument to each function is a character string specifying the symbol name as known133 to C or Fortran, that is the function or subroutine name. (That the symbol is loaded can be tested by, for example, is.loaded("cg"). Use the name you pass to .C or .Fortran rather than the translated symbol name.)
There can be up to 65 further arguments giving R objects to be passed to compiled code. Normally these are copied before being passed in, and copied again to an R list object when the compiled code returns. If the arguments are given names, these are used as names for the components in the returned list object (but not passed to the compiled code).
The following table gives the mapping between the modes of R atomic vectors and the types of arguments to a C function or Fortran subroutine.
R storage mode
C type
Fortran type
logical int * INTEGER
integer int * INTEGER
double double * DOUBLE PRECISION
complex Rcomplex * DOUBLE COMPLEX
character char ** CHARACTER(255)
raw unsigned char * none
On all R platforms int and INTEGER are 32-bit. Code ported from S-PLUS (which uses long * for logical and integer) will not work on all 64-bit platforms (although it may appear to work on some, including Windows). Note also that if your compiled code is a mixture of C functions and Fortran subprograms the argument types must match as given in the table above.
C type Rcomplex is a structure with double members r and i defined in the header file R_ext/Complex.h included by R.h. (On most platforms this is stored in a way compatible with the C99 double complex type: however, it may not be possible to pass Rcomplex to a C99 function expecting a double complex argument. Nor need it be compatible with a C++ complex type. Moreover, the compatibility can depend on the optimization level set for the compiler.)
Only a single character string of fixed length can be passed to or from Fortran (the length is not passed), and the success of this is compiler-dependent: its use was formally deprecated in 2019. Other R objects can be passed to .C, but it is much better to use one of the other interfaces.
It is possible to pass numeric vectors of storage mode double to C as float * or to Fortran as REAL by setting the attribute Csingle, most conveniently by using the R functions as.single, single or mode. This is intended only to be used to aid interfacing existing C or Fortran code.
Logical values are sent as 0 (FALSE), 1 (TRUE) or INT_MIN = -2147483648 (NA, but only if NAOK is true), and the compiled code should return one of these three values. (Non-zero values other than INT_MIN are mapped to TRUE.) Note that the use of int * for Fortran logical is not guaranteed to be portable (although people have gotten away with it for many years): it is better to pass integers and convert to/from Fortran logical in a Fortran wrapper.
Unless formal argument NAOK is true, all the other arguments are checked for missing values NA and for the IEEE special values NaN, Inf and -Inf, and the presence of any of these generates an error. If it is true, these values are passed unchecked.
Argument PACKAGE confines the search for the symbol name to a specific shared object (or use "base" for code compiled into R). Its use is highly desirable, as there is no way to avoid two package writers using the same symbol name, and such name clashes are normally sufficient to cause R to crash. (If it is not present and the call is from the body of a function defined in a package namespace, the shared object loaded by the first (if any) useDynLib directive will be used.)
Note that the compiled code should not return anything except through its arguments: C functions should be of type void and Fortran subprograms should be subroutines.
To fix ideas, let us consider a very simple example which convolves two finite sequences. (This is hard to do fast in interpreted R code, but easy in C code.) We could do this using .C by
void convolve(double *a, int *na, double *b, int *nb, double *ab)
{
int nab = *na + *nb - 1;
for(int i = 0; i < nab; i++)
ab[i] = 0.0;
for(int i = 0; i < *na; i++)
for(int j = 0; j < *nb; j++)
ab[i + j] += a[i] * b[j];
}
called from R by
conv <- function(a, b)
.C("convolve",
as.double(a),
as.integer(length(a)),
as.double(b),
as.integer(length(b)),
ab = double(length(a) + length(b) - 1))$ab
Note that we take care to coerce all the arguments to the correct R storage mode before calling .C; mistakes in matching the types can lead to wrong results or hard-to-catch errors.
Special care is needed in handling character vector arguments in C (or C++). On entry the contents of the elements are duplicated and assigned to the elements of a char ** array, and on exit the elements of the C array are copied to create new elements of a character vector. This means that the contents of the character strings of the char ** array can be changed, including to \0 to shorten the string, but the strings cannot be lengthened. It is possible134 to allocate a new string via R_alloc and replace an entry in the char ** array by the new string. However, when character vectors are used other than in a read-only way, the .Call interface is much to be preferred.
Passing character strings to Fortran code needs even more care, is deprecated and should be avoided where possible. Only the first element of the character vector is passed in, as a fixed-length (255) character array. Up to 255 characters are passed back to a length-one character vector. How well this works (or even if it works at all) depends on the C and Fortran compilers on each platform (including on their options). Often what is being passed to Fortran is one of a small set of possible values (a factor in R terms) which could alternatively be passed as an integer code: similarly Fortran code that wants to generate diagnostic messages could pass an integer code to a C or R wrapper which would convert it to a character string.
It is possible to pass some R objects other than atomic vectors via .C, but this is only supported for historical compatibility: use the .Call or .External interfaces for such objects. Any C/C++ code that includes Rinternals.h should be called via .Call or .External.
Next: Registering native routines, Previous: Interface functions .C and .Fortran, Up: System and foreign language interfaces [Contents][Index]
5.3 dyn.load and dyn.unload
Compiled code to be used with R is loaded as a shared object (Unix-alikes including macOS, see Creating shared objects for more information) or DLL (Windows).
The shared object/DLL is loaded by dyn.load and unloaded by dyn.unload. Unloading is not normally necessary and is not safe in general, but it is needed to allow the DLL to be re-built on some platforms, including Windows. Unloading a DLL and then re-loading a DLL of the same name may not work: Solaris uses the first version loaded. A DLL that registers C finalizers, but fails to unregister them when unloaded, may cause R to crash after unloading.
The first argument to both functions is a character string giving the path to the object. Programmers should not assume a specific file extension for the object/DLL (such as .so) but use a construction like
file.path(path1, path2, paste0("mylib", .Platform$dynlib.ext))
for platform independence. On Unix-alike systems the path supplied to dyn.load can be an absolute path, one relative to the current directory or, if it starts with ‘~’, relative to the user’s home directory.
Loading is most often done automatically based on the useDynLib() declaration in the NAMESPACE file, but may be done explicitly via a call to library.dynam. This has the form
library.dynam("libname", package, lib.loc)
where libname is the object/DLL name with the extension omitted. Note that the first argument, chname, should not be package since this will not work if the package is installed under another name.
Under some Unix-alike systems there is a choice of how the symbols are resolved when the object is loaded, governed by the arguments local and now. Only use these if really necessary: in particular using now=FALSE and then calling an unresolved symbol will terminate R unceremoniously.
R provides a way of executing some code automatically when a object/DLL is either loaded or unloaded. This can be used, for example, to register native routines with R’s dynamic symbol mechanism, initialize some data in the native code, or initialize a third party library. On loading a DLL, R will look for a routine within that DLL named R_init_lib where lib is the name of the DLL file with the extension removed. For example, in the command
library.dynam("mylib", package, lib.loc)
R looks for the symbol named R_init_mylib. Similarly, when unloading the object, R looks for a routine named R_unload_lib, e.g., R_unload_mylib. In either case, if the routine is present, R will invoke it and pass it a single argument describing the DLL. This is a value of type DllInfo which is defined in the Rdynload.h file in the R_ext directory.
Note that there are some implicit restrictions on this mechanism as the basename of the DLL needs to be both a valid file name and valid as part of a C entry point (e.g. it cannot contain ‘.’): for portable code it is best to confine DLL names to be ASCII alphanumeric plus underscore. If entry point R_init_lib is not found it is also looked for with ‘.’ replaced by ‘_’.
The following example shows templates for the initialization and unload routines for the mylib DLL.
#include <R_ext/Rdynload.h>
void
R_init_mylib(DllInfo *info)
{
/* Register routines,
allocate resources. */
}
void
R_unload_mylib(DllInfo *info)
{
/* Release resources. */
}
If a shared object/DLL is loaded more than once the most recent version is used.135 More generally, if the same symbol name appears in several shared objects, the most recently loaded occurrence is used. The PACKAGE argument and registration (see the next section) provide good ways to avoid any ambiguity in which occurrence is meant.
On Unix-alikes the paths used to resolve dynamically linked dependent libraries are fixed (for security reasons) when the process is launched, so dyn.load will only look for such libraries in the locations set by the R shell script (via etc/ldpaths) and in the OS-specific defaults.
Windows allows more control (and less security) over where dependent DLLs are looked for. On all versions this includes the PATH environment variable, but with lowest priority: note that it does not include the directory from which the DLL was loaded. It is possible to add a single path with quite high priority via the DLLpath argument to dyn.load. This is (by default) used by library.dynam to include the package’s libs/i386 or libs/x64 directory in the DLL search path.
Next: Creating shared objects, Previous: dyn.load and dyn.unload, Up: System and foreign language interfaces [Contents][Index]
5.4 Registering native routines
By ‘native’ routine, we mean an entry point in compiled code.
In calls to .C, .Call, .Fortran and .External, R must locate the specified native routine by looking in the appropriate shared object/DLL. By default, R uses the operating-system-specific dynamic loader to lookup the symbol in all136 loaded DLLs and the R executable or libraries it is linked to. Alternatively, the author of the DLL can explicitly register routines with R and use a single, platform-independent mechanism for finding the routines in the DLL. One can use this registration mechanism to provide additional information about a routine, including the number and type of the arguments, and also make it available to R programmers under a different name.
Registering routines has two main advantages: it provides a faster137 way to find the address of the entry point via tables stored in the DLL at compilation time, and it provides a run-time check that the entry point is called with the right number of arguments and, optionally, the right argument types.
To register routines with R, one calls the C routine R_registerRoutines. This is typically done when the DLL is first loaded within the initialization routine R_init_dll name described in dyn.load and dyn.unload. R_registerRoutines takes 5 arguments. The first is the DllInfo object passed by R to the initialization routine. This is where R stores the information about the methods. The remaining 4 arguments are arrays describing the routines for each of the 4 different interfaces: .C, .Call, .Fortran and .External. Each argument is a NULL-terminated array of the element types given in the following table:
.C R_CMethodDef
.Call R_CallMethodDef
.Fortran R_FortranMethodDef
.External R_ExternalMethodDef
Currently, the R_ExternalMethodDef type is the same as R_CallMethodDef type and contains fields for the name of the routine by which it can be accessed in R, a pointer to the actual native symbol (i.e., the routine itself), and the number of arguments the routine expects to be passed from R. For example, if we had a routine named myCall defined as
SEXP myCall(SEXP a, SEXP b, SEXP c);
we would describe this as
static const R_CallMethodDef callMethods[] = {
{"myCall", (DL_FUNC) &myCall, 3},
{NULL, NULL, 0}
};
along with any other routines for the .Call interface. For routines with a variable number of arguments invoked via the .External interface, one specifies -1 for the number of arguments which tells R not to check the actual number passed.
Routines for use with the .C and .Fortran interfaces are described with similar data structures, which have one optional additional field for describing the type of each argument. If specified, this field should be an array with the SEXP types describing the expected type of each argument of the routine. (Technically, the elements of the types array are of type R_NativePrimitiveArgType which is just an unsigned integer.) The R types and corresponding type identifiers are provided in the following table:
numeric REALSXP
integer INTSXP
logical LGLSXP
single SINGLESXP
character STRSXP
list VECSXP
Consider a C routine, myC, declared as
void myC(double *x, int *n, char **names, int *status);
We would register it as
static R_NativePrimitiveArgType myC_type[] = {
REALSXP, INTSXP, STRSXP, LGLSXP
};
static const R_CMethodDef cMethods[] = {
{"myC", (DL_FUNC) &myC, 4, myC_type},
{NULL, NULL, 0, NULL}
};
If registering types, check carefully that the number of types matches the number of arguments: as the type array (here myC_type) is passed as a pointer in C, the registration mechanism cannot check this for you.
Note that .Fortran entry points are mapped to lowercase, so registration should use lowercase only.
Having created the arrays describing each routine, the last step is to actually register them with R. We do this by calling R_registerRoutines. For example, if we have the descriptions above for the routines accessed by the .C and .Call we would use the following code:
void
R_init_myLib(DllInfo *info)
{
R_registerRoutines(info, cMethods, callMethods, NULL, NULL);
}
This routine will be invoked when R loads the shared object/DLL named myLib. The last two arguments in the call to R_registerRoutines are for the routines accessed by .Fortran and .External interfaces. In our example, these are given as NULL since we have no routines of these types.
When R unloads a shared object/DLL, its registrations are removed. There is no other facility for unregistering a symbol.
Examples of registering routines can be found in the different packages in the R source tree (e.g., stats and graphics). Also, there is a brief, high-level introduction in R News (volume 1/3, September 2001, pages 20–23, https://www.r-project.org/doc/Rnews/Rnews_2001-3.pdf).
Once routines are registered, they can be referred to as R objects if this is arranged in the useDynLib call in the package’s NAMESPACE file (see useDynLib). So for example the stats package has
# Refer to all C/Fortran routines by their name prefixed by C_
useDynLib(stats, .registration = TRUE, .fixes = "C_")
in its NAMESPACE file, and then ansari.test’s default methods can contain
pansari <- function(q, m, n)
.C(C_pansari, as.integer(length(q)), p = as.double(q),
as.integer(m), as.integer(n))$p
This avoids the overhead of looking up an entry point each time it is used, and ensures that the entry point in the package is the one used (without a PACKAGE = "pkg" argument).
R_init_ routines are often of the form
void attribute_visible R_init_mypkg(DllInfo *dll)
{
R_registerRoutines(dll, CEntries, CallEntries, FortEntries,
ExternalEntries);
R_useDynamicSymbols(dll, FALSE);
R_forceSymbols(dll, TRUE);
...
}
The R_useDynamicSymbols call says the DLL is not to be searched for entry points specified by character strings so .C etc calls will only find registered symbols: the R_forceSymbols call only allows .C etc calls which specify entry points by R objects such as C_pansari (and not by character strings). Each provides some protection against accidentally finding your entry points when people supply a character string without a package, and avoids slowing down such searches. (For the visibility attribute see Controlling visibility.)
In more detail, if a package mypkg contains entry points reg and unreg and the first is registered as a 0-argument .Call routine, we could use (from code in the package)
.Call("reg")
.Call("unreg")
Without or with registration, these will both work. If R_init_mypkg calls R_useDynamicSymbols(dll, FALSE), only the first will work. If in addition to registration the NAMESPACE file contains
useDynLib(mypkg, .registration = TRUE, .fixes = "C_")
then we can call .Call(C_reg). Finally, if R_init_mypkg also calls R_forceSymbols(dll, TRUE), only .Call(C_reg) will work (and not .Call("reg")). This is usually what we want: it ensures that all of our own .Call calls go directly to the intended code in our package and that no one else accidentally finds our entry points. (Should someone need to call our code from outside the package, for example for debugging, they can use .Call(mypkg:::C_reg).)
Speed considerations
Example: converting a package to use registration
Linking to native routines in other packages
Next: Example: converting a package to use registration, Previous: Registering native routines, Up: Registering native routines [Contents][Index]
5.4.1 Speed considerations
Sometimes registering native routines or using a PACKAGE argument can make a large difference. The results can depend quite markedly on the OS (and even if it is 32- or 64-bit), on the version of R and what else is loaded into R at the time.
To fix ideas, first consider x86_64 OS 10.7 and R 2.15.2. A simple .Call function might be
foo <- function(x) .Call("foo", x)
with C code
#include <Rinternals.h>
SEXP foo(SEXP x)
{
return x;
}
If we compile with by R CMD SHLIB foo.c, load the code by dyn.load("foo.so") and run foo(pi) it took around 22 microseconds (us). Specifying the DLL by
foo2 <- function(x) .Call("foo", x, PACKAGE = "foo")
reduced the time to 1.7 us.
Now consider making these functions part of a package whose NAMESPACE file uses useDynlib(foo). This immediately reduces the running time as "foo" will be preferentially looked for foo.dll. Without specifying PACKAGE it took about 5 us (it needs to fathom out the appropriate DLL each time it is invoked but it does not need to search all DLLs), and with the PACKAGE argument it is again about 1.7 us.
Next suppose the package has registered the native routine foo. Then foo() still has to find the appropriate DLL but can get to the entry point in the DLL faster, in about 4.2 us. And foo2() now takes about 1 us. If we register the symbols in the NAMESPACE file and use
foo3 <- function(x) .Call(C_foo, x)
then the address for the native routine is looked up just once when the package is loaded, and foo3(pi) takes about 0.8 us.
Versions using .C() rather than .Call() took about 0.2 us longer.
These are all quite small differences, but C routines are not uncommonly invoked millions of times for run times of a few microseconds each, and those doing such things may wish to be aware of the differences.
On Linux and Solaris there is a smaller overhead in looking up symbols.
Symbol lookup on Windows used to be far slower, so R maintains a small cache. If the cache is currently empty enough that the symbol can be stored in the cache then the performance is similar to Linux and Solaris: if not it may be slower. R’s own code always uses registered symbols and so these never contribute to the cache: however many other packages do rely on symbol lookup.
In more recent versions of R all the standard packages register native symbols and do not allow symbol search, so in a new session foo() can only look in foo.so and may be as fast as foo2(). This will no longer apply when many contributed packages are loaded, and generally those last loaded are searched first. For example, consider R 3.3.2 on x86_64 Linux. In an empty R session, both foo() and foo2() took about 0.75 us; however after packages igraph and spatstat had been loaded (which loaded another 12 DLLs), foo() took 3.6 us but foo2() still took about 0.80 us. Using registration in a package reduced this to 0.55 us and foo3() took 0.40 us, times which were unchanged when further packages were loaded.
Next: Linking to native routines in other packages, Previous: Speed considerations, Up: Registering native routines [Contents][Index]
5.4.2 Example: converting a package to use registration
The splines package was converted to use symbol registration in 2001, but we can use it as an example138 of what needs to be done for a small package.
Find the relevant entry points. This is somewhat OS-specific, but something like the following should be possible at the OS command-line
nm -g /path/to/splines.so | grep " T "
0000000000002670 T _spline_basis
0000000000001ec0 T _spline_value
This indicates that there are two relevant entry points. (They may or may not have a leading underscore, as here. Fortran entry points will have a trailing underscore.) Check in the R code that they are called by the package and how: in this case they are used by .Call.
Alternatively, examine the package’s R code for all .C, .Fortran, .Call and .External calls.
Construct the registration table. First write skeleton registration code, conventionally in file src/init.c (or at the end of the only C source file in the package: if included in a C++ file the ‘R_init’ function would need to be declared extern "C"):
#include <stdlib.h> // for NULL
#include <R_ext/Rdynload.h>
#define CALLDEF(name, n) {#name, (DL_FUNC) &name, n}
static const R_CallMethodDef R_CallDef[] = {
CALLDEF(spline_basis, ?),
CALLDEF(spline_value, ?),
{NULL, NULL, 0}
};
void R_init_splines(DllInfo *dll)
{
R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL);
}
and then replace the ? in the skeleton with the actual numbers of arguments. You will need to add declarations (also known as ‘prototypes’) of the functions unless appending to the only C source file. Some packages will already have these in a header file, or you could create one and include it in init.c, for example splines.h containing
#include <Rinternals.h> // for SEXP
extern SEXP spline_basis(SEXP knots, SEXP order, SEXP xvals, SEXP derivs);
extern SEXP spline_value(SEXP knots, SEXP coeff, SEXP order, SEXP x, SEXP deriv);
Tools are available to extract declarations, at least for C and C++ code: see the help file for package_native_routine_registration_skeleton in package tools. Here we could have used
cproto -I/path/to/R/include -e splines.c
For examples of registering other types of calls, see packages graphics and stats. In particular, when registering entry points for .Fortran one needs declarations as if called from C, such as
#include <R_ext/RS.h>
void F77_NAME(supsmu)(int *n, double *x, double *y,
double *w, int *iper, double *span, double *alpha,
double *smo, double *sc, double *edf);
gfortran 8.4, 9.2 and later can help generate such prototypes with its flag -fc-prototypes-external (although one will need to replace the hard-coded trailing underscore with the F77_NAME macro).
One can get away with inaccurate argument lists in the declarations: it is easy to specify the arguments for .Call (all SEXP) and .External (one SEXP) and as the arguments for .C and .Fortran are all pointers, specifying them as void * suffices. (For most platforms one can omit all the arguments, although link-time optimization will warn.)
Using -fc-prototypes-external will give a prototype using int_least32_t *lgl for Fortran LOGICAL LGL, but this is not portable and traditionally it has been assumed that the C/C++ equivalent was int *lgl. If adding a declaration just to register a .Fortran call, the most portable version is void *lgl.
(Optional but highly recommended.) Restrict .Call etc to use the symbols you chose to register by editing src/init.c to contain
void R_init_splines(DllInfo *dll)
{
R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL);
R_useDynamicSymbols(dll, FALSE);
}
A skeleton for the steps so far can be made using package_native_routine_registration_skeleton in package tools. This will optionally create declarations based on the usage in the R code.
The remaining steps are optional but recommended.
Edit the NAMESPACE file to create R objects for the registered symbols:
useDynLib(splines, .registration = TRUE, .fixes = "C_")
Find all the relevant calls in the R code and edit them to use the R objects. This entailed changing the lines
temp <- .Call("spline_basis", knots, ord, x, derivs, PACKAGE = "splines")
y[accept] <- .Call("spline_value", knots, coeff, ord, x[accept], deriv, PACKAGE = "splines")
y = .Call("spline_value", knots, coef(object), ord, x, deriv, PACKAGE = "splines")
to
temp <- .Call(C_spline_basis, knots, ord, x, derivs)
y[accept] <- .Call(C_spline_value, knots, coeff, ord, x[accept], deriv)
y = .Call(C_spline_value, knots, coef(object), ord, x, deriv)
Check that there is no exportPattern directive which unintentionally exports the newly created R objects.
Restrict .Call to use the R symbols by editing src/init.c to contain
void R_init_splines(DllInfo *dll)
{
R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL);
R_useDynamicSymbols(dll, FALSE);
R_forceSymbols(dll, TRUE);
}
Consider visibility. On some OSes we can hide entry points from the loader, which precludes any possible name clashes and calling them accidentally (usually with incorrect arguments and crashing the R process). If we repeat the first step we now see
nm -g /path/to/splines.so | grep " T "
0000000000002e00 T _R_init_splines
00000000000025e0 T _spline_basis
0000000000001e20 T _spline_value
If there were any entry points not intended to be used by the package we should try to avoid exporting them, for example by making them static. Now that the two relevant entry points are only accessed via the registration table, we can hide them. There are two ways to do so on some Unix-alikes. We can hide individual entry points via
#include <R_ext/Visibility.h>
SEXP attribute_hidden
spline_basis(SEXP knots, SEXP order, SEXP xvals, SEXP derivs)
…
SEXP attribute_hidden
spline_value(SEXP knots, SEXP coeff, SEXP order, SEXP x, SEXP deriv)
…
Alternatively, we can change the default visibility for all C symbols by including
PKG_CFLAGS = $(C_VISIBILITY)
in src/Makevars, and then we need to allow registration by declaring R_init_splines to be visible:
#include <R_ext/Visibility.h>
void attribute_visible
R_init_splines(DllInfo *dll)
…
See Controlling visibility for more details, including using Fortran code and ways to restrict visibility on Windows.
We end up with a file src/init.c containing
#include <stdlib.h>
#include <R_ext/Rdynload.h>
#include <R_ext/Visibility.h> // optional
#include "splines.h"
#define CALLDEF(name, n) {#name, (DL_FUNC) &name, n}
static const R_CallMethodDef R_CallDef[] = {
CALLDEF(spline_basis, 4),
CALLDEF(spline_value, 5),
{NULL, NULL, 0}
};
void
attribute_visible // optional
R_init_splines(DllInfo *dll)
{
R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL);
R_useDynamicSymbols(dll, FALSE);
R_forceSymbols(dll, TRUE);
}
Previous: Example: converting a package to use registration, Up: Registering native routines [Contents][Index]
5.4.3 Linking to native routines in other packages
In addition to registering C routines to be called by R, it can at times be useful for one package to make some of its C routines available to be called by C code in another package. The interface consists of two routines declared in header R_ext/Rdynload.h as
void R_RegisterCCallable(const char *package, const char *name,
DL_FUNC fptr);
DL_FUNC R_GetCCallable(const char *package, const char *name);
A package packA that wants to make a C routine myCfun available to C code in other packages would include the call
R_RegisterCCallable("packA", "myCfun", myCfun);
in its initialization function R_init_packA. A package packB that wants to use this routine would retrieve the function pointer with a call of the form
p_myCfun = R_GetCCallable("packA", "myCfun");
The author of packB is responsible for ensuring that p_myCfun has an appropriate declaration. In the future R may provide some automated tools to simplify exporting larger numbers of routines.
A package that wishes to make use of header files in other packages needs to declare them as a comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file. This then arranges for the include directories in the installed linked-to packages to be added to the include paths for C and C++ code.
It must specify139 ‘Imports’ or ‘Depends’ of those packages, for they have to be loaded140 prior to this one (so the path to their compiled code has been registered).
CRAN examples of the use of this mechanism include coxme linking to bdsmatrix and xts linking to zoo.
Next: Interfacing C++ code, Previous: Registering native routines, Up: System and foreign language interfaces [Contents][Index]
5.5 Creating shared objects
Shared objects for loading into R can be created using R CMD SHLIB. This accepts as arguments a list of files which must be object files (with extension .o) or sources for C, C++, Fortran, Objective C or Objective C++ (with extensions .c, .cc or .cpp, .f (fixed-form Fortran), .f90 or .f95 (free-form), .m, and .mm or .M, respectively), or commands to be passed to the linker. See R CMD SHLIB --help (or the R help for SHLIB) for usage information. Note that files intended for the Fortran pre-processor with extension .F are not accepted.
If compiling the source files does not work “out of the box”, you can specify additional flags by setting some of the variables PKG_CPPFLAGS (for the C/C++ preprocessor, mainly ‘-I’, ‘-D’ and ‘-U’ flags), PKG_CFLAGS, PKG_CXXFLAGS, PKG_FFLAGS, PKG_OBJCFLAGS, and PKG_OBJCXXFLAGS (for the C, C++, Fortran, Objective C, and Objective C++ compilers, respectively) in the file Makevars in the compilation directory (or, of course, create the object files directly from the command line). Similarly, variable PKG_LIBS in Makevars can be used for additional ‘-l’ and ‘-L’ flags to be passed to the linker when building the shared object. (Supplying linker commands as arguments to R CMD SHLIB will take precedence over PKG_LIBS in Makevars.)
It is possible to arrange to include compiled code from other languages by setting the macro ‘OBJECTS’ in file Makevars, together with suitable rules to make the objects.
Flags that are already set (for example in file etcR_ARCH/Makeconf) can be overridden by the environment variable MAKEFLAGS (at least for systems using a POSIX-compliant make), as in (Bourne shell syntax)
MAKEFLAGS="CFLAGS=-O3" R CMD SHLIB *.c
It is also possible to set such variables in personal Makevars files, which are read after the local Makevars and the system makefiles or in a site-wide Makevars.site file. See Customizing package compilation in R Installation and Administration ,
Note that as R CMD SHLIB uses Make, it will not remake a shared object just because the flags have changed, and if test.c and test.f both exist in the current directory
R CMD SHLIB test.f
will compile test.c!
If the src subdirectory of an add-on package contains source code with one of the extensions listed above or a file Makevars but not a file Makefile, R CMD INSTALL creates a shared object (for loading into R through useDynlib in the NAMESPACE, or in the .onLoad function of the package) using the R CMD SHLIB mechanism. If file Makevars exists it is read first, then the system makefile and then any personal Makevars files.
If the src subdirectory of package contains a file Makefile, this is used by R CMD INSTALL in place of the R CMD SHLIB mechanism. make is called with makefiles R_HOME/etcR_ARCH/Makeconf, src/Makefile and any personal Makevars files (in that order). The first target found in src/Makefile is used.
It is better to make use of a Makevars file rather than a Makefile: the latter should be needed only exceptionally.
Under Windows the same commands work, but Makevars.win will be used in preference to Makevars, and only src/Makefile.win will be used by R CMD INSTALL with src/Makefile being ignored. For past experiences of building DLLs with a variety of compilers, see file ‘README.packages’. Under Windows you can supply an exports definitions file called dllname-win.def: otherwise all entry points in objects (but not libraries) supplied to R CMD SHLIB will be exported from the DLL. An example is stats-win.def for the stats package: a CRAN example in package fastICA.
If you feel tempted to read the source code and subvert these mechanisms, please resist. Far too much developer time has been wasted in chasing down errors caused by failures to follow this documentation, and even more by package authors demanding explanations as to why their packages no longer work. In particular, undocumented environment or make variables are not for use by package writers and are subject to change without notice.
Next: Fortran I/O, Previous: Creating shared objects, Up: System and foreign language interfaces [Contents][Index]
5.6 Interfacing C++ code
Suppose we have the following hypothetical C++ library, consisting of the two files X.h and X.cpp, and implementing the two classes X and Y which we want to use in R.
// X.h
class X {
public: X (); ~X ();
};
class Y {
public: Y (); ~Y ();
};
// X.cpp
#include <R.h>
#include "X.h"
static Y y;
X::X() { REprintf("constructor X\n"); }
X::~X() { REprintf("destructor X\n"); }
Y::Y() { REprintf("constructor Y\n"); }
Y::~Y() { REprintf("destructor Y\n"); }
To use with R, the only thing we have to do is writing a wrapper function and ensuring that the function is enclosed in
extern "C" {
}
For example,
// X_main.cpp:
#include "X.h"
extern "C" {
void X_main () {
X x;
}
} // extern "C"
Compiling and linking should be done with the C++ compiler-linker (rather than the C compiler-linker or the linker itself); otherwise, the C++ initialization code (and hence the constructor of the static variable Y) are not called. On a properly configured system, one can simply use
R CMD SHLIB X.cpp X_main.cpp
to create the shared object, typically X.so (the file name extension may be different on your platform). Now starting R yields
R version 2.14.1 Patched (2012-01-16 r58124)
Copyright (C) 2012 The R Foundation for Statistical Computing
...
Type "q()" to quit R.
R> dyn.load(paste("X", .Platform$dynlib.ext, sep = ""))
constructor Y
R> .C("X_main")
constructor X
destructor X
list()
R> q()
Save workspace image? [y/n/c]: y
destructor Y
The R for Windows FAQ (rw-FAQ) contains details of how to compile this example under Windows.
Earlier versions of this example used C++ iostreams: this is best avoided. There is no guarantee that the output will appear in the R console, and indeed it will not on the R for Windows console. Use R code or the C entry points (see Printing) for all I/O if at all possible. Examples have been seen where merely loading a DLL that contained calls to C++ I/O upset R’s own C I/O (for example by resetting buffers on open files).
Most R header files can be included within C++ programs but they should not be included within an extern "C" block (as they include system headers141).
Legacy header S.h cannot be used with C++.
External C++ code
5.6.1 External C++ code
Quite a lot of external C++ software is header-only (e.g. most of the Boost ‘libraries’ including all those supplied by package BH, and most of Armadillo as supplied by package RcppArmadillo) and so is compiled when an R package which uses it is installed. This causes few problems.
A small number of external libraries used in R packages have a C++ interface to a library of compiled code, e.g. packages rgdal and rjags. This raises many more problems! The C++ interface uses name-mangling and the ABI142 may depend on the compiler, version and even C++ defines143, so requires the package C++ code to be compiled in exactly the same way as the library (and what that was is often undocumented). Examples include use of g++ vs clang++ or Solaris’ CC, and the two ABIs available for C++11 in g++ with different defaults for GCC 4.9 and 5.x in some Linux distributions.
Even fewer external libraries use C++ internally but present a C interface, such as rgeos. These require the C++ runtime library to be linked into the package’s shared object/DLL, and this is best done by including a dummy C++ file in the package sources.
There is a recent trend to link to the C++ interfaces offered by C software such as hdf5, pcre and ImageMagick. Their C interfaces are much preferred for portability (and can be used from C++ code). Also, the C++ interfaces are often optional in the software build or packaged separately and so users installing from package sources are far less likely to already have them installed.
Next: Linking to other packages, Previous: Interfacing C++ code, Up: System and foreign language interfaces [Contents][Index]
5.7 Fortran I/O
We have already warned against the use of C++ iostreams not least because output is not guaranteed to appear on the R console, and this warning applies equally to Fortran output to units * and 6. See Printing from Fortran, which describes workarounds.
In the past most Fortran compilers implemented I/O on top of the C I/O system and so the two interworked successfully. This was true of g77, but it is less true of gfortran as used in gcc 4 and later. In particular, any package that makes use of Fortran I/O will when compiled on Windows interfere with C I/O: when the Fortran I/O support code is initialized (typically when the package is loaded) the C stdout and stderr are switched to LF line endings. (Function init in file src/modules/lapack/init_win.c shows how to mitigate this. In a package this would look something like
#ifdef _WIN32
# include <fcntl.h>
#endif
void R_init_mypkgname(DllInfo *dll)
{
// Native symbol registration calls
#ifdef _WIN32
// gfortran I/O initialization sets these to _O_BINARY
setmode(1, _O_TEXT); /* stdout */
setmode(2, _O_TEXT); /* stderr */
#endif
}
in the file used for native symbol registration.)
Next: Handling R objects in C, Previous: Fortran I/O, Up: System and foreign language interfaces [Contents][Index]
5.8 Linking to other packages
It is not in general possible to link a DLL in package packA to a DLL provided by package packB (for the security reasons mentioned in dyn.load and dyn.unload, and also because some platforms distinguish between shared objects and dynamic libraries), but it is on Windows.
Note that there can be tricky versioning issues here, as package packB could be re-installed after package packA — it is desirable that the API provided by package packB remains backwards-compatible.
Shipping a static library in package packB for other packages to link to avoids most of the difficulties.
Unix-alikes
Windows
Next: Windows, Previous: Linking to other packages, Up: Linking to other packages [Contents][Index]
5.8.1 Unix-alikes
It is possible to link a shared object in package packA to a library provided by package packB under limited circumstances on a Unix-alike OS. There are severe portability issues, so this is not recommended for a distributed package.
This is easiest if packB provides a static library packB/lib/libpackB.a. (Note using directory lib rather than libs is conventional, and architecture-specific sub-directories may be needed and are assumed in the sample code below. The code in the static library will need to be compiled with PIC flags on platforms where it matters.) Then as the code from package packB is incorporated when package packA is installed, we only need to find the static library at install time for package packA. The only issue is to find package packB, and for that we can ask R by something like (long lines broken for display here)
PKGB_PATH=‘echo ’library(packB);
cat(system.file("lib", package="packB", mustWork=TRUE))' \
| "${R_HOME}/bin/R" --vanilla --no-echo`
PKG_LIBS="$(PKGB_PATH)$(R_ARCH)/libpackB.a"
For a dynamic library packB/lib/libpackB.so (packB/lib/libpackB.dylib on macOS: note that you cannot link to a shared object, .so, on that platform) we could use
PKGB_PATH=‘echo ’library(packB);
cat(system.file("lib", package="packB", mustWork=TRUE))' \
| "${R_HOME}/bin/R" --vanilla --no-echo`
PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -lpackB
This will work for installation, but very likely not when package packB is loaded, as the path to package packB’s lib directory is not in the ld.so144 search path. You can arrange to put it there before R is launched by setting (on some platforms) LD_RUN_PATH or LD_LIBRARY_PATH or adding to the ld.so cache (see man ldconfig). On platforms that support it, the path to the directory containing the dynamic library can be hardcoded at install time (which assumes that the location of package packB will not be changed nor the package updated to a changed API). On systems with the gcc or clang and the GNU linker (e.g. Linux) and some others this can be done by e.g.
PKGB_PATH=‘echo ’library(packB);
cat(system.file("lib", package="packB", mustWork=TRUE)))' \
| "${R_HOME}/bin/R" --vanilla --no-echo`
PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -Wl,-rpath,"$(PKGB_PATH)$(R_ARCH)" -lpackB
Some other systems (e.g. Solaris with its native linker) use -Rdir rather than -rpath,dir (and this is accepted by the compiler as well as the linker).
It may be possible to figure out what is required semi-automatically from the result of R CMD libtool --config (look for ‘hardcode’).
Making headers provided by package packB available to the code to be compiled in package packA can be done by the LinkingTo mechanism (see Registering native routines).
Previous: Unix-alikes, Up: Linking to other packages [Contents][Index]
5.8.2 Windows
Suppose package packA wants to make use of compiled code provided by packB in DLL packB/libs/exB.dll, possibly the package’s DLL packB/libs/packB.dll. (This can be extended to linking to more than one package in a similar way.) There are three issues to be addressed:
Making headers provided by package packB available to the code to be compiled in package packA.
This is done by the LinkingTo mechanism (see Registering native routines).
preparing packA.dll to link to packB/libs/exB.dll.
This needs an entry in Makevars.win of the form
PKG_LIBS= -L<something> -lexB
and one possibility is that <something> is the path to the installed pkgB/libs directory. To find that we need to ask R where it is by something like
PKGB_PATH=‘echo ’library(packB);
cat(system.file("libs", package="packB", mustWork=TRUE))' \
| rterm --vanilla --no-echo`
PKG_LIBS= -L"$(PKGB_PATH)$(R_ARCH)" -lexB
Another possibility is to use an import library, shipping with package packA an exports file exB.def. Then Makevars.win could contain
PKG_LIBS= -L. -lexB
all: $(SHLIB) before
before: libexB.dll.a
libexB.dll.a: exB.def
and then installing package packA will make and use the import library for exB.dll. (One way to prepare the exports file is to use pexports.exe.)
loading packA.dll which depends on exB.dll.
If exB.dll was used by package packB (because it is in fact packB.dll or packB.dll depends on it) and packB has been loaded before packA, then nothing more needs to be done as exB.dll will already be loaded into the R executable. (This is the most common scenario.)
More generally, we can use the DLLpath argument to library.dynam to ensure that exB.dll is found, for example by setting
library.dynam("packA", pkg, lib,
DLLpath = system.file("libs", package="packB"))
Note that DLLpath can only set one path, and so for linking to two or more packages you would need to resort to setting environment variable PATH.
Next: Interface functions .Call and .External, Previous: Linking to other packages, Up: System and foreign language interfaces [Contents][Index]
5.9 Handling R objects in C
Using C code to speed up the execution of an R function is often very fruitful. Traditionally this has been done via the .C function in R. However, if a user wants to write C code using internal R data structures, then that can be done using the .Call and .External functions. The syntax for the calling function in R in each case is similar to that of .C, but the two functions have different C interfaces. Generally the .Call interface is simpler to use, but .External is a little more general.
A call to .Call is very similar to .C, for example
.Call("convolve2", a, b)
The first argument should be a character string giving a C symbol name of code that has already been loaded into R. Up to 65 R objects can passed as arguments. The C side of the interface is
#include <R.h>
#include <Rinternals.h>
SEXP convolve2(SEXP a, SEXP b)
...
A call to .External is almost identical
.External("convolveE", a, b)
but the C side of the interface is different, having only one argument
#include <R.h>
#include <Rinternals.h>
SEXP convolveE(SEXP args)
...
Here args is a LISTSXP, a Lisp-style pairlist from which the arguments can be extracted.
In each case the R objects are available for manipulation via a set of functions and macros defined in the header file Rinternals.h or some S-compatibility macros145 See Interface functions .Call and .External for details on .Call and .External.
Before you decide to use .Call or .External, you should look at other alternatives. First, consider working in interpreted R code; if this is fast enough, this is normally the best option. You should also see if using .C is enough. If the task to be performed in C is simple enough involving only atomic vectors and requiring no call to R, .C suffices. A great deal of useful code was written using just .C before .Call and .External were available. These interfaces allow much more control, but they also impose much greater responsibilities so need to be used with care. Neither .Call nor .External copy their arguments: you should treat arguments you receive through these interfaces as read-only.
To handle R objects from within C code we use the macros and functions that have been used to implement the core parts of R. A public146 subset of these is defined in the header file Rinternals.h in the directory R_INCLUDE_DIR (default R_HOME/include) that should be available on any R installation.
A substantial amount of R, including the standard packages, is implemented using the functions and macros described here, so the R source code provides a rich source of examples and “how to do it”: do make use of the source code for inspirational examples.
It is necessary to know something about how R objects are handled in C code. All the R objects you will deal with will be handled with the type SEXP147, which is a pointer to a structure with typedef SEXPREC. Think of this structure as a variant type that can handle all the usual types of R objects, that is vectors of various modes, functions, environments, language objects and so on. The details are given later in this section and in R Internal Structures in R Internals , but for most purposes the programmer does not need to know them. Think rather of a model such as that used by Visual Basic, in which R objects are handed around in C code (as they are in interpreted R code) as the variant type, and the appropriate part is extracted for, for example, numerical calculations, only when it is needed. As in interpreted R code, much use is made of coercion to force the variant object to the right type.
Handling the effects of garbage collection
Allocating storage
Details of R types
Attributes
Classes
Handling lists
Handling character data
Finding and setting variables
Some convenience functions
Named objects and copying
Next: Allocating storage, Previous: Handling R objects in C, Up: Handling R objects in C [Contents][Index]
5.9.1 Handling the effects of garbage collection
We need to know a little about the way R handles memory allocation. The memory allocated for R objects is not freed by the user; instead, the memory is from time to time garbage collected. That is, some or all of the allocated memory not being used is freed or marked as re-usable.
The R object types are represented by a C structure defined by a typedef SEXPREC in Rinternals.h. It contains several things among which are pointers to data blocks and to other SEXPRECs. A SEXP is simply a pointer to a SEXPREC.
If you create an R object in your C code, you must tell R that you are using the object by using the PROTECT macro on a pointer to the object. This tells R that the object is in use so it is not destroyed during garbage collection. Notice that it is the object which is protected, not the pointer variable. It is a common mistake to believe that if you invoked PROTECT(p) at some point then p is protected from then on, but that is not true once a new object is assigned to p.
Protecting an R object automatically protects all the R objects pointed to in the corresponding SEXPREC, for example all elements of a protected list are automatically protected.
The programmer is solely responsible for housekeeping the calls to PROTECT. There is a corresponding macro UNPROTECT that takes as argument an int giving the number of objects to unprotect when they are no longer needed. The protection mechanism is stack-based, so UNPROTECT(n) unprotects the last n objects which were protected. The calls to PROTECT and UNPROTECT must balance when the user’s code returns and should balance in all functions. R will warn about "stack imbalance in .Call" (or .External) if the housekeeping is wrong.
Here is a small example of creating an R numeric vector in C code:
#include <R.h>
#include <Rinternals.h>
SEXP ab;
....
ab = PROTECT(allocVector(REALSXP, 2));
REAL(ab)[0] = 123.45;
REAL(ab)[1] = 67.89;
UNPROTECT(1);
Now, the reader may ask how the R object could possibly get removed during those manipulations, as it is just our C code that is running. As it happens, we can do without the protection in this example, but in general we do not know (nor want to know) what is hiding behind the R macros and functions we use, and any of them might cause memory to be allocated, hence garbage collection and hence our object ab to be removed. It is usually wise to err on the side of caution and assume that any of the R macros and functions might remove the object.
In some cases it is necessary to keep better track of whether protection is really needed. Be particularly aware of situations where a large number of objects are generated. The pointer protection stack has a fixed size (default 10,000) and can become full. It is not a good idea then to just PROTECT everything in sight and UNPROTECT several thousand objects at the end. It will almost invariably be possible to either assign the objects as part of another object (which automatically protects them) or unprotect them immediately after use.
There is a less-used macro UNPROTECT_PTR(s) that unprotects the object pointed to by the SEXP s, even if it is not the top item on the pointer protection stack. This macro was introduced for use in the parser, where the code interfacing with the R heap is generated and the generator cannot be configured to insert proper calls to PROTECT and UNPROTECT. However, UNPROTECT_PTR is dangerous to use in combination with UNPROTECT when the same object has been protected multiple times. It has been superseded by multi-set based functions R_PreserveInMSet and R_ReleaseFromMSet, which protect objects in a multi-set created by R_NewPreciousMSet and typically itself protected using PROTECT. These functions should not be needed outside parsers.
Sometimes an object is changed (for example duplicated, coerced or grown) yet the current value needs to be protected. For these cases PROTECT_WITH_INDEX saves an index of the protection location that can be used to replace the protected value using REPROTECT. For example (from the internal code for optim)
PROTECT_INDEX ipx;
....
PROTECT_WITH_INDEX(s = eval(OS->R_fcall, OS->R_env), &ipx);
REPROTECT(s = coerceVector(s, REALSXP), ipx);
Note that it is dangerous to mix UNPROTECT_PTR also with PROTECT_WITH_INDEX, as the former changes the protection locations of objects that were protected after the one being unprotected.
There is another way to avoid the effects of garbage collection: a call to R_PreserveObject adds an object to an internal list of objects not to be collects, and a subsequent call to R_ReleaseObject removes it from that list. This provides a way for objects which are not returned as part of R objects to be protected across calls to compiled code: on the other hand it becomes the user’s responsibility to release them when they are no longer needed (and this often requires the use of a finalizer). It is less efficient than the normal protection mechanism, and should be used sparingly.
For functions from packages as well as R to safely co-operate in protecting objects, certain rules have to be followed:
Pointer-protection balance. Calls to PROTECT and UNPROTECT should balance in each function. A function may only call UNPROTECT or REPROTECT on objects it has itself protected. Note that the pointer protection stack balance is restored automatically on non-local transfer of control (See Condition handling and cleanup code.), as if a call to UNPROTECT was invoked with the right argument.
Caller protection. It is the responsibility of the caller that all arguments passed to a function are protected and will stay protected for the whole execution of the callee. Typically this is achieved by PROTECT and UNPROTECT calls.
Protecting return values. Any R objects returned from a function are unprotected (the callee must maintain pointer-protection balance), and hence should be protected immediately by the caller. To be safe against future code changes, assume that any R object returned from any function may need protection. Note that even when conceptually returning an existing protected object, that object may be duplicated.
All functions/macros allocate. To be safe against future code changes, assume that any function or macro may allocate and hence garbage collector may run and destroy unprotected objects.
It is always safe and recommended to follow those rules. In fact, several R functions and macros protect their own arguments and some functions do not allocate or do not allocate when used in a certain way, but that is subject to change, so relying on that may be fragile. PROTECT and PROTECT_WITH_INDEX can be safely called with unprotected arguments and UNPROTECT does not allocate.
Next: Details of R types, Previous: Handling the effects of garbage collection, Up: Handling R objects in C [Contents][Index]
5.9.2 Allocating storage
For many purposes it is sufficient to allocate R objects and manipulate those. There are quite a few allocXxx functions defined in Rinternals.h—you may want to explore them.
One that is commonly used is allocVector, the C-level equivalent of R-level vector() and its wrappers such as integer() and character(). One distinction is that whereas the R functions always initialize the elements of the vector, allocVector only does so for lists, expressions and character vectors (the cases where the elements are themselves R objects).
If storage is required for C objects during the calculations this is best allocating by calling R_alloc; see Memory allocation. All of these memory allocation routines do their own error-checking, so the programmer may assume that they will raise an error and not return if the memory cannot be allocated.
Next: Attributes, Previous: Allocating storage, Up: Handling R objects in C [Contents][Index]
5.9.3 Details of R types
Users of the Rinternals.h macros will need to know how the R types are known internally. The different R data types are represented in C by SEXPTYPE. Some of these are familiar from R and some are internal data types. The usual R object modes are given in the table.
SEXPTYPE
R equivalent
REALSXP numeric with storage mode double
INTSXP integer
CPLXSXP complex
LGLSXP logical
STRSXP character
VECSXP list (generic vector)
LISTSXP pairlist
DOTSXP a ‘…’ object
NILSXP NULL
SYMSXP name/symbol
CLOSXP function or function closure
ENVSXP environment
Among the important internal SEXPTYPEs are LANGSXP, CHARSXP, PROMSXP, etc. (N.B.: although it is possible to return objects of internal types, it is unsafe to do so as assumptions are made about how they are handled which may be violated at user-level evaluation.) More details are given in R Internal Structures in R Internals .
Unless you are very sure about the type of the arguments, the code should check the data types. Sometimes it may also be necessary to check data types of objects created by evaluating an R expression in the C code. You can use functions like isReal, isInteger and isString to do type checking. Other such functions declared in the header file Rinternals.h include isNull, isSymbol, isLogical, isComplex, isExpression, and isEnvironment. All of these take a SEXP as argument and return 1 or 0 to indicate TRUE or FALSE.
What happens if the SEXP is not of the correct type? Sometimes you have no other option except to generate an error. You can use the function error for this. It is usually better to coerce the object to the correct type. For example, if you find that an SEXP is of the type INTEGER, but you need a REAL object, you can change the type by using
newSexp = PROTECT(coerceVector(oldSexp, REALSXP));
Protection is needed as a new object is created; the object formerly pointed to by the SEXP is still protected but now unused.148
All the coercion functions do their own error-checking, and generate NAs with a warning or stop with an error as appropriate.
Note that these coercion functions are not the same as calling as.numeric (and so on) in R code, as they do not dispatch on the class of the object. Thus it is normally preferable to do the coercion in the calling R code.
So far we have only seen how to create and coerce R objects from C code, and how to extract the numeric data from numeric R vectors. These can suffice to take us a long way in interfacing R objects to numerical algorithms, but we may need to know a little more to create useful return objects.
Next: Classes, Previous: Details of R types, Up: Handling R objects in C [Contents][Index]
5.9.4 Attributes
Many R objects have attributes: some of the most useful are classes and the dim and dimnames that mark objects as matrices or arrays. It can also be helpful to work with the names attribute of vectors.
To illustrate this, let us write code to take the outer product of two vectors (which outer and %o% already do). As usual the R code is simple
out <- function(x, y)
{
storage.mode(x) <- storage.mode(y) <- "double"
.Call("out", x, y)
}
where we expect x and y to be numeric vectors (possibly integer), possibly with names. This time we do the coercion in the calling R code.
C code to do the computations is
#include <R.h>
#include <Rinternals.h>
SEXP out(SEXP x, SEXP y)
{
int nx = length(x), ny = length(y);
SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny));
double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans);
for(int i = 0; i < nx; i++) {
double tmp = rx[i];
for(int j = 0; j < ny; j++)
rans[i + nx*j] = tmp * ry[j];
}
UNPROTECT(1);
return ans;
}
Note the way REAL is used: as it is a function call it can be considerably faster to store the result and index that.
However, we would like to set the dimnames of the result. We can use
#include <R.h>
#include <Rinternals.h>
SEXP out(SEXP x, SEXP y)
{
int nx = length(x), ny = length(y);
SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny));
double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans);
for(int i = 0; i < nx; i++) {
double tmp = rx[i];
for(int j = 0; j < ny; j++)
rans[i + nx*j] = tmp * ry[j];
}
SEXP dimnames = PROTECT(allocVector(VECSXP, 2));
SET_VECTOR_ELT(dimnames, 0, getAttrib(x, R_NamesSymbol));
SET_VECTOR_ELT(dimnames, 1, getAttrib(y, R_NamesSymbol));
setAttrib(ans, R_DimNamesSymbol, dimnames);
UNPROTECT(2);
return ans;
}
This example introduces several new features. The getAttrib and setAttrib functions get and set individual attributes. Their second argument is a SEXP defining the name in the symbol table of the attribute we want; these and many such symbols are defined in the header file Rinternals.h.
There are shortcuts here too: the functions namesgets, dimgets and dimnamesgets are the internal versions of the default methods of names<-, dim<- and dimnames<- (for vectors and arrays), and there are functions such as GetColNames, GetRowNames, GetMatrixDimnames and GetArrayDimnames.
What happens if we want to add an attribute that is not pre-defined? We need to add a symbol for it via a call to install. Suppose for illustration we wanted to add an attribute "version" with value 3.0. We could use
SEXP version;
version = PROTECT(allocVector(REALSXP, 1));
REAL(version)[0] = 3.0;
setAttrib(ans, install("version"), version);
UNPROTECT(1);
Using install when it is not needed is harmless and provides a simple way to retrieve the symbol from the symbol table if it is already installed. However, the lookup takes a non-trivial amount of time, so consider code such as
static SEXP VerSymbol = NULL;
...
if (VerSymbol == NULL) VerSymbol = install("version");
if it is to be done frequently.
This example can be simplified by another convenience function:
SEXP version = PROTECT(ScalarReal(3.0));
setAttrib(ans, install("version"), version);
UNPROTECT(1);
Next: Handling lists, Previous: Attributes, Up: Handling R objects in C [Contents][Index]
5.9.5 Classes
In R the class is just the attribute named "class" so it can be handled as such, but there is a shortcut classgets. Suppose we want to give the return value in our example the class "mat". We can use
#include <R.h>
#include <Rinternals.h>
....
SEXP ans, dim, dimnames, class;
....
class = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(class, 0, mkChar("mat"));
classgets(ans, class);
UNPROTECT(4);
return ans;
}
As the value is a character vector, we have to know how to create that from a C character array, which we do using the function mkChar.
Next: Handling character data, Previous: Classes, Up: Handling R objects in C [Contents][Index]
5.9.6 Handling lists
Some care is needed with lists, as R moved early on from using LISP-like lists (now called “pairlists”) to S-like generic vectors. As a result, the appropriate test for an object of mode list is isNewList, and we need allocVector(VECSXP, n) and not allocList(n).
List elements can be retrieved or set by direct access to the elements of the generic vector. Suppose we have a list object
a <- list(f = 1, g = 2, h = 3)
Then we can access a$g as a[[2]] by
double g;
....
g = REAL(VECTOR_ELT(a, 1))[0];
This can rapidly become tedious, and the following function (based on one in package stats) is very useful:
/* get the list element named str, or return NULL */
SEXP getListElement(SEXP list, const char *str)
{
SEXP elmt = R_NilValue, names = getAttrib(list, R_NamesSymbol);
for (int i = 0; i < length(list); i++)
if(strcmp(CHAR(STRING_ELT(names, i)), str) == 0) {
elmt = VECTOR_ELT(list, i);
break;
}
return elmt;
}
and enables us to say
double g;
g = REAL(getListElement(a, "g"))[0];
Next: Finding and setting variables, Previous: Handling lists, Up: Handling R objects in C [Contents][Index]
5.9.7 Handling character data
R character vectors are stored as STRSXPs, a vector type like VECSXP where every element is of type CHARSXP. The CHARSXP elements of STRSXPs are accessed using STRING_ELT and SET_STRING_ELT.
CHARSXPs are read-only objects and must never be modified. In particular, the C-style string contained in a CHARSXP should be treated as read-only and for this reason the CHAR function used to access the character data of a CHARSXP returns (const char *) (this also allows compilers to issue warnings about improper use). Since CHARSXPs are immutable, the same CHARSXP can be shared by any STRSXP needing an element representing the same string. R maintains a global cache of CHARSXPs so that there is only ever one CHARSXP representing a given string in memory.
You can obtain a CHARSXP by calling mkChar and providing a nul-terminated C-style string. This function will return a pre-existing CHARSXP if one with a matching string already exists, otherwise it will create a new one and add it to the cache before returning it to you. The variant mkCharLen can be used to create a CHARSXP from part of a buffer and will ensure null-termination.
Note that R character strings are restricted to 2^31 - 1 bytes, and hence so should the input to mkChar be (C allows longer strings on 64-bit platforms).
Next: Some convenience functions, Previous: Handling character data, Up: Handling R objects in C [Contents][Index]
5.9.8 Finding and setting variables
It will be usual that all the R objects needed in our C computations are passed as arguments to .Call or .External, but it is possible to find the values of R objects from within the C given their names. The following code is the equivalent of get(name, envir = rho).
SEXP getvar(SEXP name, SEXP rho)
{
SEXP ans;
if(!isString(name) || length(name) != 1)
error("name is not a single string");
if(!isEnvironment(rho))
error("rho should be an environment");
ans = findVar(installChar(STRING_ELT(name, 0)), rho);
Rprintf("first value is %f\n", REAL(ans)[0]);
return R_NilValue;
}
The main work is done by findVar, but to use it we need to install name as a name in the symbol table. As we wanted the value for internal use, we return NULL.
Similar functions with syntax
void defineVar(SEXP symbol, SEXP value, SEXP rho)
void setVar(SEXP symbol, SEXP value, SEXP rho)
can be used to assign values to R variables. defineVar creates a new binding or changes the value of an existing binding in the specified environment frame; it is the analogue of assign(symbol, value, envir = rho, inherits = FALSE), but unlike assign, defineVar does not make a copy of the object value.149 setVar searches for an existing binding for symbol in rho or its enclosing environments. If a binding is found, its value is changed to value. Otherwise, a new binding with the specified value is created in the global environment. This corresponds to assign(symbol, value, envir = rho, inherits = TRUE).
At times it may also be useful to create a new environment frame in C code. R_NewEnv is a C version of the R function new.env:
SEXP R_NewEnv(SEXP enclos, int hash, ins size)
Next: Named objects and copying, Previous: Finding and setting variables, Up: Handling R objects in C [Contents][Index]
5.9.9 Some convenience functions
Some operations are done so frequently that there are convenience functions to handle them. (All these are provided via the header file Rinternals.h.)
Suppose we wanted to pass a single logical argument ignore_quotes: we could use
int ign = asLogical(ignore_quotes);
if(ign == NA_LOGICAL) error("'ignore_quotes' must be TRUE or FALSE");
which will do any coercion needed (at least from a vector argument), and return NA_LOGICAL if the value passed was NA or coercion failed. There are also asInteger, asReal and asComplex. The function asChar returns a CHARSXP. All of these functions ignore any elements of an input vector after the first.
To return a length-one real vector we can use
double x;
...
return ScalarReal(x);
and there are versions of this for all the atomic vector types (those for a length-one character vector being ScalarString with argument a CHARSXP and mkString with argument const char *).
SEXP ScalarReal(double);
SEXP ScalarInteger(int);
SEXP ScalarLogical(int)
SEXP ScalarRaw(Rbyte);
SEXP ScalarComplex(Rcomplex);
SEXP ScalarString(SEXP);
SEXP mkString(const char *);
Some of the isXXXX functions differ from their apparent R-level counterparts: for example isVector is true for any atomic vector type (isVectorAtomic) and for lists and expressions (isVectorList) (with no check on attributes). isMatrix is a test of a length-2 "dim" attribute.
Rboolean isVector(SEXP);
Rboolean isVectorAtomic(SEXP);
Rboolean isVectorList(SEXP);
Rboolean isMatrix(SEXP);
Rboolean isPairList(SEXP);
Rboolean isPrimitive(SEXP);
Rboolean isTs(SEXP);
Rboolean isNumeric(SEXP);
Rboolean isArray(SEXP);
Rboolean isFactor(SEXP);
Rboolean isObject(SEXP);
Rboolean isFunction(SEXP);
Rboolean isLanguage(SEXP);
Rboolean isNewList(SEXP);
Rboolean isList(SEXP);
Rboolean isOrdered(SEXP);
Rboolean isUnordered(SEXP);
There are a series of small macros/functions to help construct pairlists and language objects (whose internal structures just differ by SEXPTYPE). Function CONS(u, v) is the basic building block: it constructs a pairlist from u followed by v (which is a pairlist or R_NilValue). LCONS is a variant that constructs a language object. Functions list1 to list6 construct a pairlist from one to six items, and lang1 to lang6 do the same for a language object (a function to call plus zero to five arguments). Functions elt and lastElt find the ith element and the last element of a pairlist, and nthcdr returns a pointer to the nth position in the pairlist (whose CAR is the nth item).
Functions str2type and type2str map R length-one character strings to and from SEXPTYPE numbers, and type2char maps numbers to C character strings.
Semi-internal convenience functions
Previous: Some convenience functions, Up: Some convenience functions [Contents][Index]
5.9.9.1 Semi-internal convenience functions
There is quite a collection of functions that may be used in your C code if you are willing to adapt to rare “API” changes. These typically contain “workhorses” of their R counterparts.
Functions any_duplicated and any_duplicated3 are fast versions of R’s any(duplicated(.)).
Function R_compute_identical corresponds to R’s identical function.
Previous: Some convenience functions, Up: Handling R objects in C [Contents][Index]
5.9.10 Named objects and copying
[The NAMED mechanism has been replaced by reference counting.]
When assignments are done in R such as
x <- 1:10
y <- x
the named object is not necessarily copied, so after those two assignments y and x are bound to the same SEXPREC (the structure a SEXP points to). This means that any code which alters one of them has to make a copy before modifying the copy if the usual R semantics are to apply. Note that whereas .C and .Fortran do copy their arguments (unless the dangerous dup = FALSE is used), .Call and .External do not. So duplicate is commonly called on arguments to .Call before modifying them.
However, at least some of this copying is unneeded. In the first assignment shown, x <- 1:10, R first creates an object with value 1:10 and then assigns it to x but if x is modified no copy is necessary as the temporary object with value 1:10 cannot be referred to again. R distinguishes between named and unnamed objects via a field in a SEXPREC that can be accessed via the macros NAMED and SET_NAMED. This can take values
0
The object is not bound to any symbol
1
The object has been bound to exactly one symbol
>= 2
The object has potentially been bound to two or more symbols, and one should act as if another variable is currently bound to this value. The maximal value is NAMEDMAX.
Note the past tenses: R does not do currently do full reference counting and there may currently be fewer bindings.
It is safe to modify the value of any SEXP for which NAMED(foo) is zero, and if NAMED(foo) is two or more, the value should be duplicated (via a call to duplicate) before any modification. Note that it is the responsibility of the author of the code making the modification to do the duplication, even if it is x whose value is being modified after y <- x.
The case NAMED(foo) == 1 allows some optimization, but it can be ignored (and duplication done whenever NAMED(foo) > 0). (This optimization is not currently usable in user code.) It is intended for use within replacement functions. Suppose we used
x <- 1:10
foo(x) <- 3
which is computed as
x <- 1:10
x <- "foo<-"(x, 3)
Then inside "foo<-" the object pointing to the current value of x will have NAMED(foo) as one, and it would be safe to modify it as the only symbol bound to it is x and that will be rebound immediately. (Provided the remaining code in "foo<-" make no reference to x, and no one is going to attempt a direct call such as y <- "foo<-"(x).)
This mechanism was replaced in R 4.0.0. To support future changes, package code should use the macros MAYBE_REFERENCED, MAYBE_SHARED, and MARK_NOT_MUTABLE. These currently correspond to
MAYBE_REFERENCED(x)
NAMED(x) > 0
MAYBE_SHARED(x)
NAMED(x) > 1
MARK_NOT_MUTABLE(x)
SET_NAMED(x, NAMEDMAX)
Next: Evaluating R expressions from C, Previous: Handling R objects in C, Up: System and foreign language interfaces [Contents][Index]
5.10 Interface functions .Call and .External
In this section we consider the details of the R/C interfaces.
These two interfaces have almost the same functionality. .Call is based on the interface of the same name in S version 4, and .External is based on R’s .Internal. .External is more complex but allows a variable number of arguments.
Calling .Call
Calling .External
Missing and special values
Next: Calling .External, Previous: Interface functions .Call and .External, Up: Interface functions .Call and .External [Contents][Index]
5.10.1 Calling .Call
Let us convert our finite convolution example to use .Call. The calling function in R is
conv <- function(a, b) .Call("convolve2", a, b)
which could hardly be simpler, but as we shall see all the type coercion is transferred to the C code, which is
#include <R.h>
#include <Rinternals.h>
SEXP convolve2(SEXP a, SEXP b)
{
int na, nb, nab;
double *xa, *xb, *xab;
SEXP ab;
a = PROTECT(coerceVector(a, REALSXP));
b = PROTECT(coerceVector(b, REALSXP));
na = length(a); nb = length(b); nab = na + nb - 1;
ab = PROTECT(allocVector(REALSXP, nab));
xa = REAL(a); xb = REAL(b); xab = REAL(ab);
for(int i = 0; i < nab; i++) xab[i] = 0.0;
for(int i = 0; i < na; i++)
for(int j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];
UNPROTECT(3);
return ab;
}
Next: Missing and special values, Previous: Calling .Call, Up: Interface functions .Call and .External [Contents][Index]
5.10.2 Calling .External
We can use the same example to illustrate .External. The R code changes only by replacing .Call by .External
conv <- function(a, b) .External("convolveE", a, b)
but the main change is how the arguments are passed to the C code, this time as a single SEXP. The only change to the C code is how we handle the arguments.
#include <R.h>
#include <Rinternals.h>
SEXP convolveE(SEXP args)
{
int i, j, na, nb, nab;
double *xa, *xb, *xab;
SEXP a, b, ab;
a = PROTECT(coerceVector(CADR(args), REALSXP));
b = PROTECT(coerceVector(CADDR(args), REALSXP));
...
}
Once again we do not need to protect the arguments, as in the R side of the interface they are objects that are already in use. The macros
first = CADR(args);
second = CADDR(args);
third = CADDDR(args);
fourth = CAD4R(args);
provide convenient ways to access the first four arguments. More generally we can use the CDR and CAR macros as in
args = CDR(args); a = CAR(args);
args = CDR(args); b = CAR(args);
which clearly allows us to extract an unlimited number of arguments (whereas .Call has a limit, albeit at 65 not a small one).
More usefully, the .External interface provides an easy way to handle calls with a variable number of arguments, as length(args) will give the number of arguments supplied (of which the first is ignored). We may need to know the names (‘tags’) given to the actual arguments, which we can by using the TAG macro and using something like the following example, that prints the names and the first value of its arguments if they are vector types.
SEXP showArgs(SEXP args)
{
args = CDR(args); /* skip ‘name’ */
for(int i = 0; args != R_NilValue; i++, args = CDR(args)) {
const char *name =
isNull(TAG(args)) ? "" : CHAR(PRINTNAME(TAG(args)));
SEXP el = CAR(args);
if (length(el) == 0) {
Rprintf("[%d] ‘%s’ R type, length 0\n", i+1, name);
continue;
}
switch(TYPEOF(el)) {
case REALSXP:
Rprintf("[%d] ‘%s’ %f\n", i+1, name, REAL(el)[0]);
break;
case LGLSXP:
case INTSXP:
Rprintf("[%d] ‘%s’ %d\n", i+1, name, INTEGER(el)[0]);
break;
case CPLXSXP:
{
Rcomplex cpl = COMPLEX(el)[0];
Rprintf("[%d] ‘%s’ %f + %fi\n", i+1, name, cpl.r, cpl.i);
}
break;
case STRSXP:
Rprintf("[%d] ‘%s’ %s\n", i+1, name,
CHAR(STRING_ELT(el, 0)));
break;
default:
Rprintf("[%d] ‘%s’ R type\n", i+1, name);
}
}
return R_NilValue;
}
This can be called by the wrapper function
showArgs <- function(...) invisible(.External("showArgs", ...))
Note that this style of programming is convenient but not necessary, as an alternative style is
showArgs1 <- function(...) invisible(.Call("showArgs1", list(...)))
The (very similar) C code is in the scripts.
Additional functions for accessing pairlist components are CAAR, CDAR, CDDR, and CDDDR. These components can be modified with SETCAR, SETCDR, SETCADR, SETCADDR, SETCADDDR, and SETCAD4R.
Previous: Calling .External, Up: Interface functions .Call and .External [Contents][Index]
5.10.3 Missing and special values
One piece of error-checking the .C call does (unless NAOK is true) is to check for missing (NA) and IEEE special values (Inf, -Inf and NaN) and give an error if any are found. With the .Call interface these will be passed to our code. In this example the special values are no problem, as IEC60559 arithmetic will handle them correctly. In the current implementation this is also true of NA as it is a type of NaN, but it is unwise to rely on such details. Thus we will re-write the code to handle NAs using macros defined in R_ext/Arith.h included by R.h.
The code changes are the same in any of the versions of convolve2 or convolveE:
...
for(int i = 0; i < na; i++)
for(int j = 0; j < nb; j++)
if(ISNA(xa[i]) || ISNA(xb[j]) || ISNA(xab[i + j]))
xab[i + j] = NA_REAL;
else
xab[i + j] += xa[i] * xb[j];
...
Note that the ISNA macro, and the similar macros ISNAN (which checks for NaN or NA) and R_FINITE (which is false for NA and all the special values), only apply to numeric values of type double. Missingness of integers, logicals and character strings can be tested by equality to the constants NA_INTEGER, NA_LOGICAL and NA_STRING. These and NA_REAL can be used to set elements of R vectors to NA.
The constants R_NaN, R_PosInf and R_NegInf can be used to set doubles to the special values.
Next: Parsing R code from C, Previous: Interface functions .Call and .External, Up: System and foreign language interfaces [Contents][Index]
5.11 Evaluating R expressions from C
The main function we will use is
SEXP eval(SEXP expr, SEXP rho);
the equivalent of the interpreted R code eval(expr, envir = rho) (so rho must be an environment), although we can also make use of findVar, defineVar and findFun (which restricts the search to functions).
To see how this might be applied, here is a simplified internal version of lapply for expressions, used as
a <- list(a = 1:5, b = rnorm(10), test = runif(100))
.Call("lapply", a, quote(sum(x)), new.env())
with C code
SEXP lapply(SEXP list, SEXP expr, SEXP rho)
{
int n = length(list);
SEXP ans;
if(!isNewList(list)) error("'list' must be a list");
if(!isEnvironment(rho)) error("'rho' should be an environment");
ans = PROTECT(allocVector(VECSXP, n));
for(int i = 0; i < n; i++) {
defineVar(install("x"), VECTOR_ELT(list, i), rho);
SET_VECTOR_ELT(ans, i, eval(expr, rho));
}
setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));
UNPROTECT(1);
return ans;
}
It would be closer to lapply if we could pass in a function rather than an expression. One way to do this is via interpreted R code as in the next example, but it is possible (if somewhat obscure) to do this in C code. The following is based on the code in src/main/optimize.c.
SEXP lapply2(SEXP list, SEXP fn, SEXP rho)
{
int n = length(list);
SEXP R_fcall, ans;
if(!isNewList(list)) error("'list' must be a list");
if(!isFunction(fn)) error("'fn' must be a function");
if(!isEnvironment(rho)) error("'rho' should be an environment");
R_fcall = PROTECT(lang2(fn, R_NilValue));
ans = PROTECT(allocVector(VECSXP, n));
for(int i = 0; i < n; i++) {
SETCADR(R_fcall, VECTOR_ELT(list, i));
SET_VECTOR_ELT(ans, i, eval(R_fcall, rho));
}
setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));
UNPROTECT(2);
return ans;
}
used by
.Call("lapply2", a, sum, new.env())
Function lang2 creates an executable pairlist of two elements, but this will only be clear to those with a knowledge of a LISP-like language.
As a more comprehensive example of constructing an R call in C code and evaluating, consider the following fragment of printAttributes in src/main/print.c.
/* Need to construct a call to
print(CAR(a), digits=digits)
based on the R_print structure, then eval(call, env).
See do_docall for the template for this sort of thing.
*/
SEXP s, t;
t = s = PROTECT(allocList(3));
SET_TYPEOF(s, LANGSXP);
SETCAR(t, install("print")); t = CDR(t);
SETCAR(t, CAR(a)); t = CDR(t);
SETCAR(t, ScalarInteger(digits));
SET_TAG(t, install("digits"));
eval(s, env);
UNPROTECT(1);
At this point CAR(a) is the R object to be printed, the current attribute. There are three steps: the call is constructed as a pairlist of length 3, the list is filled in, and the expression represented by the pairlist is evaluated.
A pairlist is quite distinct from a generic vector list, the only user-visible form of list in R. A pairlist is a linked list (with CDR(t) computing the next entry), with items (accessed by CAR(t)) and names or tags (set by SET_TAG). In this call there are to be three items, a symbol (pointing to the function to be called) and two argument values, the first unnamed and the second named. Setting the type to LANGSXP makes this a call which can be evaluated.
Customarily, the evaluation environment is passed from the calling R code (see rho above). In special cases it is possible that the C code may need to obtain the current evaluation environment which can be done via R_GetCurrentEnv() function.
Zero-finding
Calculating numerical derivatives
Next: Calculating numerical derivatives, Previous: Evaluating R expressions from C, Up: Evaluating R expressions from C [Contents][Index]
5.11.1 Zero-finding
In this section we re-work the example of Becker, Chambers & Wilks (1988, pp.~205–10) on finding a zero of a univariate function. The R code and an example are
zero <- function(f, guesses, tol = 1e-7) {
f.check <- function(x) {
x <- f(x)
if(!is.numeric(x)) stop("Need a numeric result")
as.double(x)
}
.Call("zero", body(f.check), as.double(guesses), as.double(tol),
new.env())
}
cube1 <- function(x) (x^2 + 1) * (x - 1.5)
zero(cube1, c(0, 5))
where this time we do the coercion and error-checking in the R code. The C code is
SEXP mkans(double x)
{
// no need for PROTECT() here, as REAL(.) does not allocate:
SEXP ans = allocVector(REALSXP, 1);
REAL(ans)[0] = x;
return ans;
}
double feval(double x, SEXP f, SEXP rho)
{
// a version with (too) much PROTECT()ion .. "better safe than sorry"
SEXP symbol, value;
PROTECT(symbol = install("x"));
PROTECT(value = mkans(x));
defineVar(symbol, value, rho);
UNPROTECT(2);
return(REAL(eval(f, rho))[0]);
}
SEXP zero(SEXP f, SEXP guesses, SEXP stol, SEXP rho)
{
double x0 = REAL(guesses)[0], x1 = REAL(guesses)[1],
tol = REAL(stol)[0];
double f0, f1, fc, xc;
if(tol <= 0.0) error("non-positive tol value");
f0 = feval(x0, f, rho); f1 = feval(x1, f, rho);
if(f0 == 0.0) return mkans(x0);
if(f1 == 0.0) return mkans(x1);
if(f0*f1 > 0.0) error("x[0] and x[1] have the same sign");
for(;;) {
xc = 0.5*(x0+x1);
if(fabs(x0-x1) < tol) return mkans(xc);
fc = feval(xc, f, rho);
if(fc == 0) return mkans(xc);
if(f0*fc > 0.0) {
x0 = xc; f0 = fc;
} else {
x1 = xc; f1 = fc;
}
}
}
Previous: Zero-finding, Up: Evaluating R expressions from C [Contents][Index]
5.11.2 Calculating numerical derivatives
We will use a longer example (by Saikat DebRoy) to illustrate the use of evaluation and .External. This calculates numerical derivatives, something that could be done as effectively in interpreted R code but may be needed as part of a larger C calculation.
An interpreted R version and an example are
numeric.deriv <- function(expr, theta, rho=sys.frame(sys.parent()))
{
eps <- sqrt(.Machine$double.eps)
ans <- eval(substitute(expr), rho)
grad <- matrix(, length(ans), length(theta),
dimnames=list(NULL, theta))
for (i in seq_along(theta)) {
old <- get(theta[i], envir=rho)
delta <- eps * max(1, abs(old))
assign(theta[i], old+delta, envir=rho)
ans1 <- eval(substitute(expr), rho)
assign(theta[i], old, envir=rho)
grad[, i] <- (ans1 - ans)/delta
}
attr(ans, "gradient") <- grad
ans
}
omega <- 1:5; x <- 1; y <- 2
numeric.deriv(sin(omega*x*y), c("x", "y"))
where expr is an expression, theta a character vector of variable names and rho the environment to be used.
For the compiled version the call from R will be
.External("numeric_deriv", expr, theta, rho)
with example usage
.External("numeric_deriv", quote(sin(omega*x*y)),
c("x", "y"), .GlobalEnv)
Note the need to quote the expression to stop it being evaluated in the caller.
Here is the complete C code which we will explain section by section.
#include <R.h> /* for DOUBLE_EPS */
#include <Rinternals.h>
SEXP numeric_deriv(SEXP args)
{
SEXP theta, expr, rho, ans, ans1, gradient, par, dimnames;
double tt, xx, delta, eps = sqrt(DOUBLE_EPS), *rgr, *rans;
int i, start;
expr = CADR(args);
if(!isString(theta = CADDR(args)))
error("theta should be of type character");
if(!isEnvironment(rho = CADDDR(args)))
error("rho should be an environment");
ans = PROTECT(coerceVector(eval(expr, rho), REALSXP));
gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));
rgr = REAL(gradient); rans = REAL(ans);
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
par = PROTECT(findVar(installChar(STRING_ELT(theta, i)), rho));
tt = REAL(par)[0];
xx = fabs(tt);
delta = (xx < 1) ? eps : xx*eps;
REAL(par)[0] += delta;
ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP));
for(int j = 0; j < LENGTH(ans); j++)
rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;
REAL(par)[0] = tt;
UNPROTECT(2); /* par, ans1 */
}
dimnames = PROTECT(allocVector(VECSXP, 2));
SET_VECTOR_ELT(dimnames, 1, theta);
dimnamesgets(gradient, dimnames);
setAttrib(ans, install("gradient"), gradient);
UNPROTECT(3); /* ans gradient dimnames */
return ans;
}
The code to handle the arguments is
expr = CADR(args);
if(!isString(theta = CADDR(args)))
error("theta should be of type character");
if(!isEnvironment(rho = CADDDR(args)))
error("rho should be an environment");
Note that we check for correct types of theta and rho but do not check the type of expr. That is because eval can handle many types of R objects other than EXPRSXP. There is no useful coercion we can do, so we stop with an error message if the arguments are not of the correct mode.
The first step in the code is to evaluate the expression in the environment rho, by
ans = PROTECT(coerceVector(eval(expr, rho), REALSXP));
We then allocate space for the calculated derivative by
gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));
The first argument to allocMatrix gives the SEXPTYPE of the matrix: here we want it to be REALSXP. The other two arguments are the numbers of rows and columns. (Note that LENGTH is intended to be used for vectors: length is more generally applicable.)
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
par = PROTECT(findVar(installChar(STRING_ELT(theta, i)), rho));
Here, we are entering a for loop. We loop through each of the variables. In the for loop, we first create a symbol corresponding to the i’th element of the STRSXP theta. Here, STRING_ELT(theta, i) accesses the i’th element of the STRSXP theta. Macro CHAR() extracts the actual character representation150 of it: it returns a pointer. We then install the name and use findVar to find its value.
tt = REAL(par)[0];
xx = fabs(tt);
delta = (xx < 1) ? eps : xx*eps;
REAL(par)[0] += delta;
ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP));
We first extract the real value of the parameter, then calculate delta, the increment to be used for approximating the numerical derivative. Then we change the value stored in par (in environment rho) by delta and evaluate expr in environment rho again. Because we are directly dealing with original R memory locations here, R does the evaluation for the changed parameter value.
for(int j = 0; j < LENGTH(ans); j++)
rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;
REAL(par)[0] = tt;
UNPROTECT(2);
}
Now, we compute the i’th column of the gradient matrix. Note how it is accessed: R stores matrices by column (like Fortran).
dimnames = PROTECT(allocVector(VECSXP, 2));
SET_VECTOR_ELT(dimnames, 1, theta);
dimnamesgets(gradient, dimnames);
setAttrib(ans, install("gradient"), gradient);
UNPROTECT(3);
return ans;
}
First we add column names to the gradient matrix. This is done by allocating a list (a VECSXP) whose first element, the row names, is NULL (the default) and the second element, the column names, is set as theta. This list is then assigned as the attribute having the symbol R_DimNamesSymbol. Finally we set the gradient matrix as the gradient attribute of ans, unprotect the remaining protected locations and return the answer ans.
Next: External pointers and weak references, Previous: Evaluating R expressions from C, Up: System and foreign language interfaces [Contents][Index]
5.12 Parsing R code from C
Suppose an R extension want to accept an R expression from the user and evaluate it. The previous section covered evaluation, but the expression will be entered as text and needs to be parsed first. A small part of R’s parse interface is declared in header file R_ext/Parse.h151.
An example of the usage can be found in the (example) Windows package windlgs included in the R source tree. The essential part is
#include <R.h>
#include <Rinternals.h>
#include <R_ext/Parse.h>
SEXP menu_ttest3()
{
char cmd[256];
SEXP cmdSexp, cmdexpr, ans = R_NilValue;
ParseStatus status;
...
if(done == 1) {
cmdSexp = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(cmdSexp, 0, mkChar(cmd));
cmdexpr = PROTECT(R_ParseVector(cmdSexp, -1, &status, R_NilValue));
if (status != PARSE_OK) {
UNPROTECT(2);
error("invalid call %s", cmd);
}
/* Loop is needed here as EXPSEXP will be of length > 1 */
for(int i = 0; i < length(cmdexpr); i++)
ans = eval(VECTOR_ELT(cmdexpr, i), R_GlobalEnv);
UNPROTECT(2);
}
return ans;
}
Note that a single line of text may give rise to more than one R expression.
R_ParseVector is essentially the code used to implement parse(text=) at R level. The first argument is a character vector (corresponding to text) and the second the maximal number of expressions to parse (corresponding to n). The third argument is a pointer to a variable of an enumeration type, and it is normal (as parse does) to regard all values other than PARSE_OK as an error. Other values which might be returned are PARSE_INCOMPLETE (an incomplete expression was found) and PARSE_ERROR (a syntax error), in both cases the value returned being R_NilValue. The fourth argument is a length one character vector to be used as a filename in error messages, a srcfile object or the R NULL object (as in the example above). If a srcfile object was used, a srcref attribute would be attached to the result, containing a list of srcref objects of the same length as the expression, to allow it to be echoed with its original formatting.
Accessing source references
Previous: Parsing R code from C, Up: Parsing R code from C [Contents][Index]
5.12.1 Accessing source references
The source references added by the parser are recorded by R’s evaluator as it evaluates code. Two functions make these available to debuggers running C code:
SEXP R_GetCurrentSrcref(int skip);
This function checks R_Srcref and the current evaluation stack for entries that contain source reference information. The skip argument tells how many source references to skip before returning the SEXP of the srcref object, counting from the top of the stack. If skip < 0, abs(skip) locations are counted up from the bottom of the stack. If too few or no source references are found, NULL is returned.
SEXP R_GetSrcFilename(SEXP srcref);
This function extracts the filename from the source reference for display, returning a length 1 character vector containing the filename. If no name is found, "" is returned.
Next: Vector accessor functions, Previous: Parsing R code from C, Up: System and foreign language interfaces [Contents][Index]
5.13 External pointers and weak references
The SEXPTYPEs EXTPTRSXP and WEAKREFSXP can be encountered at R level, but are created in C code.
External pointer SEXPs are intended to handle references to C structures such as ‘handles’, and are used for this purpose in package RODBC for example. They are unusual in their copying semantics in that when an R object is copied, the external pointer object is not duplicated. (For this reason external pointers should only be used as part of an object with normal semantics, for example an attribute or an element of a list.)
An external pointer is created by
SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot);
where p is the pointer (and hence this cannot portably be a function pointer), and tag and prot are references to ordinary R objects which will remain in existence (be protected from garbage collection) for the lifetime of the external pointer object. A useful convention is to use the tag field for some form of type identification and the prot field for protecting the memory that the external pointer represents, if that memory is allocated from the R heap. Both tag and prot can be R_NilValue, and often are.
An alternative way to create an external pointer from a function pointer is
typedef void * (*R_DL_FUNC)();
SEXP R_MakeExternalPtrFn(R_DL_FUNC p, SEXP tag, SEXP prot);
The elements of an external pointer can be accessed and set via
void *R_ExternalPtrAddr(SEXP s);
DL_FUNC R_ExternalPtrAddrFn(SEXP s);
SEXP R_ExternalPtrTag(SEXP s);
SEXP R_ExternalPtrProtected(SEXP s);
void R_ClearExternalPtr(SEXP s);
void R_SetExternalPtrAddr(SEXP s, void *p);
void R_SetExternalPtrTag(SEXP s, SEXP tag);
void R_SetExternalPtrProtected(SEXP s, SEXP p);
Clearing a pointer sets its value to the C NULL pointer.
An external pointer object can have a finalizer, a piece of code to be run when the object is garbage collected. This can be R code or C code, and the various interfaces are, respectively.
void R_RegisterFinalizer(SEXP s, SEXP fun);
void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit);
typedef void (*R_CFinalizer_t)(SEXP);
void R_RegisterCFinalizer(SEXP s, R_CFinalizer_t fun);
void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit);
The R function indicated by fun should be a function of a single argument, the object to be finalized. R does not perform a garbage collection when shutting down, and the onexit argument of the extended forms can be used to ask that the finalizer be run during a normal shutdown of the R session. It is suggested that it is good practice to clear the pointer on finalization.
The only R level function for interacting with external pointers is reg.finalizer which can be used to set a finalizer.
It is probably not a good idea to allow an external pointer to be saved and then reloaded, but if this happens the pointer will be set to the C NULL pointer.
Finalizers can be run at many places in the code base and much of it, including the R interpreter, is not re-entrant. So great care is needed in choosing the code to be run in a finalizer. Finalizers are marked to be run at garbage collection but only run at a somewhat safe point thereafter.
Weak references are used to allow the programmer to maintain information on entities without preventing the garbage collection of the entities once they become unreachable.
A weak reference contains a key and a value. The value is reachable is if it either reachable directly or via weak references with reachable keys. Once a value is determined to be unreachable during garbage collection, the key and value are set to R_NilValue and the finalizer will be run later in the garbage collection.
Weak reference objects are created by one of
SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit);
SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin,
Rboolean onexit);
where the R or C finalizer are specified in exactly the same way as for an external pointer object (whose finalization interface is implemented via weak references).
The parts can be accessed via
SEXP R_WeakRefKey(SEXP w);
SEXP R_WeakRefValue(SEXP w);
void R_RunWeakRefFinalizer(SEXP w);
A toy example of the use of weak references can be found at https://homepage.stat.uiowa.edu/~luke/R/references/weakfinex.html, but that is used to add finalizers to external pointers which can now be done more directly. At the time of writing no CRAN or Bioconductor package uses weak references.
An example
Previous: External pointers and weak references, Up: External pointers and weak references [Contents][Index]
5.13.1 An example
Package RODBC uses external pointers to maintain its channels, connections to databases. There can be several connections open at once, and the status information for each is stored in a C structure (pointed to by thisHandle in the code extract below) that is returned via an external pointer as part of the RODBC ‘channel’ (as the "handle_ptr" attribute). The external pointer is created by
SEXP ans, ptr;
ans = PROTECT(allocVector(INTSXP, 1));
ptr = R_MakeExternalPtr(thisHandle, install("RODBC_channel"), R_NilValue);
PROTECT(ptr);
R_RegisterCFinalizerEx(ptr, chanFinalizer, TRUE);
...
/* return the channel no */
INTEGER(ans)[0] = nChannels;
/* and the connection string as an attribute */
setAttrib(ans, install("connection.string"), constr);
setAttrib(ans, install("handle_ptr"), ptr);
UNPROTECT(3);
return ans;
Note the symbol given to identify the usage of the external pointer, and the use of the finalizer. Since the final argument when registering the finalizer is TRUE, the finalizer will be run at the end of the R session (unless it crashes). This is used to close and clean up the connection to the database. The finalizer code is simply
static void chanFinalizer(SEXP ptr)
{
if(!R_ExternalPtrAddr(ptr)) return;
inRODBCClose(R_ExternalPtrAddr(ptr));
R_ClearExternalPtr(ptr); /* not really needed */
}
Clearing the pointer and checking for a NULL pointer avoids any possibility of attempting to close an already-closed channel.
R’s connections provide another example of using external pointers, in that case purely to be able to use a finalizer to close and destroy the connection if it is no longer is use.
Next: Character encoding issues, Previous: External pointers and weak references, Up: System and foreign language interfaces [Contents][Index]
5.14 Vector accessor functions
The vector accessors like REAL, INTEGER, LOGICAL, RAW, COMPLEX, and VECTOR_ELT are functions when used in R extensions. (For efficiency they may be macros or inline functions when used in the R source code, apart from SET_STRING_ELT and SET_VECTOR_ELT which are always functions.)
The accessor functions check that they are being used on an appropriate type of SEXP.
Formerly it was possible for packages to obtain internal versions of some accessors by defining ‘USE_RINTERNALS’ before including Rinternals.h. This is no longer the case. Defining ‘USE_RINTERNALS’ now has no effect.
Previous: Vector accessor functions, Up: System and foreign language interfaces [Contents][Index]
5.15 Character encoding issues
CHARSXPs can be marked as coming from a known encoding (Latin-1 or UTF-8). This is mainly intended for human-readable output, and most packages can just treat such CHARSXPs as a whole. However, if they need to be interpreted as characters or output at C level then it would normally be correct to ensure that they are converted to the encoding of the current locale: this can be done by accessing the data in the CHARSXP by translateChar rather than by CHAR. If re-encoding is needed this allocates memory with R_alloc which thus persists to the end of the .Call/.External call unless vmaxset is used (see Transient storage allocation).
There is a similar function translateCharUTF8 which converts to UTF-8: this has the advantage that a faithful translation is almost always possible (whereas only a few languages can be represented in the encoding of the current locale unless that is UTF-8).
Both translateChar and translateCharUTF8 will translate any input, using escapes such as ‘<A9>’ and ‘<U+0093>’ to represent untranslatable parts of the input.
There is a public interface to the encoding marked on CHARSXPs via
typedef enum {CE_NATIVE, CE_UTF8, CE_LATIN1, CE_BYTES, CE_SYMBOL, CE_ANY} cetype_t;
cetype_t getCharCE(SEXP);
SEXP mkCharCE(const char *, cetype_t);
Only CE_UTF8 and CE_LATIN1 are marked on CHARSXPs (and so Rf_getCharCE will only return one of the first three), and these should only be used on non-ASCII strings. Value CE_BYTES is used to make CHARSXPs which should be regarded as a set of bytes and not translated. Value CE_SYMBOL is used internally to indicate Adobe Symbol encoding. Value CE_ANY is used to indicate a character string that will not need re-encoding – this is used for character strings known to be in ASCII, and can also be used as an input parameter where the intention is that the string is treated as a series of bytes. (See the comments under mkChar about the length of input allowed.)
Function
const char *reEnc(const char *x, cetype_t ce_in, cetype_t ce_out,
int subst);
can be used to re-encode character strings: like translateChar it returns a string allocated by R_alloc. This can translate from CE_SYMBOL to CE_UTF8, but not conversely. Argument subst controls what to do with untranslatable characters or invalid input: this is done byte-by-byte with 1 indicates to output hex of the form <a0>, and 2 to replace by ., with any other value causing the byte to produce no output.
There is also
SEXP mkCharLenCE(const char *, size_t, cetype_t);
to create marked character strings of a given length.
Next: Generic functions and methods, Previous: System and foreign language interfaces, Up: Writing R Extensions [Contents][Index]
6 The R API: entry points for C code
There are a large number of entry points in the R executable/DLL that can be called from C code (and some that can be called from Fortran code). Only those documented here are stable enough that they will only be changed with considerable notice.
The recommended procedure to use these is to include the header file R.h in your C code by
#include <R.h>
This will include several other header files from the directory R_INCLUDE_DIR/R_ext, and there are other header files there that can be included too, but many of the features they contain should be regarded as undocumented and unstable.
Most of these header files, including all those included by R.h, can be used from C++ code.
Note: Because R re-maps many of its external names to avoid clashes with system or user code, it is essential to include the appropriate header files when using these entry points.
This remapping can cause problems152, and can be eliminated by defining R_NO_REMAP (before including any R headers) and prepending ‘Rf_’ to all the function names used from Rinternals.h and R_ext/Error.h. These problems can usually be avoided by including other headers (such as system headers and those for external software used by the package) before any R headers. (Headers from other packages may include R headers directly or via inclusion from further packages, and may define R_NO_REMAP with or without including Rinternals.h.)
We can classify the entry points as
API
Entry points which are documented in this manual and declared in an installed header file. These can be used in distributed packages and will only be changed after deprecation.
public
Entry points declared in an installed header file that are exported on all R platforms but are not documented and subject to change without notice.
private
Entry points that are used when building R and exported on all R platforms but are not declared in the installed header files. Do not use these in distributed code.
hidden
Entry points that are where possible (Windows and some modern Unix-alike compilers/loaders when using R as a shared library) not exported.
Memory allocation
Error signaling
Random number generation
Missing and IEEE special values
Printing
Calling C from Fortran and vice versa
Numerical analysis subroutines
Optimization
Integration
Utility functions
Re-encoding
Condition handling and cleanup code
Allowing interrupts
Platform and version information
Inlining C functions
Controlling visibility
Using these functions in your own C code
Organization of header files
Next: Error signaling, Previous: The R API: entry points for C code, Up: The R API: entry points for C code [Contents][Index]
6.1 Memory allocation
There are two types of memory allocation available to the C programmer, one in which R manages the clean-up and the other in which user has full control (and responsibility).
These functions are declared in header R_exts/RS.h which is included by R.h.
Transient storage allocation
User-controlled memory
Next: User-controlled memory, Previous: Memory allocation, Up: Memory allocation [Contents][Index]
6.1.1 Transient storage allocation
Here R will reclaim the memory at the end of the call to .C, .Call or .External. Use
char *R_alloc(size_t n, int size)
which allocates n units of size bytes each. A typical usage (from package stats) is
x = (int *) R_alloc(nrows(merge)+2, sizeof(int));
(size_t is defined in stddef.h which the header defining R_alloc includes.)
There is a similar call, S_alloc (for compatibility with older versions of S) which zeroes the memory allocated,
char *S_alloc(long n, int size)
and
char *S_realloc(char *p, long new, long old, int size)
which changes the allocation size from old to new units, and zeroes the additional units.
For compatibility with current versions of S, header S.h (only) defines wrapper macros equivalent to
type* Salloc(long n, int type)
type* Srealloc(char *p, long new, long old, int type)
This memory is taken from the heap, and released at the end of the .C, .Call or .External call. Users can also manage it, by noting the current position with a call to vmaxget and subsequently clearing memory allocated by a call to vmaxset. An example might be
void *vmax = vmaxget()
// a loop involving the use of R_alloc at each iteration
vmaxset(vmax)
This is only recommended for experts.
Note that this memory will be freed on error or user interrupt (if allowed: see Allowing interrupts).
The memory returned is only guaranteed to be aligned as required for double pointers: take precautions if casting to a pointer which needs more. There is also
long double *R_allocLD(size_t n)
which is guaranteed to have the 16-byte alignment needed for long double pointers on some platforms.
These functions should only be used in code called by .C etc, never from front-ends. They are not thread-safe.
Previous: Transient storage allocation, Up: Memory allocation [Contents][Index]
6.1.2 User-controlled memory
The other form of memory allocation is an interface to malloc, the interface providing R error signaling. This memory lasts until freed by the user and is additional to the memory allocated for the R workspace.
The interface macros are
type* Calloc(size_t n, type)
type* Realloc(any *p, size_t n, type)
void Free(any *p)
providing analogues of calloc, realloc and free. If there is an error during allocation it is handled by R, so if these return the memory has been successfully allocated or freed. Free will set the pointer p to NULL. (Some but not all versions of S did so.)
Users should arrange to Free this memory when no longer needed, including on error or user interrupt. This can often be done most conveniently from an on.exit action in the calling R function – see pwilcox for an example.
Do not assume that memory allocated by Calloc/Realloc comes from the same pool as used by malloc: in particular do not use free or strdup with it.
Memory obtained by these macros should be aligned in the same way as malloc, that is ‘suitably aligned for any kind of variable’.
These macros need to be prefixed by R_ if STRICT_R_HEADERS has been defined: the prefixed forms can always be used.
char * CallocCharBuf(size_t n)
void * Memcpy(p, q, n)
void * Memzero(p, m)
CallocCharBuf is shorthand for Calloc(n+1, char) to allow for the nul terminator. Memcpy and Memzero take n items from array p and copy them to array p or zero them respectively.
Next: Random number generation, Previous: Memory allocation, Up: The R API: entry points for C code [Contents][Index]
6.2 Error signaling
The basic error signaling routines are the equivalents of stop and warning in R code, and use the same interface.
void error(const char * format, ...);
void warning(const char * format, ...);
void errorcall(SEXP call, const char * format, ...);
void warningcall(SEXP call, const char * format, ...);
void warningcall_immediate(SEXP call, const char * format, ...);
These have the same call sequences as calls to printf, but in the simplest case can be called with a single character string argument giving the error message. (Don’t do this if the string contains ‘%’ or might otherwise be interpreted as a format.)
These are defined in header R_ext/Error.h included by R.h.
If STRICT_R_HEADERS is not defined there was also an S-compatibility interface included in R_ext/RS.h. This is hidden as from R 4.2.0: define R_LEGACY_S_DEFS to 1 before including any R headers to continue to use them.
That interface used calls of the form
PROBLEM ...... ERROR
MESSAGE ...... WARN
PROBLEM ...... RECOVER(NULL_ENTRY)
MESSAGE ...... WARNING(NULL_ENTRY)
the last two being the forms available in all S versions. Here ‘......’ is a set of arguments to printf, so can be a string or a format string followed by arguments separated by commas. (There is a length limit of 4095 bytes on the whole message, with silent truncation from R 4.1.2, unchecked earlier.)
Error signaling from Fortran
Previous: Error signaling, Up: Error signaling [Contents][Index]
6.2.1 Error signaling from Fortran
There are two interface function provided to call error and warning from Fortran code, in each case with a simple character string argument. They are defined as
subroutine rexit(message)
subroutine rwarn(message)
Messages of more than 255 characters are truncated, with a warning.
Next: Missing and IEEE special values, Previous: Error signaling, Up: The R API: entry points for C code [Contents][Index]
6.3 Random number generation
The interface to R’s internal random number generation routines is
double unif_rand();
double norm_rand();
double exp_rand();
double R_unif_index(double);
giving one uniform, normal or exponential pseudo-random variate. However, before these are used, the user must call
GetRNGstate();
and after all the required variates have been generated, call
PutRNGstate();
These essentially read in (or create) .Random.seed and write it out after use.
These are defined in header R_ext/Random.h.
File S.h defines seed_in and seed_out for S-compatibility rather than GetRNGstate and PutRNGstate. These take a long * argument which is ignored.
The random number generator is private to R; there is no way to select the kind of RNG or set the seed except by evaluating calls to the R functions.
The C code behind R’s rxxx functions can be accessed by including the header file Rmath.h; See Distribution functions. Those calls generate a single variate and should also be enclosed in calls to GetRNGstate and PutRNGstate.
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6.4 Missing and IEEE special values
A set of functions is provided to test for NA, Inf, -Inf and NaN. These functions are accessed via macros:
ISNA(x) True for R’s NA only
ISNAN(x) True for R’s NA and IEEE NaN
R_FINITE(x) False for Inf, -Inf, NA, NaN
and via function R_IsNaN which is true for NaN but not NA.
Do use R_FINITE rather than isfinite or finite; the latter is often mendacious and isfinite is only available on a some platforms, on which R_FINITE is a macro expanding to isfinite.
Currently in C code ISNAN is a macro calling isnan. (Since this gives problems on some C++ systems, if the R headers is called from C++ code a function call is used.)
You can check for Inf or -Inf by testing equality to R_PosInf or R_NegInf, and set (but not test) an NA as NA_REAL.
All of the above apply to double variables only. For integer variables there is a variable accessed by the macro NA_INTEGER which can used to set or test for missingness.
These are defined in header R_ext/Arith.h included by R.h.
Next: Calling C from Fortran and vice versa, Previous: Missing and IEEE special values, Up: The R API: entry points for C code [Contents][Index]
6.5 Printing
The most useful function for printing from a C routine compiled into R is Rprintf. This is used in exactly the same way as printf, but is guaranteed to write to R’s output (which might be a GUI console rather than a file, and can be re-directed by sink). It is wise to write complete lines (including the "\n") before returning to R. It is defined in R_ext/Print.h.
The function REprintf is similar but writes on the error stream (stderr) which may or may not be different from the standard output stream.
Functions Rvprintf and REvprintf are analogues using the vprintf interface. Because that is a C99153 interface, they are only defined by R_ext/Print.h in C++ code if the macro R_USE_C99_IN_CXX is defined before it is included or (as from R 4.0.0) a C++11 compiler is used.
Another circumstance when it may be important to use these functions is when using parallel computation on a cluster of computational nodes, as their output will be re-directed/logged appropriately.
Printing from Fortran
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6.5.1 Printing from Fortran
On many systems Fortran write and print statements can be used, but the output may not interleave well with that of C, and may be invisible on GUI interfaces. They are not portable and best avoided.
Some subroutines are provided to ease the output of information from Fortran code.
subroutine dblepr(label, nchar, data, ndata)
subroutine realpr(label, nchar, data, ndata)
subroutine intpr (label, nchar, data, ndata)
and from R 4.0.0,
subroutine labelpr(label, nchar)
subroutine dblepr1(label, nchar, var)
subroutine realpr1(label, nchar, var)
subroutine intpr1 (label, nchar, var)
Here label is a character label of up to 255 characters, nchar is its length (which can be -1 if the whole label is to be used), data is an array of length at least ndata of the appropriate type (double precision, real and integer respectively) and var is a (scalar) variable. These routines print the label on one line and then print data or var as if it were an R vector on subsequent line(s). Note that some compilers will give an error or warning unless data is an array: others will accept a scalar when ndata has value one or zero. NB: There is no check on the type of data or var, so using real (including a real constant) instead of double precision will give incorrect answers.
intpr works with zero ndata so can be used to print a label in earlier versions of R.
Next: Numerical analysis subroutines, Previous: Printing, Up: The R API: entry points for C code [Contents][Index]
6.6 Calling C from Fortran and vice versa
Naming conventions for symbols generated by Fortran differ by platform: it is not safe to assume that Fortran names appear to C with a trailing underscore. To help cover up the platform-specific differences there is a set of macros154 that should be used.
F77_SUB(name)
to define a function in C to be called from Fortran
F77_NAME(name)
to declare a Fortran routine in C before use
F77_CALL(name)
to call a Fortran routine from C
F77_COMDECL(name)
to declare a Fortran common block in C
F77_COM(name)
to access a Fortran common block from C
On most current platforms these are all the same, but it is unwise to rely on this. Note that names containing underscores were not legal in Fortran 77, and are not portably handled by the above macros. (Also, all Fortran names for use by R are lower case, but this is not enforced by the macros.)
For example, suppose we want to call R’s normal random numbers from Fortran. We need a C wrapper along the lines of
#include <R.h>
void F77_SUB(rndstart)(void) { GetRNGstate(); }
void F77_SUB(rndend)(void) { PutRNGstate(); }
double F77_SUB(normrnd)(void) { return norm_rand(); }
to be called from Fortran as in
subroutine testit()
double precision normrnd, x
call rndstart()
x = normrnd()
call dblepr("X was", 5, x, 1)
call rndend()
end
Note that this is not guaranteed to be portable, for the return conventions might not be compatible between the C and Fortran compilers used. (Passing values via arguments is safer.)
The standard packages, for example stats, are a rich source of further examples.
Where supported, link time optimization provides a reliable way to check the consistency of calls to C from Fortran or vice versa. See Using Link-time Optimization. One place where this occurs is the registration of .Fortran calls in C code (see Registering native routines). For example
init.c:10:13: warning: type of ‘vsom_’ does not match original
declaration [-Wlto-type-mismatch]
extern void F77_NAME(vsom)(void *, void *, void *, void *,
void *, void *, void *, void *, void *);
vsom.f90:20:33: note: type mismatch in parameter 9
subroutine vsom(neurons,dt,dtrows,dtcols,xdim,ydim,alpha,train)
vsom.f90:20:33: note: ‘vsom’ was previously declared here
shows that a subroutine has been registered with 9 arguments (as that is what the .Fortran call used) but only has 8.
Fortran character strings
Fortran LOGICAL
Passing functions
Next: Fortran LOGICAL, Previous: Calling C from Fortran and vice versa, Up: Calling C from Fortran and vice versa [Contents][Index]
6.6.1 Fortran character strings
Passing character strings from C to Fortran or vice versa is not portable, but can be done with care. The internal representations are different: a character array in C (or C++) is nul-terminated so its length can be computed by strlen. Fortran character arrays are typically stored as an array of bytes and a length. This matters when passing strings from C to Fortran or vice versa: in many cases one has been able to get away with passing the string but not the length. However, in 2019 this changed for gfortran, starting with version 9 but backported to versions 7 and 8. Several months later, gfortran 9.2 introduced an option
-ftail-call-workaround
and made it the current default but said it might be withdrawn in future.
Suppose we want a function to report a message from Fortran to R’s console (one could use labelpr, or intpr with dummy data, but this might be the basis of a custom reporting function). Suppose the equivalent in Fortran would be
subroutine rmsg(msg)
character*(*) msg
print *.msg
end
in file rmsg.f. Using gfortran 9.2 and later we can extract the C view by
gfortran -c -fc-prototypes-external rmsg.f
which gives
void rmsg_ (char *msg, size_t msg_len);
(where size_t applies to version 8 and later). We could re-write that portably in C as
#define USE_FC_LEN_T
#include <Rconfig.h> // included by R.h, so define USE_FC_LEN_T early
void F77_NAME(rmsg)(char *msg, FC_LEN_T msg_len)
{
char cmsg[msg_len+1];
strncpy(cmsg, msg, msg_len);
cmsg[msg_len] = ‘\0’; // nul-terminate the string, to be sure
// do something with ‘cmsg’
}
in code depending on R(>= 3.6.2). For earlier versions of R we could just assume that msg is nul-terminated (not guaranteed, but people have been getting away with it for many years), so the complete C side might be
#define USE_FC_LEN_T
#include <Rconfig.h>
#ifdef FC_LEN_T
void F77_NAME(rmsg)(char *msg, FC_LEN_T msg_len)
{
char cmsg[msg_len+1];
strncpy(cmsg, msg, msg_len);
cmsg[msg_len] = ‘\0’;
// do something with ‘cmsg’
}
#else
void F77_NAME(rmsg)(char *msg)
{
// do something with ‘msg’
}
#endif
An alternative is to use Fortran 2003 features to set up the Fortran routine to pass a C-compatible character string. We could use something like
module cfuncs
use iso_c_binding, only: c_char, c_null_char
interface
subroutine cmsg(msg) bind(C, name = ‘cmsg’)
use iso_c_binding, only: c_char
character(kind = c_char):: msg(*)
end subroutine cmsg
end interface
end module
subroutine rmsg(msg)
use cfuncs
character(*) msg
call cmsg(msg//c_null_char) ! need to concatenate a nul terminator
end subroutine rmsg
where the C side is simply
void cmsg(const char *msg)
{
// do something with nul-terminated string ‘msg’
}
Passing a variable-length string from C to Fortran is trickier, but https://software.intel.com/content/www/us/en/develop/documentation/fortran-compiler-oneapi-dev-guide-and-reference/top/compiler-reference/mixed-language-programming/standard-tools-for-interoperability/bind.html provides a recipe. However, all the uses in BLAS and LAPACK are of a single character, and for these we can write a wrapper in Fortran along the lines of
subroutine c_dgemm(transa, transb, m, n, k, alpha,
+ a, lda, b, ldb, beta, c, ldc)
+ bind(C, name = ‘Cdgemm’)
use iso_c_binding, only : c_char, c_int, c_double
character(c_char):: transa, transb
integer(c_int):: m, n, k, lda, ldb, ldc
real(c_double):: alpha, beta, a(lda,*), b(ldb,*), c(ldc,*)
call dgemm(transa, transb, m, n, k, alpha,
+ a, lda, b, ldb, beta, c, ldc)
end subroutine c_dgemm
which is then called from C with declaration
void
Cdgemm(const char *transa, const char *transb, const int *m,
const int *n, const int *k, const double *alpha,
const double *a, const int *lda, const double *b, const int *ldb,
const double *beta, double *c, const int *ldc);
Alternatively, do as R does as from version 3.6.2 and pass the character length(s) from C to Fortran. A portable way to do this is
// before any R headers, or define in PKG_CPPFLAGS
#define USE_FC_LEN_T
#include <Rconfig.h>
#include <R_ext/BLAS.h>
#ifndef FCONE
# define FCONE
#endif
...
F77_CALL(dgemm)("N", "T", &nrx, &ncy, &ncx, &one, x,
&nrx, y, &nry, &zero, z, &nrx FCONE FCONE);
(Note there is no comma before or between the FCONE invocations.) It is strongly recommended that packages which call from C/C++ BLAS/LAPACK routines with character arguments adopt this approach.
Next: Passing functions, Previous: Fortran character strings, Up: Calling C from Fortran and vice versa [Contents][Index]
6.6.2 Fortran LOGICAL
Passing Fortran LOGICAL variables to/from C/C++ is potentially compiler-dependent. Fortran compilers have long used a 32-bit integer type so it is pretty portable to use int * on the C/C++ side. However, recent versions of gfortran via the option -fc-prototypes-external say the C equivalent is int_least32_t *: ‘Link-Time Optimization’ will report int * as a mismatch. It is possible to use iso_c_binding in Fortran to map LOGICAL variables to the C99 type _Bool, but it is usually simpler to pass integers to and fro.
Previous: Fortran LOGICAL, Up: Calling C from Fortran and vice versa [Contents][Index]
6.6.3 Passing functions
A number of packages call C functions passed as arguments to Fortran code along the lines of
c subroutine fcn(m,n,x,fvec,iflag)
c integer m,n,iflag
c double precision x(n),fvec(m)
...
subroutine lmdif(fcn, ...
where the C declaration and call are
void fcn_lmdif(int *m, int *n, double *par, double *fvec, int *iflag);
void F77_NAME(lmdif)(void (*fcn_lmdif)(int *m, int *n, double *par,
double *fvec, int *iflag), ...
F77_CALL(lmdif)(&fcn_lmdif, ...
This works on most platforms but depends on the C and Fortran compilers agreeing on calling conventions: this have been seen to fail. The most portable solution seems to be to convert the Fortran code to C, perhaps using f2c.
Next: Optimization, Previous: Calling C from Fortran and vice versa, Up: The R API: entry points for C code [Contents][Index]
6.7 Numerical analysis subroutines
R contains a large number of mathematical functions for its own use, for example numerical linear algebra computations and special functions.
The header files R_ext/BLAS.h, R_ext/Lapack.h and R_ext/Linpack.h contains declarations of the BLAS, LAPACK and LINPACK linear algebra functions included in R. These are expressed as calls to Fortran subroutines, and they will also be usable from users’ Fortran code. Although not part of the official API, this set of subroutines is unlikely to change (but might be supplemented).
The header file Rmath.h lists many other functions that are available and documented in the following subsections. Many of these are C interfaces to the code behind R functions, so the R function documentation may give further details.
Distribution functions
Mathematical functions
Numerical Utilities
Mathematical constants
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6.7.1 Distribution functions
The routines used to calculate densities, cumulative distribution functions and quantile functions for the standard statistical distributions are available as entry points.
The arguments for the entry points follow the pattern of those for the normal distribution:
double dnorm(double x, double mu, double sigma, int give_log);
double pnorm(double x, double mu, double sigma, int lower_tail,
int give_log);
double qnorm(double p, double mu, double sigma, int lower_tail,
int log_p);
double rnorm(double mu, double sigma);
That is, the first argument gives the position for the density and CDF and probability for the quantile function, followed by the distribution’s parameters. Argument lower_tail should be TRUE (or 1) for normal use, but can be FALSE (or 0) if the probability of the upper tail is desired or specified.
Finally, give_log should be non-zero if the result is required on log scale, and log_p should be non-zero if p has been specified on log scale.
Note that you directly get the cumulative (or “integrated”) hazard function, H(t) = - log(1 - F(t)), by using
- pdist(t, ..., /*lower_tail = */ FALSE, /* give_log = */ TRUE)
or shorter (and more cryptic) - pdist(t, ..., 0, 1).
The random-variate generation routine rnorm returns one normal variate. See Random number generation, for the protocol in using the random-variate routines.
Note that these argument sequences are (apart from the names and that rnorm has no n) mainly the same as the corresponding R functions of the same name, so the documentation of the R functions can be used. Note that the exponential and gamma distributions are parametrized by scale rather than rate.
For reference, the following table gives the basic name (to be prefixed by ‘d’, ‘p’, ‘q’ or ‘r’ apart from the exceptions noted) and distribution-specific arguments for the complete set of distributions.
beta beta a, b
non-central beta nbeta a, b, ncp
binomial binom n, p
Cauchy cauchy location, scale
chi-squared chisq df
non-central chi-squared nchisq df, ncp
exponential exp scale (and not rate)
F f n1, n2
non-central F nf n1, n2, ncp
gamma gamma shape, scale
geometric geom p
hypergeometric hyper NR, NB, n
logistic logis location, scale
lognormal lnorm logmean, logsd
negative binomial nbinom size, prob
normal norm mu, sigma
Poisson pois lambda
Student’s t t n
non-central t nt df, delta
Studentized range tukey (*) rr, cc, df
uniform unif a, b
Weibull weibull shape, scale
Wilcoxon rank sum wilcox m, n
Wilcoxon signed rank signrank n
Entries marked with an asterisk only have ‘p’ and ‘q’ functions available, and none of the non-central distributions have ‘r’ functions.
(If remapping is suppressed, the Normal distribution names are Rf_dnorm4, Rf_pnorm5 and Rf_qnorm5.)
After calls to dwilcox, pwilcox or qwilcox the function wilcox_free() should be called, and similarly signrank_free() for the signed rank functions. Since wilcox_free() and signrank_free() were only added to Rmath.h in R 4.2.0, their use requires something like
#include "Rmath.h"
#include "Rversion.h"
#if R_VERSION < R_Version(4, 2, 0)
extern void wilcox_free(void);
extern void signrank_free(void);
#endif
For the negative binomial distribution (‘nbinom’), in addition to the (size, prob) parametrization, the alternative (size, mu) parametrization is provided as well by functions ‘[dpqr]nbinom_mu()’, see ?NegBinomial in R.
Functions dpois_raw(x, *) and dbinom_raw(x, *) are versions of the Poisson and binomial probability mass functions which work continuously in x, whereas dbinom(x,*) and dpois(x,*) only return non zero values for integer x.
double dbinom_raw(double x, double n, double p, double q, int give_log)
double dpois_raw (double x, double lambda, int give_log)
Note that dbinom_raw() returns both p and q = 1-p which may be advantageous when one of them is close to 1.
Next: Numerical Utilities, Previous: Distribution functions, Up: Numerical analysis subroutines [Contents][Index]
6.7.2 Mathematical functions
Function: double gammafn (double x) ¶
Function: double lgammafn (double x) ¶
Function: double digamma (double x) ¶
Function: double trigamma (double x) ¶
Function: double tetragamma (double x) ¶
Function: double pentagamma (double x) ¶
Function: double psigamma (double x, double deriv) ¶
The Gamma function, the natural logarithm of its absolute value and first four derivatives and the n-th derivative of Psi, the digamma function, which is the derivative of lgammafn. In other words, digamma(x) is the same as psigamma(x,0), trigamma(x) == psigamma(x,1), etc.
Function: double beta (double a, double b) ¶
Function: double lbeta (double a, double b) ¶
The (complete) Beta function and its natural logarithm.
Function: double choose (double n, double k) ¶
Function: double lchoose (double n, double k) ¶
The number of combinations of k items chosen from from n and the natural logarithm of its absolute value, generalized to arbitrary real n. k is rounded to the nearest integer (with a warning if needed).
Function: double bessel_i (double x, double nu, double expo) ¶
Function: double bessel_j (double x, double nu) ¶
Function: double bessel_k (double x, double nu, double expo) ¶
Function: double bessel_y (double x, double nu) ¶
Bessel functions of types I, J, K and Y with index nu. For bessel_i and bessel_k there is the option to return exp(-x) I(x; nu) or exp(x) K(x; nu) if expo is 2. (Use expo == 1 for unscaled values.)
Next: Mathematical constants, Previous: Mathematical functions, Up: Numerical analysis subroutines [Contents][Index]
6.7.3 Numerical Utilities
There are a few other numerical utility functions available as entry points.
Function: double R_pow (double x, double y) ¶
Function: double R_pow_di (double x, int i) ¶
R_pow(x, y) and R_pow_di(x, i) compute x^y and x^i, respectively using R_FINITE checks and returning the proper result (the same as R) for the cases where x, y or i are 0 or missing or infinite or NaN.
Function: double log1p (double x) ¶
Computes log(1 + x) (log 1 plus x), accurately even for small x, i.e., |x| << 1.
This should be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes (except under C++, so it may not be declared for C++98).
Function: double log1pmx (double x) ¶
Computes log(1 + x) - x (log 1 plus x minus x), accurately even for small x, i.e., |x| << 1.
Function: double log1pexp (double x) ¶
Computes log(1 + exp(x)) (log 1 plus exp), accurately, notably for large x, e.g., x > 720.
Function: double log1mexp (double x) ¶
Computes log(1 - exp(-x)) (log 1 minus exp), accurately, carefully for two regions of x, optimally cutting off at log 2 (= 0.693147..), using ((-x) > -M_LN2 ? log(-expm1(-x)) : log1p(-exp(-x))).
Function: double expm1 (double x) ¶
Computes exp(x) - 1 (exp x minus 1), accurately even for small x, i.e., |x| << 1.
This should be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes (except under C++, so it may not be declared for C++98).
Function: double lgamma1p (double x) ¶
Computes log(gamma(x + 1)) (log(gamma(1 plus x))), accurately even for small x, i.e., 0 < x < 0.5.
Function: double cospi (double x) ¶
Computes cos(pi * x) (where pi is 3.14159...), accurately, notably for half integer x.
This might be provided by your platform155, in which case it is not included in Rmath.h, but is in math.h which Rmath.h includes. (Ensure that neither math.h nor cmath is included before Rmath.h or define
#define __STDC_WANT_IEC_60559_FUNCS_EXT__ 1
before the first inclusion.)
Function: double sinpi (double x) ¶
Computes sin(pi * x) accurately, notably for (half) integer x.
This might be provided by your platform, in which case it is not included in Rmath.h, but is in math.h which Rmath.h includes (but see the comments for cospi).
Function: double tanpi (double x) ¶
Computes tan(pi * x) accurately, notably for (half) integer x.
This might be provided by your platform, in which case it is not included in Rmath.h, but is in math.h which Rmath.h includes (but see the comments for cospi).
Function: double logspace_add (double logx, double logy) ¶
Function: double logspace_sub (double logx, double logy) ¶
Function: double logspace_sum (const double* logx, int n) ¶
Compute the log of a sum or difference from logs of terms, i.e., “x + y” as log (exp(logx) + exp(logy)) and “x - y” as log (exp(logx) - exp(logy)), and “sum_i x[i]” as log (sum[i = 1:n exp(logx[i])] ) without causing unnecessary overflows or throwing away too much accuracy.
Function: int imax2 (int x, int y) ¶
Function: int imin2 (int x, int y) ¶
Function: double fmax2 (double x, double y) ¶
Function: double fmin2 (double x, double y) ¶
Return the larger (max) or smaller (min) of two integer or double numbers, respectively. Note that fmax2 and fmin2 differ from C99/C++11’s fmax and fmin when one of the arguments is a NaN: these versions return NaN.
Function: double sign (double x) ¶
Compute the signum function, where sign(x) is 1, 0, or -1, when x is positive, 0, or negative, respectively, and NaN if x is a NaN.
Function: double fsign (double x, double y) ¶
Performs “transfer of sign” and is defined as |x| * sign(y).
Function: double fprec (double x, double digits) ¶
Returns the value of x rounded to digits decimal digits (after the decimal point).
This is the function used by R’s signif().
Function: double fround (double x, double digits) ¶
Returns the value of x rounded to digits significant decimal digits.
This is the function used by R’s round(). (Note that C99/C++11 provide a round function but C++98 need not.)
Function: double ftrunc (double x) ¶
Returns the value of x truncated (to an integer value) towards zero.
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6.7.4 Mathematical constants
R has a set of commonly used mathematical constants encompassing constants defined by POSIX and usually156 found in math.h (but maybe not in the C++ header cmath) and contains further ones that are used in statistical computations. These are defined to (at least) 30 digits accuracy in Rmath.h. The following definitions use ln(x) for the natural logarithm (log(x) in R).
Name
Definition (ln = log)
round(value, 7)
M_E e 2.7182818
M_LOG2E log2(e) 1.4426950
M_LOG10E log10(e) 0.4342945
M_LN2 ln(2) 0.6931472
M_LN10 ln(10) 2.3025851
M_PI pi 3.1415927
M_PI_2 pi/2 1.5707963
M_PI_4 pi/4 0.7853982
M_1_PI 1/pi 0.3183099
M_2_PI 2/pi 0.6366198
M_2_SQRTPI 2/sqrt(pi) 1.1283792
M_SQRT2 sqrt(2) 1.4142136
M_SQRT1_2 1/sqrt(2) 0.7071068
M_SQRT_3 sqrt(3) 1.7320508
M_SQRT_32 sqrt(32) 5.6568542
M_LOG10_2 log10(2) 0.3010300
M_2PI 2*pi 6.2831853
M_SQRT_PI sqrt(pi) 1.7724539
M_1_SQRT_2PI 1/sqrt(2*pi) 0.3989423
M_SQRT_2dPI sqrt(2/pi) 0.7978846
M_LN_SQRT_PI ln(sqrt(pi)) 0.5723649
M_LN_SQRT_2PI ln(sqrt(2*pi)) 0.9189385
M_LN_SQRT_PId2 ln(sqrt(pi/2)) 0.2257914
There are a set of constants (PI, DOUBLE_EPS) (and so on) defined (unless STRICT_R_HEADERS is defined) in the included header R_ext/Constants.h, mainly for compatibility with S.
Further, the included header R_ext/Boolean.h has enumeration constants TRUE and FALSE of type Rboolean in order to provide a way of using “logical” variables in C consistently. This can conflict with other software: for example it conflicts with the headers in IJG’s jpeg-9 (but not earlier versions).
Next: Integration, Previous: Numerical analysis subroutines, Up: The R API: entry points for C code [Contents][Index]
6.8 Optimization
The C code underlying optim can be accessed directly. The user needs to supply a function to compute the function to be minimized, of the type
typedef double optimfn(int n, double *par, void *ex);
where the first argument is the number of parameters in the second argument. The third argument is a pointer passed down from the calling routine, normally used to carry auxiliary information.
Some of the methods also require a gradient function
typedef void optimgr(int n, double *par, double *gr, void *ex);
which passes back the gradient in the gr argument. No function is provided for finite-differencing, nor for approximating the Hessian at the result.
The interfaces (defined in header R_ext/Applic.h) are
Nelder Mead:
void nmmin(int n, double *xin, double *x, double *Fmin, optimfn fn,
int *fail, double abstol, double intol, void *ex,
double alpha, double beta, double gamma, int trace,
int *fncount, int maxit);
BFGS:
void vmmin(int n, double *x, double *Fmin,
optimfn fn, optimgr gr, int maxit, int trace,
int *mask, double abstol, double reltol, int nREPORT,
void *ex, int *fncount, int *grcount, int *fail);
Conjugate gradients:
void cgmin(int n, double *xin, double *x, double *Fmin,
optimfn fn, optimgr gr, int *fail, double abstol,
double intol, void *ex, int type, int trace,
int *fncount, int *grcount, int maxit);
Limited-memory BFGS with bounds:
void lbfgsb(int n, int lmm, double *x, double *lower,
double *upper, int *nbd, double *Fmin, optimfn fn,
optimgr gr, int *fail, void *ex, double factr,
double pgtol, int *fncount, int *grcount,
int maxit, char *msg, int trace, int nREPORT);
Simulated annealing:
void samin(int n, double *x, double *Fmin, optimfn fn, int maxit,
int tmax, double temp, int trace, void *ex);
Many of the arguments are common to the various methods. n is the number of parameters, x or xin is the starting parameters on entry and x the final parameters on exit, with final value returned in Fmin. Most of the other parameters can be found from the help page for optim: see the source code src/appl/lbfgsb.c for the values of nbd, which specifies which bounds are to be used.
Next: Utility functions, Previous: Optimization, Up: The R API: entry points for C code [Contents][Index]
6.9 Integration
The C code underlying integrate can be accessed directly. The user needs to supply a vectorizing C function to compute the function to be integrated, of the type
typedef void integr_fn(double *x, int n, void *ex);
where x[] is both input and output and has length n, i.e., a C function, say fn, of type integr_fn must basically do for(i in 1:n) x[i] := f(x[i], ex). The vectorization requirement can be used to speed up the integrand instead of calling it n times. Note that in the current implementation built on QUADPACK, n will be either 15 or 21. The ex argument is a pointer passed down from the calling routine, normally used to carry auxiliary information.
There are interfaces (defined in header R_ext/Applic.h) for integrals over finite and infinite intervals (or “ranges” or “integration boundaries”).
Finite:
void Rdqags(integr_fn f, void *ex, double *a, double *b,
double *epsabs, double *epsrel,
double *result, double *abserr, int *neval, int *ier,
int *limit, int *lenw, int *last,
int *iwork, double *work);
Infinite:
void Rdqagi(integr_fn f, void *ex, double *bound, int *inf,
double *epsabs, double *epsrel,
double *result, double *abserr, int *neval, int *ier,
int *limit, int *lenw, int *last,
int *iwork, double *work);
Only the 3rd and 4th argument differ for the two integrators; for the finite range integral using Rdqags, a and b are the integration interval bounds, whereas for an infinite range integral using Rdqagi, bound is the finite bound of the integration (if the integral is not doubly-infinite) and inf is a code indicating the kind of integration range,
inf = 1
corresponds to (bound, +Inf),
inf = -1
corresponds to (-Inf, bound),
inf = 2
corresponds to (-Inf, +Inf),
f and ex define the integrand function, see above; epsabs and epsrel specify the absolute and relative accuracy requested, result, abserr and last are the output components value, abs.err and subdivisions of the R function integrate, where neval gives the number of integrand function evaluations, and the error code ier is translated to R’s integrate() $ message, look at that function definition. limit corresponds to integrate(..., subdivisions = *). It seems you should always define the two work arrays and the length of the second one as
lenw = 4 * limit;
iwork = (int *) R_alloc(limit, sizeof(int));
work = (double *) R_alloc(lenw, sizeof(double));
The comments in the source code in src/appl/integrate.c give more details, particularly about reasons for failure (ier >= 1).
Next: Re-encoding, Previous: Integration, Up: The R API: entry points for C code [Contents][Index]
6.10 Utility functions
R has a fairly comprehensive set of sort routines which are made available to users’ C code. The following is declared in header file Rinternals.h.
Function: void R_orderVector (int* indx, int n, SEXP arglist, Rboolean nalast, Rboolean decreasing) ¶
Function: void R_orderVector1 (int* indx, int n, SEXP x, Rboolean nalast, Rboolean decreasing) ¶
R_orderVector() corresponds to R’s order(..., na.last, decreasing). More specifically, indx <- order(x, y, na.last, decreasing) corresponds to R_orderVector(indx, n, Rf_lang2(x, y), nalast, decreasing) and for three vectors, Rf_lang3(x,y,z) is used as arglist.
Both R_orderVector and R_orderVector1 assume the vector indx to be allocated to length >= n. On return, indx[] contains a permutation of 0:(n-1), i.e., 0-based C indices (and not 1-based R indices, as R’s order()).
When ordering only one vector, R_orderVector1 is faster and corresponds (but is 0-based) to R’s indx <- order(x, na.last, decreasing). It was added in R 3.3.0.
All other sort routines are declared in header file R_ext/Utils.h (included by R.h) and include the following.
Function: void R_isort (int* x, int n) ¶
Function: void R_rsort (double* x, int n) ¶
Function: void R_csort (Rcomplex* x, int n) ¶
Function: void rsort_with_index (double* x, int* index, int n) ¶
The first three sort integer, real (double) and complex data respectively. (Complex numbers are sorted by the real part first then the imaginary part.) NAs are sorted last.
rsort_with_index sorts on x, and applies the same permutation to index. NAs are sorted last.
Function: void revsort (double* x, int* index, int n) ¶
Is similar to rsort_with_index but sorts into decreasing order, and NAs are not handled.
Function: void iPsort (int* x, int n, int k) ¶
Function: void rPsort (double* x, int n, int k) ¶
Function: void cPsort (Rcomplex* x, int n, int k) ¶
These all provide (very) partial sorting: they permute x so that x[k] is in the correct place with smaller values to the left, larger ones to the right.
Function: void R_qsort (double *v, size_t i, size_t j) ¶
Function: void R_qsort_I (double *v, int *I, int i, int j) ¶
Function: void R_qsort_int (int *iv, size_t i, size_t j) ¶
Function: void R_qsort_int_I (int *iv, int *I, int i, int j) ¶
These routines sort v[i:j] or iv[i:j] (using 1-indexing, i.e., v[1] is the first element) calling the quicksort algorithm as used by R’s sort(v, method = "quick") and documented on the help page for the R function sort. The ..._I() versions also return the sort.index() vector in I. Note that the ordering is not stable, so tied values may be permuted.
Note that NAs are not handled (explicitly) and you should use different sorting functions if NAs can be present.
Function: subroutine qsort4 (double precision v, integer indx, integer ii, integer jj) ¶
Function: subroutine qsort3 (double precision v, integer ii, integer jj) ¶
The Fortran interface routines for sorting double precision vectors are qsort3 and qsort4, equivalent to R_qsort and R_qsort_I, respectively.
Function: void R_max_col (double* matrix, int* nr, int* nc, int* maxes, int* ties_meth) ¶
Given the nr by nc matrix matrix in column-major (“Fortran”) order, R_max_col() returns in maxes[i-1] the column number of the maximal element in the i-th row (the same as R’s max.col() function). In the case of ties (multiple maxima), *ties_meth is an integer code in 1:3 determining the method: 1 = “random”, 2 = “first” and 3 = “last”. See R’s help page ?max.col.
Function: int findInterval (double* xt, int n, double x, Rboolean rightmost_closed, Rboolean all_inside, int ilo, int* mflag) ¶
Function: int findInterval2(double* xt, int n, double x, Rboolean rightmost_closed, Rboolean all_inside, Rboolean left_open, int ilo, int* mflag) ¶
Given the ordered vector xt of length n, return the interval or index of x in xt[], typically max(i; 1 <= i <= n & xt[i] <= x) where we use 1-indexing as in R and Fortran (but not C). If rightmost_closed is true, also returns n-1 if x equals xt[n]. If all_inside is not 0, the result is coerced to lie in 1:(n-1) even when x is outside the xt[] range. On return, *mflag equals -1 if x < xt[1], +1 if x >= xt[n], and 0 otherwise.
The algorithm is particularly fast when ilo is set to the last result of findInterval() and x is a value of a sequence which is increasing or decreasing for subsequent calls.
findInterval2() is a generalization of findInterval(), with an extra Rboolean argument left_open. Setting left_open = TRUE basically replaces all left-closed right-open intervals t) by left-open ones t], see the help page of R function findInterval for details.
There is also an F77_CALL(interv)() version of findInterval() with the same arguments, but all pointers.
A system-independent interface to produce the name of a temporary file is provided as
Function: char * R_tmpnam (const char *prefix, const char *tmpdir) ¶
Function: char * R_tmpnam2 (const char *prefix, const char *tmpdir, const char *fileext) ¶
Function: void R_free_tmpnam (char *name) ¶
Return a pathname for a temporary file with name beginning with prefix and ending with fileext in directory tmpdir. A NULL prefix or extension is replaced by "". Note that the return value is dynamically allocated and should be freed using R_free_tmpnam when no longer needed (unlike the system call tmpnam). Freeing the result using free is no longer recommended.
There is also the internal function used to expand file names in several R functions, and called directly by path.expand.
Function: const char * R_ExpandFileName (const char *fn) ¶
Expand a path name fn by replacing a leading tilde by the user’s home directory (if defined). The precise meaning is platform-specific; it will usually be taken from the environment variable HOME if this is defined.
For historical reasons there are Fortran interfaces to functions D1MACH and I1MACH. These can be called from C code as e.g. F77_CALL(d1mach)(4). Note that these are emulations of the original functions by Fox, Hall and Schryer on NetLib at https://www.netlib.org/slatec/src/ for IEC 60559 arithmetic (required by R).
Next: Condition handling and cleanup code, Previous: Utility functions, Up: The R API: entry points for C code [Contents][Index]
6.11 Re-encoding
R has its own C-level interface to the encoding conversion capabilities provided by iconv because there are incompatibilities between the declarations in different implementations of iconv.
These are declared in header file R_ext/Riconv.h.
Function: void * Riconv_open (const char *to, const char *from) ¶
Set up a pointer to an encoding object to be used to convert between two encodings: "" indicates the current locale.
Function: size_t Riconv (void *cd, const char **inbuf, size_t *inbytesleft, char **outbuf, size_t *outbytesleft) ¶
Convert as much as possible of inbuf to outbuf. Initially the size_t variables indicate the number of bytes available in the buffers, and they are updated (and the char pointers are updated to point to the next free byte in the buffer). The return value is the number of characters converted, or (size_t)-1 (beware: size_t is usually an unsigned type). It should be safe to assume that an error condition sets errno to one of E2BIG (the output buffer is full), EILSEQ (the input cannot be converted, and might be invalid in the encoding specified) or EINVAL (the input does not end with a complete multi-byte character).
Function: int Riconv_close (void * cd) ¶
Free the resources of an encoding object.
Next: Allowing interrupts, Previous: Re-encoding, Up: The R API: entry points for C code [Contents][Index]
6.12 Condition handling and cleanup code
Three functions are available for establishing condition handlers from within C code:
#include <Rinternals.h>
SEXP R_tryCatchError(SEXP (*fun)(void *data), void *data,
SEXP (*hndlr)(SEXP cond, void *hdata), void *hdata);
SEXP R_tryCatch(SEXP (*fun)(void *data), void *data,
SEXP,
SEXP (*hndlr)(SEXP cond, void *hdata), void *hdata,
void (*clean)(void *cdata), void *cdata);
SEXP R_withCallingErrorHandler(SEXP (*fun)(void *data), void *data,
SEXP (*hndlr)(SEXP cond, void *hdata), void *hdata)
R_tryCatchError establishes an exiting handler for conditions inheriting form class error.
R_tryCatch can be used to establish a handler for other conditions and to register a cleanup action. The conditions to be handled are specified as a character vector (STRSXP). A NULL pointer can be passed as fun or clean if condition handling or cleanup are not needed.
These are currently implemented using the R-level tryCatch mechanism so are subject to some overhead.
R_withCallingErrorHandler establishes a calling handler for conditions inheriting form class error. It establishes the handler without calling back into R and will therefore be more efficient.
The function R_UnwindProtect can be used to ensure that a cleanup action takes place on ordinary return as well as on a non-local transfer of control, which R implements as a longjmp.
SEXP R_UnwindProtect(SEXP (*fun)(void *data), void *data,
void (*clean)(void *data, Rboolean jump), void *cdata,
SEXP cont);
R_UnwindProtect can be used in two ways. The simper usage, suitable for use in C code, passes NULL for the cont argument. R_UnwindProtect will call fun(data). If fun returns a value, then R_UnwindProtect calls clean(cleandata, FALSE) before returning the value returned by fun. If fun executes a non-local transfer of control, then clean(cleandata, TRUE) is called, and the non-local transfer of control is resumed.
The second use pattern, suitable to support C++ stack unwinding, uses two additional functions:
SEXP R_MakeUnwindCont();
void NORET R_ContinueUnwind(SEXP cont);
R_MakeUnwindCont allocates a continuation token cont to pass to R_UnwindProtect. This token should be protected with PROTECT before calling R_UnwindProtect. When the clean function is called with jump == TRUE, indicating that R is executing a non-local transfer of control, it can throw a C++ exception to a C++ catch outside the C++ code to be unwound, and then use the continuation token in the a call R_ContinueUnwind(cont) to resume the non-local transfer of control within R.
Next: Platform and version information, Previous: Condition handling and cleanup code, Up: The R API: entry points for C code [Contents][Index]
6.13 Allowing interrupts
No part of R can be interrupted whilst running long computations in compiled code, so programmers should make provision for the code to be interrupted at suitable points by calling from C
#include <R_ext/Utils.h>
void R_CheckUserInterrupt(void);
and from Fortran
subroutine rchkusr()
These check if the user has requested an interrupt, and if so branch to R’s error signaling functions.
Note that it is possible that the code behind one of the entry points defined here if called from your C or Fortran code could be interruptible or generate an error and so not return to your code.
Next: Inlining C functions, Previous: Allowing interrupts, Up: The R API: entry points for C code [Contents][Index]
6.14 Platform and version information
The header files define USING_R, which can be used to test if the code is indeed being used with R.
Header file Rconfig.h (included by R.h) is used to define platform-specific macros that are mainly for use in other header files. The macro WORDS_BIGENDIAN is defined on big-endian157 systems (e.g. most OSes on Sparc and PowerPC hardware) and not on little-endian systems (nowadays all the commoner R platforms). It can be useful when manipulating binary files. NB: these macros apply only to the C compiler used to build R, not necessarily to another C or C++ compiler.
Header file Rversion.h (not included by R.h) defines a macro R_VERSION giving the version number encoded as an integer, plus a macro R_Version to do the encoding. This can be used to test if the version of R is late enough, or to include back-compatibility features. For protection against very old versions of R which did not have this macro, use a construction such as
#if defined(R_VERSION) && R_VERSION >= R_Version(3, 1, 0)
...
#endif
More detailed information is available in the macros R_MAJOR, R_MINOR, R_YEAR, R_MONTH and R_DAY: see the header file Rversion.h for their format. Note that the minor version includes the patchlevel (as in ‘2.2’).
Packages which use alloca need to ensure it is defined: as it is part of neither C nor POSIX there is no standard way to do so. One can use
#include <Rconfig.h> // for HAVE_ALLOCA_H
#ifdef __GNUC__
// this covers gcc, clang, icc
# undef alloca
# define alloca(x) __builtin_alloca((x))
#elif defined(HAVE_ALLOCA_H)
// needed for native compilers on Solaris and AIX
# include <alloca.h>
#endif
(and this should be included before standard C headers such as stdlib.h, since on some platforms these include malloc.h which may have a conflicting definition), which suffices for known R platforms.
Next: Controlling visibility, Previous: Platform and version information, Up: The R API: entry points for C code [Contents][Index]
6.15 Inlining C functions
The C99 keyword inline should be recognized by all compilers nowadays used to build R. Portable code which might be used with earlier versions of R can be written using the macro R_INLINE (defined in file Rconfig.h included by R.h), as for example from package cluster
#include <R.h>
static R_INLINE int ind_2(int l, int j)
{
...
}
Be aware that using inlining with functions in more than one compilation unit is almost impossible to do portably, see https://www.greenend.org.uk/rjk/tech/inline.html, so this usage is for static functions as in the example. All the R configure code has checked is that R_INLINE can be used in a single C file with the compiler used to build R. We recommend that packages making extensive use of inlining include their own configure code.
Next: Using these functions in your own C code, Previous: Inlining C functions, Up: The R API: entry points for C code [Contents][Index]
6.16 Controlling visibility
Header R_ext/Visibility.h has some definitions for controlling the visibility of entry points. These are only effective when ‘HAVE_VISIBILITY_ATTRIBUTE’ is defined – this is checked when R is configured and recorded in header Rconfig.h (included by R_ext/Visibility.h). It is often defined on modern Unix-alikes with a recent compiler158, but not supported on macOS nor Windows. Minimizing the visibility of symbols in a shared library will both speed up its loading (unlikely to be significant) and reduce the possibility of linking to other entry points of the same name.
C/C++ entry points prefixed by attribute_hidden will not be visible in the shared object. There is no comparable mechanism for Fortran entry points, but there is a more comprehensive scheme used by, for example package stats. Most compilers which allow control of visibility will allow control of visibility for all symbols via a flag, and where known the flag is encapsulated in the macros ‘C_VISIBILITY’, ‘CXX_VISIBILITY’159 and ‘F_VISIBILITY’ for C, C++ and Fortran compilers.160 These are defined in etc/Makeconf and so available for normal compilation of package code. For example, src/Makevars could include some of
PKG_CFLAGS=$(C_VISIBILITY)
PKG_CXXFLAGS=$(CXX_VISIBILITY)
PKG_FFLAGS=$(F_VISIBILITY)
This would end up with no visible entry points, which would be pointless. However, the effect of the flags can be overridden by using the attribute_visible prefix. A shared object which registers its entry points needs only for have one visible entry point, its initializer, so for example package stats has
void attribute_visible R_init_stats(DllInfo *dll)
{
R_registerRoutines(dll, CEntries, CallEntries, FortEntries, NULL);
R_useDynamicSymbols(dll, FALSE);
...
}
Because the ‘C_VISIBILITY’ mechanism is only useful in conjunction with attribute_visible, it is not enabled unless ‘HAVE_VISIBILITY_ATTRIBUTE’ is defined. The usual visibility flag is -fvisibility=hidden: some compilers also support -fvisibility-inlines-hidden which can be used by overriding ‘C_VISIBILITY’ and ‘CXX_VISIBILITY’ in config.site when building R, or editing etc/Makeconf in the R installation.
Note that configure only checks that visibility attributes and flags are accepted, not that they actually hide symbols.
The visibility mechanism is not available on Windows, but there is an equally effective way to control which entry points are visible, by supplying a definitions file pkgnme/src/pkgname-win.def: only entry points listed in that file will be visible. Again using stats as an example, it has
LIBRARY stats.dll
EXPORTS
R_init_stats
Next: Organization of header files, Previous: Controlling visibility, Up: The R API: entry points for C code [Contents][Index]
6.17 Using these functions in your own C code
It is possible to build Mathlib, the R set of mathematical functions documented in Rmath.h, as a standalone library libRmath under both Unix-alikes and Windows. (This includes the functions documented in Numerical analysis subroutines as from that header file.)
The library is not built automatically when R is installed, but can be built in the directory src/nmath/standalone in the R sources: see the file README there. To use the code in your own C program include
#define MATHLIB_STANDALONE
#include <Rmath.h>
and link against ‘-lRmath’ (and perhaps ‘-lm’). There is an example file test.c.
A little care is needed to use the random-number routines. You will need to supply the uniform random number generator
double unif_rand(void)
or use the one supplied (and with a dynamic library or DLL you will have to use the one supplied, which is the Marsaglia-multicarry with an entry points
set_seed(unsigned int, unsigned int)
to set its seeds and
get_seed(unsigned int *, unsigned int *)
to read the seeds).
Previous: Using these functions in your own C code, Up: The R API: entry points for C code [Contents][Index]
6.18 Organization of header files
The header files which R installs are in directory R_INCLUDE_DIR (default R_HOME/include). This currently includes
R.h includes many other files
S.h different version for code ported from S
Rinternals.h definitions for using R’s internal structures
Rdefines.h macros for an S-like interface to the above (no longer maintained)
Rmath.h standalone math library
Rversion.h R version information
Rinterface.h for add-on front-ends (Unix-alikes only)
Rembedded.h for add-on front-ends
R_ext/Applic.h optimization and integration
R_ext/BLAS.h C definitions for BLAS routines
R_ext/Callbacks.h C (and R function) top-level task handlers
R_ext/GetX11Image.h X11Image interface used by package trkplot
R_ext/Lapack.h C definitions for some LAPACK routines
R_ext/Linpack.h C definitions for some LINPACK routines, not all of which are included in R
R_ext/Parse.h a small part of R’s parse interface: not part of the stable API.
R_ext/RStartup.h for add-on front-ends
R_ext/Rdynload.h needed to register compiled code in packages
R_ext/R-ftp-http.h interface to internal method of download.file
R_ext/Riconv.h interface to iconv
R_ext/Visibility.h definitions controlling visibility
R_ext/eventloop.h for add-on front-ends and for packages that need to share in the R event loops (not Windows)
The following headers are included by R.h:
Rconfig.h configuration info that is made available
R_ext/Arith.h handling for NAs, NaNs, Inf/-Inf
R_ext/Boolean.h TRUE/FALSE type
R_ext/Complex.h C typedefs for R’s complex
R_ext/Constants.h constants
R_ext/Error.h error signaling
R_ext/Memory.h memory allocation
R_ext/Print.h Rprintf and variations.
R_ext/RS.h definitions common to R.h and S.h, including F77_CALL etc.
R_ext/Random.h random number generation
R_ext/Utils.h sorting and other utilities
R_ext/libextern.h definitions for exports from R.dll on Windows.
The graphics systems are exposed in headers R_ext/GraphicsEngine.h, R_ext/GraphicsDevice.h (which it includes) and R_ext/QuartzDevice.h. Facilities for defining custom connection implementations are provided in R_ext/Connections.h, but make sure you consult the file before use.
Let us re-iterate the advice to include system headers before the R header files, especially Rinternals.h (included by Rdefines.h) and Rmath.h, which redefine names which may be used in system headers (fewer if ‘R_NO_REMAP’ is defined before inclusion, or ‘R_NO_REMAP_RMATH’ for Rmath.h).
Next: Linking GUIs and other front-ends to R, Previous: The R API: entry points for C code, Up: Writing R Extensions [Contents][Index]
7 Generic functions and methods
R programmers will often want to add methods for existing generic functions, and may want to add new generic functions or make existing functions generic. In this chapter we give guidelines for doing so, with examples of the problems caused by not adhering to them.
This chapter only covers the ‘informal’ class system copied from S3, and not with the S4 (formal) methods of package methods.
First, a caveat: a function named gen.cl will be invoked by the generic gen for class cl, so do not name functions in this style unless they are intended to be methods.
The key function for methods is NextMethod, which dispatches the next method. It is quite typical for a method function to make a few changes to its arguments, dispatch to the next method, receive the results and modify them a little. An example is
t.data.frame <- function(x)
{
x <- as.matrix(x)
NextMethod("t")
}
Note that the example above works because there is a next method, the default method, not that a new method is selected when the class is changed.
Any method a programmer writes may be invoked from another method by NextMethod, with the arguments appropriate to the previous method. Further, the programmer cannot predict which method NextMethod will pick (it might be one not yet dreamt of), and the end user calling the generic needs to be able to pass arguments to the next method. For this to work
A method must have all the arguments of the generic, including … if the generic does.
It is a grave misunderstanding to think that a method needs only to accept the arguments it needs. The original S version of predict.lm did not have a … argument, although predict did. It soon became clear that predict.glm needed an argument dispersion to handle over-dispersion. As predict.lm had neither a dispersion nor a … argument, NextMethod could no longer be used. (The legacy, two direct calls to predict.lm, lives on in predict.glm in R, which is based on the workaround for S3 written by Venables & Ripley.)
Further, the user is entitled to use positional matching when calling the generic, and the arguments to a method called by UseMethod are those of the call to the generic. Thus
A method must have arguments in exactly the same order as the generic.
To see the scale of this problem, consider the generic function scale, defined as
scale <- function (x, center = TRUE, scale = TRUE)
UseMethod("scale")
Suppose an unthinking package writer created methods such as
scale.foo <- function(x, scale = FALSE, ...) { }
Then for x of class "foo" the calls
scale(x, , TRUE)
scale(x, scale = TRUE)
would most likely do different things, to the justifiable consternation of the end user.
To add a further twist, which default is used when a user calls scale(x) in our example? What if
scale.bar <- function(x, center, scale = TRUE) NextMethod("scale")
and x has class c("bar", "foo")? It is the default specified in the method that is used, but the default specified in the generic may be the one the user sees. This leads to the recommendation:
If the generic specifies defaults, all methods should use the same defaults.
An easy way to follow these recommendations is to always keep generics simple, e.g.
scale <- function(x, ...) UseMethod("scale")
Only add parameters and defaults to the generic if they make sense in all possible methods implementing it.
Adding new generics
Previous: Generic functions and methods, Up: Generic functions and methods [Contents][Index]
7.1 Adding new generics
When creating a new generic function, bear in mind that its argument list will be the maximal set of arguments for methods, including those written elsewhere years later. So choosing a good set of arguments may well be an important design issue, and there need to be good arguments not to include a … argument.
If a … argument is supplied, some thought should be given to its position in the argument sequence. Arguments which follow … must be named in calls to the function, and they must be named in full (partial matching is suppressed after …). Formal arguments before … can be partially matched, and so may ‘swallow’ actual arguments intended for …. Although it is commonplace to make the … argument the last one, that is not always the right choice.
Sometimes package writers want to make generic a function in the base package, and request a change in R. This may be justifiable, but making a function generic with the old definition as the default method does have a small performance cost. It is never necessary, as a package can take over a function in the base package and make it generic by something like
foo <- function(object, ...) UseMethod("foo")
foo.default <- function(object, ...) base::foo(object)
Earlier versions of this manual suggested assigning foo.default <- base::foo. This is not a good idea, as it captures the base function at the time of installation and it might be changed as R is patched or updated.
The same idea can be applied for functions in other packages.
Next: Function and variable index, Previous: Generic functions and methods, Up: Writing R Extensions [Contents][Index]
8 Linking GUIs and other front-ends to R
There are a number of ways to build front-ends to R: we take this to mean a GUI or other application that has the ability to submit commands to R and perhaps to receive results back (not necessarily in a text format). There are other routes besides those described here, for example the package Rserve (from CRAN, see also https://www.rforge.net/Rserve/) and connections to Java in ‘JRI’ (part of the rJava package on CRAN).
Note that the APIs described in this chapter are only intended to be used in an alternative front-end: they are not part of the API made available for R packages and can be dangerous to use in a conventional package (although packages may contain alternative front-ends). Conversely some of the functions from the API (such as R_alloc) should not be used in front-ends.
Embedding R under Unix-alikes
Embedding R under Windows
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8.1 Embedding R under Unix-alikes
R can be built as a shared library161 if configured with --enable-R-shlib. This shared library can be used to run R from alternative front-end programs. We will assume this has been done for the rest of this section. Also, it can be built as a static library if configured with --enable-R-static-lib, and that can be used in a very similar way (at least on Linux: on other platforms one needs to ensure that all the symbols exported by libR.a are linked into the front-end).
The command-line R front-end, R_HOME/bin/exec/R, is one such example, and the former GNOME (see package gnomeGUI on CRAN’s ‘Archive’ area) and macOS consoles are others. The source for R_HOME/bin/exec/R is in file src/main/Rmain.c and is very simple
int Rf_initialize_R(int ac, char **av); /* in ../unix/system.c */
void Rf_mainloop(); /* in main.c */
extern int R_running_as_main_program; /* in ../unix/system.c */
int main(int ac, char **av)
{
R_running_as_main_program = 1;
Rf_initialize_R(ac, av);
Rf_mainloop(); /* does not return */
return 0;
}
indeed, misleadingly simple. Remember that R_HOME/bin/exec/R is run from a shell script R_HOME/bin/R which sets up the environment for the executable, and this is used for
Setting R_HOME and checking it is valid, as well as the path R_SHARE_DIR and R_DOC_DIR to the installed share and doc directory trees. Also setting R_ARCH if needed.
Setting LD_LIBRARY_PATH to include the directories used in linking R. This is recorded as the default setting of R_LD_LIBRARY_PATH in the shell script R_HOME/etcR_ARCH/ldpaths.
Processing some of the arguments, for example to run R under a debugger and to launch alternative front-ends to provide GUIs.
The first two of these can be achieved for your front-end by running it via R CMD. So, for example
R CMD /usr/local/lib/R/bin/exec/R
R CMD exec/R
will both work in a standard R installation. (R CMD looks first for executables in R_HOME/bin. These command-lines need modification if a sub-architecture is in use.) If you do not want to run your front-end in this way, you need to ensure that R_HOME is set and LD_LIBRARY_PATH is suitable. (The latter might well be, but modern Unix/Linux systems do not normally include /usr/local/lib (/usr/local/lib64 on some architectures), and R does look there for system components.)
The other senses in which this example is too simple are that all the internal defaults are used and that control is handed over to the R main loop. There are a number of small examples162 in the tests/Embedding directory. These make use of Rf_initEmbeddedR in src/main/Rembedded.c, and essentially use
#include <Rembedded.h>
int main(int ac, char **av)
{
/* do some setup */
Rf_initEmbeddedR(argc, argv);
/* do some more setup */
/* submit some code to R, which is done interactively via
run_Rmainloop();
A possible substitute for a pseudo-console is
R_ReplDLLinit();
while(R_ReplDLLdo1() > 0) {
/* add user actions here if desired */
}
*/
Rf_endEmbeddedR(0);
/* final tidying up after R is shutdown */
return 0;
}
If you do not want to pass R arguments, you can fake an argv array, for example by
char *argv[]= {"REmbeddedPostgres", "--silent"};
Rf_initEmbeddedR(sizeof(argv)/sizeof(argv[0]), argv);
However, to make a GUI we usually do want to run run_Rmainloop after setting up various parts of R to talk to our GUI, and arranging for our GUI callbacks to be called during the R mainloop.
One issue to watch is that on some platforms Rf_initEmbeddedR and Rf_endEmbeddedR change the settings of the FPU (e.g. to allow errors to be trapped and to make use of extended precision registers).
The standard code sets up a session temporary directory in the usual way, unless R_TempDir is set to a non-NULL value before Rf_initEmbeddedR is called. In that case the value is assumed to contain an existing writable directory, and it is not cleaned up when R is shut down.
Rf_initEmbeddedR sets R to be in interactive mode: you can set R_Interactive (defined in Rinterface.h) subsequently to change this.
Note that R expects to be run with the locale category ‘LC_NUMERIC’ set to its default value of C, and so should not be embedded into an application which changes that.
It is the user’s responsibility to attempt to initialize only once. To protect the R interpreter, Rf_initialize_R will exit the process if re-initialization is attempted.
Compiling against the R library
Setting R callbacks
Registering symbols
Meshing event loops
Threading issues
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8.1.1 Compiling against the R library
Suitable flags to compile and link against the R (shared or static) library can be found by
R CMD config --cppflags
R CMD config --ldflags
(These apply only to an uninstalled copy or a standard install.)
If R is installed, pkg-config is available and neither sub-architectures nor a macOS framework have been used, alternatives for a shared R library are
pkg-config --cflags libR
pkg-config --libs libR
and for a static R library
pkg-config --cflags libR
pkg-config --static --libs libR
(This may work for an installed OS framework if pkg-config is taught where to look for libR.pc: it is installed inside the framework.)
However, a more comprehensive way is to set up a Makefile to compile the front-end. Suppose file myfe.c is to be compiled to myfe. A suitable Makefile might be
## WARNING: does not work when ${R_HOME} contains spaces
include ${R_HOME}/etc${R_ARCH}/Makeconf
all: myfe
## The following is not needed, but avoids PIC flags.
myfe.o: myfe.c
$(CC) $(ALL_CPPFLAGS) $(CFLAGS) -c myfe.c -o $@
## replace $(LIBR) $(LIBS) by $(STATIC_LIBR) if R was build with a static libR
myfe: myfe.o
$(MAIN_LINK) -o $@ myfe.o $(LIBR) $(LIBS)
invoked as
R CMD make
R CMD myfe
Even though not recommended, ${R_HOME} may contain spaces. In that case, it cannot be passed as an argument to include in the makefile. Instead, one can instruct make using the -f option to include Makeconf, for example via recursive invocation of make, see Writing portable packages.
all:
$(MAKE) -f "${R_HOME}/etc${R_ARCH}/Makeconf" -f Makefile.inner
Additional flags which $(MAIN_LINK) includes are, amongst others, those to select OpenMP and --export-dynamic for the GNU linker on some platforms. In principle $(LIBS) is not needed when using a shared R library as libR is linked against those libraries, but some platforms need the executable also linked against them.
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8.1.2 Setting R callbacks
For Unix-alikes there is a public header file Rinterface.h that makes it possible to change the standard callbacks used by R in a documented way. This defines pointers (if R_INTERFACE_PTRS is defined)
extern void (*ptr_R_Suicide)(const char *);
extern void (*ptr_R_ShowMessage)(const char *);
extern int (*ptr_R_ReadConsole)(const char *, unsigned char *, int, int);
extern void (*ptr_R_WriteConsole)(const char *, int);
extern void (*ptr_R_WriteConsoleEx)(const char *, int, int);
extern void (*ptr_R_ResetConsole)();
extern void (*ptr_R_FlushConsole)();
extern void (*ptr_R_ClearerrConsole)();
extern void (*ptr_R_Busy)(int);
extern void (*ptr_R_CleanUp)(SA_TYPE, int, int);
extern int (*ptr_R_ShowFiles)(int, const char **, const char **,
const char *, Rboolean, const char *);
extern int (*ptr_R_ChooseFile)(int, char *, int);
extern int (*ptr_R_EditFile)(const char *);
extern void (*ptr_R_loadhistory)(SEXP, SEXP, SEXP, SEXP);
extern void (*ptr_R_savehistory)(SEXP, SEXP, SEXP, SEXP);
extern void (*ptr_R_addhistory)(SEXP, SEXP, SEXP, SEXP);
extern int (*ptr_R_EditFiles)(int, const char **, const char **, const char *);
extern SEXP (*ptr_do_selectlist)(SEXP, SEXP, SEXP, SEXP);
extern SEXP (*ptr_do_dataentry)(SEXP, SEXP, SEXP, SEXP);
extern SEXP (*ptr_do_dataviewer)(SEXP, SEXP, SEXP, SEXP);
extern void (*ptr_R_ProcessEvents)();
which allow standard R callbacks to be redirected to your GUI. What these do is generally documented in the file src/unix/system.txt.
Function: void R_ShowMessage (char *message) ¶
This should display the message, which may have multiple lines: it should be brought to the user’s attention immediately.
Function: void R_Busy (int which) ¶
This function invokes actions (such as change of cursor) when R embarks on an extended computation (which=1) and when such a state terminates (which=0).
Function: int R_ReadConsole (const char *prompt, unsigned char *buf, int buflen, int hist) ¶
Function: void R_WriteConsole (const char *buf, int buflen) ¶
Function: void R_WriteConsoleEx (const char *buf, int buflen, int otype) ¶
Function: void R_ResetConsole () ¶
Function: void R_FlushConsole () ¶
Function: void R_ClearerrConsole () ¶
These functions interact with a console.
R_ReadConsole prints the given prompt at the console and then does a fgets(3)–like operation, transferring up to buflen characters into the buffer buf. The last two bytes should be set to ‘"\n\0"’ to preserve sanity. If hist is non-zero, then the line should be added to any command history which is being maintained. The return value is 0 is no input is available and >0 otherwise.
R_WriteConsoleEx writes the given buffer to the console, otype specifies the output type (regular output or warning/error). Call to R_WriteConsole(buf, buflen) is equivalent to R_WriteConsoleEx(buf, buflen, 0). To ensure backward compatibility of the callbacks, ptr_R_WriteConsoleEx is used only if ptr_R_WriteConsole is set to NULL. To ensure that stdout() and stderr() connections point to the console, set the corresponding files to NULL via
R_Outputfile = NULL;
R_Consolefile = NULL;
R_ResetConsole is called when the system is reset after an error. R_FlushConsole is called to flush any pending output to the system console. R_ClearerrConsole clears any errors associated with reading from the console.
Function: int R_ShowFiles (int nfile, const char **file, const char **headers, const char *wtitle, Rboolean del, const char *pager) ¶
This function is used to display the contents of files.
Function: int R_ChooseFile (int new, char *buf, int len) ¶
Choose a file and return its name in buf of length len. Return value is 0 for success, > 0 otherwise.
Function: int R_EditFile (const char *buf) ¶
Send a file to an editor window.
Function: int R_EditFiles (int nfile, const char **file, const char **title, const char *editor) ¶
Send nfile files to an editor, with titles possibly to be used for the editor window(s).
Function: SEXP R_loadhistory (SEXP, SEXP, SEXP, SEXP); ¶
Function: SEXP R_savehistory (SEXP, SEXP, SEXP, SEXP); ¶
Function: SEXP R_addhistory (SEXP, SEXP, SEXP, SEXP); ¶
.Internal functions for loadhistory, savehistory and timestamp.
If the console has no history mechanism these can be as simple as
SEXP R_loadhistory (SEXP call, SEXP op, SEXP args, SEXP env)
{
errorcall(call, "loadhistory is not implemented");
return R_NilValue;
}
SEXP R_savehistory (SEXP call, SEXP op , SEXP args, SEXP env)
{
errorcall(call, "savehistory is not implemented");
return R_NilValue;
}
SEXP R_addhistory (SEXP call, SEXP op , SEXP args, SEXP env)
{
return R_NilValue;
}
The R_addhistory function should return silently if no history mechanism is present, as a user may be calling timestamp purely to write the time stamp to the console.
Function: void R_Suicide (const char *message) ¶
This should abort R as rapidly as possible, displaying the message. A possible implementation is
void R_Suicide (const char *message)
{
char pp[1024];
snprintf(pp, 1024, "Fatal error: %s\n", message);
R_ShowMessage(pp);
R_CleanUp(SA_SUICIDE, 2, 0);
}
Function: void R_CleanUp (SA_TYPE saveact, int status, int RunLast) ¶
This function invokes any actions which occur at system termination. It needs to be quite complex:
#include <Rinterface.h>
#include <Rembedded.h> /* for Rf_KillAllDevices */
void R_CleanUp (SA_TYPE saveact, int status, int RunLast)
{
if(saveact == SA_DEFAULT) saveact = SaveAction;
if(saveact == SA_SAVEASK) {
/* ask what to do and set saveact */
}
switch (saveact) {
case SA_SAVE:
if(runLast) R_dot_Last();
if(R_DirtyImage) R_SaveGlobalEnv();
/* save the console history in R_HistoryFile */
break;
case SA_NOSAVE:
if(runLast) R_dot_Last();
break;
case SA_SUICIDE:
default:
break;
}
R_RunExitFinalizers();
/* clean up after the editor e.g. CleanEd() */
R_CleanTempDir();
/* close all the graphics devices */
if(saveact != SA_SUICIDE) Rf_KillAllDevices();
fpu_setup(FALSE);
exit(status);
}
These callbacks should never be changed in a running R session (and hence cannot be called from an extension package).
Function: SEXP R_dataentry (SEXP, SEXP, SEXP, SEXP); ¶
Function: SEXP R_dataviewer (SEXP, SEXP, SEXP, SEXP); ¶
Function: SEXP R_selectlist (SEXP, SEXP, SEXP, SEXP); ¶
.External functions for dataentry (and edit on matrices and data frames), View and select.list. These can be changed if they are not currently in use.
Next: Meshing event loops, Previous: Setting R callbacks, Up: Embedding R under Unix-alikes [Contents][Index]
8.1.3 Registering symbols
An application embedding R needs a different way of registering symbols because it is not a dynamic library loaded by R as would be the case with a package. Therefore R reserves a special DllInfo entry for the embedding application such that it can register symbols to be used with .C, .Call etc. This entry can be obtained by calling getEmbeddingDllInfo, so a typical use is
DllInfo *info = R_getEmbeddingDllInfo();
R_registerRoutines(info, cMethods, callMethods, NULL, NULL);
The native routines defined by cMethods and callMethods should be present in the embedding application. See Registering native routines for details on registering symbols in general.
Next: Threading issues, Previous: Registering symbols, Up: Embedding R under Unix-alikes [Contents][Index]
8.1.4 Meshing event loops
One of the most difficult issues in interfacing R to a front-end is the handling of event loops, at least if a single thread is used. R uses events and timers for
Running X11 windows such as the graphics device and data editor, and interacting with them (e.g., using locator()).
Supporting Tcl/Tk events for the tcltk package (for at least the X11 version of Tk).
Preparing input.
Timing operations, for example for profiling R code and Sys.sleep().
Interrupts, where permitted.
Specifically, the Unix-alike command-line version of R runs separate event loops for
Preparing input at the console command-line, in file src/unix/sys-unix.c.
Waiting for a response from a socket in the internal functions for direct socket access in file src/modules/internet/Rsock.c and for the interface to libcurl.
Mouse and window events when displaying the X11-based dataentry window, in file src/modules/X11/dataentry.c. This is regarded as modal, and no other events are serviced whilst it is active.
There is a protocol for adding event handlers to the first two types of event loops, using types and functions declared in the header R_ext/eventloop.h and described in comments in file src/unix/sys-std.c. It is possible to add (or remove) an input handler for events on a particular file descriptor, or to set a polling interval (via R_wait_usec) and a function to be called periodically via R_PolledEvents: the polling mechanism is used by the tcltk package.
It is not intended that these facilities are used by packages, but if they are needed exceptionally, the package should ensure that it cleans up and removes its handlers when its namespace is unloaded. Note that the header sys/select.h is needed163: users should check this is available and define HAVE_SYS_SELECT_H before including R_ext/eventloop.h. (It is often the case that another header will include sys/select.h before eventloop.h is processed, but this should not be relied on.)
An alternative front-end needs both to make provision for other R events whilst waiting for input, and to ensure that it is not frozen out during events of the second type. The ability to add a polled handler as R_timeout_handler is used by the tcltk package.
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8.1.5 Threading issues
Embedded R is designed to be run in the main thread, and all the testing is done in that context. There is a potential issue with the stack-checking mechanism where threads are involved. This uses two variables declared in Rinterface.h (if CSTACK_DEFNS is defined) as
extern uintptr_t R_CStackLimit; /* C stack limit */
extern uintptr_t R_CStackStart; /* Initial stack address */
Note that uintptr_t is an optional C99 type for which a substitute is defined in R, so your code needs to define HAVE_UINTPTR_T appropriately. To do so, test if the type is defined in C header stdint.h or C++ header cstdint and if so include the header and define HAVE_UINTPTR_T before including Rinterface.h. (For C code one can simply include Rconfig.h, possibly via R.h, and for C++11 code Rinterface.h will include the header cstdint.)
These will be set164 when Rf_initialize_R is called, to values appropriate to the main thread. Stack-checking can be disabled by setting R_CStackLimit = (uintptr_t)-1 immediately after Rf_initialize_R is called, but it is better to if possible set appropriate values. (What these are and how to determine them are OS-specific, and the stack size limit may differ for secondary threads. If you have a choice of stack size, at least 10Mb is recommended.)
You may also want to consider how signals are handled: R sets signal handlers for several signals, including SIGINT, SIGSEGV, SIGPIPE, SIGUSR1 and SIGUSR2, but these can all be suppressed by setting the variable R_SignalHandlers (declared in Rinterface.h) to 0.
Note that these variables must not be changed by an R package: a package should not call R internals which makes use of the stack-checking mechanism on a secondary thread.
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8.2 Embedding R under Windows
All Windows interfaces to R call entry points in the DLL R.dll, directly or indirectly. Simpler applications may find it easier to use the indirect route via (D)COM.
Using (D)COM
Calling R.dll directly
Finding R_HOME
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8.2.1 Using (D)COM
(D)COM is a standard Windows mechanism used for communication between Windows applications. One application (here R) is run as COM server which offers services to clients, here the front-end calling application. The services are described in a ‘Type Library’ and are (more or less) language-independent, so the calling application can be written in C or C++ or Visual Basic or Perl or Python and so on. The ‘D’ in (D)COM refers to ‘distributed’, as the client and server can be running on different machines.
The basic R distribution is not a (D)COM server, but two addons are currently available that interface directly with R and provide a (D)COM server:
There is a (D)COM server called StatConnector written by Thomas Baier available via https://www.autstat.com/, which works with R packages to support transfer of data to and from R and remote execution of R commands, as well as embedding of an R graphics window.
Recent versions have usage restrictions.
Next: Finding R_HOME, Previous: Using (D)COM, Up: Embedding R under Windows [Contents][Index]
8.2.2 Calling R.dll directly
The R DLL is mainly written in C and has _cdecl entry points. Calling it directly will be tricky except from C code (or C++ with a little care).
There is a version of the Unix-alike interface calling
int Rf_initEmbeddedR(int ac, char **av);
void Rf_endEmbeddedR(int fatal);
which is an entry point in R.dll. Examples of its use (and a suitable Makefile.win) can be found in the tests/Embedding directory of the sources. You may need to ensure that R_HOME/bin is in your PATH so the R DLLs are found.
Examples of calling R.dll directly are provided in the directory src/gnuwin32/front-ends, including a simple command-line front end rtest.c whose code is
#define Win32
#include <windows.h>
#include <stdio.h>
#include <Rversion.h>
#define LibExtern __declspec(dllimport) extern
#include <Rembedded.h>
#include <R_ext/RStartup.h>
/* for askok and askyesnocancel */
#include <graphapp.h>
/* for signal-handling code */
#include <psignal.h>
/* simple input, simple output */
/* This version blocks all events: a real one needs to call ProcessEvents
frequently. See rterm.c and ../system.c for one approach using
a separate thread for input.
*/
int myReadConsole(const char *prompt, char *buf, int len, int addtohistory)
{
fputs(prompt, stdout);
fflush(stdout);
if(fgets(buf, len, stdin)) return 1; else return 0;
}
void myWriteConsole(const char *buf, int len)
{
printf("%s", buf);
}
void myCallBack(void)
{
/* called during i/o, eval, graphics in ProcessEvents */
}
void myBusy(int which)
{
/* set a busy cursor ... if which = 1, unset if which = 0 */
}
static void my_onintr(int sig) { UserBreak = 1; }
int main (int argc, char **argv)
{
structRstart rp;
Rstart Rp = &rp;
char Rversion[25], *RHome;
sprintf(Rversion, "%s.%s", R_MAJOR, R_MINOR);
if(strcmp(getDLLVersion(), Rversion) != 0) {
fprintf(stderr, "Error: R.DLL version does not match\n");
exit(1);
}
R_setStartTime();
R_DefParams(Rp);
if((RHome = get_R_HOME()) == NULL) {
fprintf(stderr, "R_HOME must be set in the environment or Registry\n");
exit(1);
}
Rp->rhome = RHome;
Rp->home = getRUser();
Rp->CharacterMode = LinkDLL;
Rp->ReadConsole = myReadConsole;
Rp->WriteConsole = myWriteConsole;
Rp->CallBack = myCallBack;
Rp->ShowMessage = askok;
Rp->YesNoCancel = askyesnocancel;
Rp->Busy = myBusy;
Rp->R_Quiet = TRUE; /* Default is FALSE */
Rp->R_Interactive = FALSE; /* Default is TRUE */
Rp->RestoreAction = SA_RESTORE;
Rp->SaveAction = SA_NOSAVE;
R_SetParams(Rp);
R_set_command_line_arguments(argc, argv);
FlushConsoleInputBuffer(GetStdHandle(STD_INPUT_HANDLE));
signal(SIGBREAK, my_onintr);
GA_initapp(0, 0);
readconsolecfg();
setup_Rmainloop();
#ifdef SIMPLE_CASE
run_Rmainloop();
#else
R_ReplDLLinit();
while(R_ReplDLLdo1() > 0) {
/* add user actions here if desired */
}
/* only get here on EOF (not q()) */
#endif
Rf_endEmbeddedR(0);
return 0;
}
The ideas are
Check that the front-end and the linked R.dll match – other front-ends may allow a looser match.
Find and set the R home directory and the user’s home directory. The former may be available from the Windows Registry: it will be in HKEY_LOCAL_MACHINE\Software\R-core\R\InstallPath from an administrative install and HKEY_CURRENT_USER\Software\R-core\R\InstallPath otherwise, if selected during installation (as it is by default).
Define startup conditions and callbacks via the Rstart structure. R_DefParams sets the defaults, and R_SetParams sets updated values.
Record the command-line arguments used by R_set_command_line_arguments for use by the R function commandArgs().
Set up the signal handler and the basic user interface.
Run the main R loop, possibly with our actions intermeshed.
Arrange to clean up.
An underlying theme is the need to keep the GUI ‘alive’, and this has not been done in this example. The R callback R_ProcessEvents needs to be called frequently to ensure that Windows events in R windows are handled expeditiously. Conversely, R needs to allow the GUI code (which is running in the same process) to update itself as needed – two ways are provided to allow this:
R_ProcessEvents calls the callback registered by Rp->callback. A version of this is used to run package Tcl/Tk for tcltk under Windows, for the code is
void R_ProcessEvents(void)
{
while (peekevent()) doevent(); /* Windows events for GraphApp */
if (UserBreak) { UserBreak = FALSE; onintr(); }
R_CallBackHook();
if(R_tcldo) R_tcldo();
}
The mainloop can be split up to allow the calling application to take some action after each line of input has been dealt with: see the alternative code below #ifdef SIMPLE_CASE.
It may be that no R GraphApp windows need to be considered, although these include pagers, the windows() graphics device, the R data and script editors and various popups such as choose.file() and select.list(). It would be possible to replace all of these, but it seems easier to allow GraphApp to handle most of them.
It is possible to run R in a GUI in a single thread (as RGui.exe shows) but it will normally be easier165 to use multiple threads.
Note that R’s own front ends use a stack size of 10Mb, whereas MinGW executables default to 2Mb, and Visual C++ ones to 1Mb. The latter stack sizes are too small for a number of R applications, so general-purpose front-ends should use a larger stack size.
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8.2.3 Finding R_HOME
Both applications which embed R and those which use a system call to invoke R (as Rscript.exe, Rterm.exe or R.exe) need to be able to find the R bin directory. The simplest way to do so is the ask the user to set an environment variable R_HOME and use that, but naive users may be flummoxed as to how to do so or what value to use.
The R for Windows installers have for a long time allowed the value of R_HOME to be recorded in the Windows Registry: this is optional but selected by default. Where it is recorded has changed over the years to allow for multiple versions of R to be installed at once, and to allow 32- and 64-bit versions of R to be installed on the same machine.
The basic Registry location is Software\R-core\R. For an administrative install this is under HKEY_LOCAL_MACHINE and on a 64-bit OS HKEY_LOCAL_MACHINE\Software\R-core\R is by default redirected for a 32-bit application, so a 32-bit application will see the information for the last 32-bit install, and a 64-bit application that for the last 64-bit install. For a personal install, the information is under HKEY_CURRENT_USER\Software\R-core\R which is seen by both 32-bit and 64-bit applications and so records the last install of either architecture. To circumvent this, there are locations Software\R-core\R32 and Software\R-core\R64 which always refer to one architecture.
When R is installed and recording is not disabled then two string values are written at that location for keys InstallPath and Current Version, and these keys are removed when R is uninstalled. To allow information about other installed versions to be retained, there is also a key named something like 3.0.0 or 3.0.0 patched or 3.1.0 Pre-release with a value for InstallPath.
So a comprehensive algorithm to search for R_HOME is something like
Decide which of personal or administrative installs should have precedence. There are arguments both ways: we find that with roaming profiles that HKEY_CURRENT_USER\Software often gets reverted to an earlier version. Do the following for one or both of HKEY_CURRENT_USER and HKEY_LOCAL_MACHINE.
If the desired architecture is known, look in Software\R-core\R32 or Software\R-core\R64, and if that does not exist or the architecture is immaterial, in Software\R-core\R.
If key InstallPath exists then this is R_HOME (recorded using backslashes). If it does not, look for version-specific keys like 2.11.0 alpha, pick the latest (which is of itself a complicated algorithm as 2.11.0 patched > 2.11.0 > 2.11.0 alpha > 2.8.1) and use its value for InstallPath.
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Function and variable index
Jump to:
. \
A B C D E F G I L M N O P Q R S T U V W
Index Entry
Section
.
.C: Interface functions .C and .Fortran
.Call: Handling R objects in C
.Call: Calling .Call
.External: Handling R objects in C
.External: Calling .External
.Fortran: Interface functions .C and .Fortran
.Last.lib: Load hooks
.onAttach: Load hooks
.onDetach: Load hooks
.onLoad: Load hooks
.onUnload: Load hooks
.Random.seed: Random numbers
\
\acronym: Marking text
\alias: Documenting functions
\arguments: Documenting functions
\author: Documenting functions
\bold: Marking text
\cite: Marking text
\code: Marking text
\command: Marking text
\concept: Indices
\cr: Sectioning
\CRANpkg{pkg}: User-defined macros
\deqn: Mathematics
\describe: Lists and tables
\description: Documenting functions
\details: Documenting functions
\dfn: Marking text
\doi{numbers}: User-defined macros
\dontrun: Documenting functions
\dontshow: Documenting functions
\dots: Insertions
\dQuote: Marking text
\email: Marking text
\emph: Marking text
\enc: Insertions
\enumerate: Lists and tables
\env: Marking text
\eqn: Mathematics
\examples: Documenting functions
\figure: Figures
\file: Marking text
\format: Documenting data sets
\href: Marking text
\if: Conditional text
\ifelse: Conditional text
\itemize: Lists and tables
\kbd: Marking text
\keyword: Documenting functions
\ldots: Insertions
\link: Cross-references
\method: Documenting functions
\name: Documenting functions
\newcommand: User-defined macros
\note: Documenting functions
\option: Marking text
\out: Conditional text
\packageAuthor: User-defined macros
\packageDescription: User-defined macros
\packageDESCRIPTION: User-defined macros
\packageIndices: User-defined macros
\packageMaintainer: User-defined macros
\packageTitle: User-defined macros
\pkg: Marking text
\preformatted: Marking text
\R: Insertions
\RdOpts: Dynamic pages
\references: Documenting functions
\renewcommand: User-defined macros
\S3method: Documenting functions
\samp: Marking text
\section: Sectioning
\seealso: Documenting functions
\Sexpr: Dynamic pages
\source: Documenting data sets
\sQuote: Marking text
\sspace: User-defined macros
\strong: Marking text
\tabular: Lists and tables
\title: Documenting functions
\url: Marking text
\usage: Documenting functions
\value: Documenting functions
\var: Marking text
\verb: Marking text
A
allocList: Evaluating R expressions from C
allocMatrix: Calculating numerical derivatives
allocVector: Allocating storage
any_duplicated: Semi-internal convenience functions
any_duplicated3: Semi-internal convenience functions
asChar: Some convenience functions
asComplex: Some convenience functions
asInteger: Some convenience functions
asLogical: Some convenience functions
asReal: Some convenience functions
AUTHORS: Package subdirectories
B
bessel_i: Mathematical functions
bessel_j: Mathematical functions
bessel_k: Mathematical functions
bessel_y: Mathematical functions
beta: Mathematical functions
BLAS_LIBS: Using Makevars
browser: Browsing
C
CAAR: Calling .External
CAD4R: Calling .External
CADDDR: Calling .External
CADDR: Calling .External
CADR: Calling .External
Calloc: User-controlled memory
CallocCharBuf: User-controlled memory
CAR: Calling .External
CDAR: Calling .External
CDDDR: Calling .External
CDDR: Calling .External
CDR: Calling .External
cgmin: Optimization
choose: Mathematical functions
CITATION: Package subdirectories
CITATION: Preparing translations
classgets: Classes
CleanEd: Setting R callbacks
coerceVector: Details of R types
COMPLEX: Vector accessor functions
CONS: Some convenience functions
cons: Some convenience functions
COPYRIGHTS: The DESCRIPTION file
COPYRIGHTS: Package subdirectories
cospi: Numerical Utilities
cPsort: Utility functions
D
dblepr: Printing from Fortran
dblepr1: Printing from Fortran
debug: Debugging R code
debugger: Debugging R code
defineVar: Finding and setting variables
digamma: Mathematical functions
dimgets: Attributes
dimnamesgets: Attributes
dump.frames: Debugging R code
duplicate: Named objects and copying
dyn.load: dyn.load and dyn.unload
dyn.unload: dyn.load and dyn.unload
E
elt: Some convenience functions
error: Error signaling
errorcall: Error signaling
eval: Evaluating R expressions from C
expm1: Numerical Utilities
export: Specifying imports and exports
exportClasses: Namespaces with S4 classes and methods
exportClassPattern: Namespaces with S4 classes and methods
exportMethods: Namespaces with S4 classes and methods
exportPattern: Specifying imports and exports
exportPattern: Namespaces with S4 classes and methods
exp_rand: Random numbers
F
FALSE: Mathematical constants
findFun: Evaluating R expressions from C
findInterval: Utility functions
findInterval2(double*: Utility functions
findVar: Finding and setting variables
FLIBS: Using Makevars
fmax2: Numerical Utilities
fmin2: Numerical Utilities
fprec: Numerical Utilities
fpu_setup: Setting R callbacks
Free: User-controlled memory
fround: Numerical Utilities
fsign: Numerical Utilities
ftrunc: Numerical Utilities
G
gammafn: Mathematical functions
gctorture: Using gctorture
GetArrayDimnames: Attributes
getAttrib: Attributes
getCharCE: Character encoding issues
GetColNames: Attributes
GetMatrixDimnames: Attributes
GetRNGstate: Random numbers
GetRowNames: Attributes
I
imax2: Numerical Utilities
imin2: Numerical Utilities
import: Specifying imports and exports
importClassesFrom: Namespaces with S4 classes and methods
importFrom: Specifying imports and exports
importMethodsFrom: Namespaces with S4 classes and methods
install: Attributes
installChar: Finding and setting variables
INTEGER: Vector accessor functions
integr_fn: Integration
intpr: Printing from Fortran
intpr1: Printing from Fortran
iPsort: Utility functions
isArray: Some convenience functions
isComplex: Details of R types
isEnvironment: Details of R types
isExpression: Details of R types
isFactor: Some convenience functions
isFunction: Some convenience functions
isInteger: Details of R types
isLanguage: Some convenience functions
isList: Some convenience functions
isLogical: Details of R types
isMatrix: Some convenience functions
ISNA: Missing and special values
ISNA: Missing and IEEE values
ISNAN: Missing and special values
ISNAN: Missing and IEEE values
isNewList: Some convenience functions
isNull: Details of R types
isNumeric: Some convenience functions
isObject: Some convenience functions
isOrdered: Some convenience functions
isPairList: Some convenience functions
isPrimitive: Some convenience functions
isReal: Details of R types
isString: Details of R types
isSymbol: Details of R types
isTs: Some convenience functions
isUnordered: Some convenience functions
isVector: Some convenience functions
isVectorAtomic: Some convenience functions
isVectorList: Some convenience functions
L
labelpr: Printing from Fortran
lang1: Some convenience functions
lang2: Some convenience functions
lang3: Some convenience functions
lang4: Some convenience functions
lang5: Some convenience functions
lang6: Some convenience functions
LAPACK_LIBS: Using Makevars
lastElt: Some convenience functions
lbeta: Mathematical functions
lbfgsb: Optimization
lchoose: Mathematical functions
LCONS: Some convenience functions
lcons: Some convenience functions
lgamma1p: Numerical Utilities
lgammafn: Mathematical functions
library.dynam: Package subdirectories
library.dynam: dyn.load and dyn.unload
list1: Some convenience functions
list2: Some convenience functions
list3: Some convenience functions
list4: Some convenience functions
list5: Some convenience functions
list6: Some convenience functions
log1mexp: Numerical Utilities
log1p: Numerical Utilities
log1pexp: Numerical Utilities
log1pmx: Numerical Utilities
LOGICAL: Vector accessor functions
logspace_add: Numerical Utilities
logspace_sub: Numerical Utilities
logspace_sum: Numerical Utilities
M
MARK_NOT_MUTABLE: Named objects and copying
MAYBE_REFERENCED: Named objects and copying
MAYBE_SHARED: Named objects and copying
Memcpy: User-controlled memory
Memzero: User-controlled memory
mkChar: Handling character data
mkCharCE: Character encoding issues
mkCharLen: Handling character data
mkCharLenCE: Character encoding issues
mkString: Some convenience functions
M_E: Mathematical constants
M_PI: Mathematical constants
N
namesgets: Attributes
NA_REAL: Missing and IEEE values
NEWS.Rd: Package subdirectories
nmmin: Optimization
norm_rand: Random numbers
nrows: Transient storage allocation
nthcdr: Some convenience functions
O
OBJECTS: Using Makevars
OBJECTS: Creating shared objects
onintr: Calling R.dll directly
optimfn: Optimization
optimgr: Optimization
P
pentagamma: Mathematical functions
PKG_CFLAGS: Creating shared objects
PKG_CPPFLAGS: Creating shared objects
PKG_CXXFLAGS: Creating shared objects
PKG_FCFLAGS: Using F9x code
PKG_FFLAGS: Creating shared objects
PKG_LIBS: Creating shared objects
PKG_OBJCFLAGS: Creating shared objects
PKG_OBJCXXFLAGS: Creating shared objects
PRINTNAME: Calling .External
PrintValue: Inspecting R objects
prompt: Rd format
PROTECT: Garbage Collection
protect: Garbage Collection
PROTECT_WITH_INDEX: Garbage Collection
psigamma: Mathematical functions
PutRNGstate: Random numbers
Q
qsort3: Utility functions
qsort4: Utility functions
R
R CMD build: Building package tarballs
R CMD check: Checking packages
R CMD config: Configure and cleanup
R CMD Rd2pdf: Processing documentation files
R CMD Rdconv: Processing documentation files
R CMD SHLIB: Creating shared objects
R CMD Stangle: Processing documentation files
R CMD Sweave: Processing documentation files
RAW: Vector accessor functions
rchkusr: Allowing interrupts
Rdqagi: Integration
Rdqags: Integration
REAL: Vector accessor functions
Realloc: User-controlled memory
realpr: Printing from Fortran
realpr1: Printing from Fortran
recover: Debugging R code
reEnc: Character encoding issues
REprintf: Printing
REPROTECT: Garbage Collection
REvprintf: Printing
revsort: Utility functions
Rf_endEmbeddedR: Embedding R under Unix-alikes
Rf_initEmbeddedR: Embedding R under Unix-alikes
Rf_initialize_R: Embedding R under Unix-alikes
Rf_KillAllDevices: Setting R callbacks
Rf_mainloop: Embedding R under Unix-alikes
Riconv: Re-encoding
Riconv_close: Re-encoding
Riconv_open: Re-encoding
Rprintf: Printing
Rprof: Profiling R code for speed
Rprof: Memory statistics from Rprof
Rprofmem: Tracking memory allocations
rPsort: Utility functions
rsort_with_index: Utility functions
run_Rmainloop: Embedding R under Unix-alikes
Rvprintf: Printing
R_addhistory: Setting R callbacks
R_alloc: Transient storage allocation
R_allocLD: Transient storage allocation
R_Busy: Setting R callbacks
R_Calloc: User-controlled memory
R_CheckUserInterrupt: Allowing interrupts
R_ChooseFile: Setting R callbacks
R_CleanTempDir: Setting R callbacks
R_CleanUp: Setting R callbacks
R_ClearerrConsole: Setting R callbacks
R_ClearExternalPtr: External pointers and weak references
R_compute_identical: Semi-internal convenience functions
R_ContinueUnwind: Condition handling and cleanup code
R_csort: Utility functions
R_dataentry: Setting R callbacks
R_dataviewer: Setting R callbacks
R_DefParams: Calling R.dll directly
R_dot_Last: Setting R callbacks
R_EditFile: Setting R callbacks
R_EditFiles: Setting R callbacks
R_ExpandFileName: Utility functions
R_ExternalPtrAddr: External pointers and weak references
R_ExternalPtrAddrFn: External pointers and weak references
R_ExternalPtrProtected: External pointers and weak references
R_ExternalPtrTag: External pointers and weak references
R_FINITE: Missing and IEEE values
R_FlushConsole: Setting R callbacks
R_forceSymbols: Registering native routines
R_Free: User-controlled memory
R_free_tmpnam: Utility functions
R_GetCCallable: Linking to native routines in other packages
R_GetCurrentEnv: Evaluating R expressions from C
R_GetCurrentSrcref: Accessing source references
R_getEmbeddingDllInfo: Registering symbols
R_GetSrcFilename: Accessing source references
R_INLINE: Inlining C functions
R_IsNaN: Missing and IEEE values
R_isort: Utility functions
R_LIBRARY_DIR: Configure and cleanup
R_loadhistory: Setting R callbacks
R_MakeExternalPtr: External pointers and weak references
R_MakeExternalPtrFn: External pointers and weak references
R_MakeUnwindCont: Condition handling and cleanup code
R_MakeWeakRef: External pointers and weak references
R_MakeWeakRefC: External pointers and weak references
R_max_col: Utility functions
R_NegInf: Missing and IEEE values
R_NewEnv: Finding and setting variables
R_NewPreciousMSet: Garbage Collection
R_orderVector: Utility functions
R_orderVector1: Utility functions
R_PACKAGE_DIR: Configure and cleanup
R_PACKAGE_DIR: Configure and cleanup
R_PACKAGE_NAME: Configure and cleanup
R_PACKAGE_NAME: Configure and cleanup
R_ParseVector: Parsing R code from C
R_PolledEvents: Meshing event loops
R_PosInf: Missing and IEEE values
R_pow: Numerical Utilities
R_pow_di: Numerical Utilities
R_PreserveInMSet: Garbage Collection
R_PreserveObject: Garbage Collection
R_ProcessEvents: Calling R.dll directly
R_ProtectWithIndex: Garbage Collection
R_PV: Inspecting R objects
R_qsort: Utility functions
R_qsort_I: Utility functions
R_qsort_int: Utility functions
R_qsort_int_I: Utility functions
R_ReadConsole: Setting R callbacks
R_Realloc: User-controlled memory
R_RegisterCCallable: Linking to native routines in other packages
R_RegisterCFinalizer: External pointers and weak references
R_RegisterCFinalizerEx: External pointers and weak references
R_RegisterFinalizer: External pointers and weak references
R_RegisterFinalizerEx: External pointers and weak references
R_registerRoutines: Registering native routines
R_ReleaseFromMSet: Garbage Collection
R_ReleaseObject: Garbage Collection
R_ReplDLLdo1: Embedding R under Unix-alikes
R_ReplDLLinit: Embedding R under Unix-alikes
R_Reprotect: Garbage Collection
R_ResetConsole: Setting R callbacks
R_rsort: Utility functions
R_RunExitFinalizers: Setting R callbacks
R_RunWeakRefFinalizer: External pointers and weak references
R_SaveGlobalEnv: Setting R callbacks
R_savehistory: Setting R callbacks
R_selectlist: Setting R callbacks
R_SetExternalPtrAddr: External pointers and weak references
R_SetExternalPtrProtected: External pointers and weak references
R_SetExternalPtrTag: External pointers and weak references
R_SetParams: Calling R.dll directly
R_setStartTime: Calling R.dll directly
R_set_command_line_arguments: Calling R.dll directly
R_ShowFiles: Setting R callbacks
R_ShowMessage: Setting R callbacks
R_Srcref: Accessing source references
R_Suicide: Setting R callbacks
R_tmpnam: Utility functions
R_tmpnam2: Utility functions
R_tryCatch: Condition handling and cleanup code
R_tryCatchError: Condition handling and cleanup code
R_unif_index: Random numbers
R_UnwindProtect: Condition handling and cleanup code
R_useDynamicSymbols: Registering native routines
R_Version: Platform and version information
R_wait_usec: Meshing event loops
R_WeakRefKey: External pointers and weak references
R_WeakRefValue: External pointers and weak references
R_withCallingErrorHandler: Condition handling and cleanup code
R_WriteConsole: Setting R callbacks
R_WriteConsoleEx: Setting R callbacks
S
S3method: Registering S3 methods
SAFE_FFLAGS: Using Makevars
samin: Optimization
ScalarComplex: Some convenience functions
ScalarInteger: Some convenience functions
ScalarLogical: Some convenience functions
ScalarRaw: Some convenience functions
ScalarReal: Some convenience functions
ScalarString: Some convenience functions
seed_in: Random numbers
seed_out: Random numbers
setAttrib: Attributes
SETCAD4R: Calling .External
SETCADDDR: Calling .External
SETCADDR: Calling .External
SETCADR: Calling .External
SETCAR: Calling .External
SETCDR: Calling .External
setup_Rmainloop: Calling R.dll directly
setVar: Finding and setting variables
SET_STRING_ELT: Handling character data
SET_TAG: Evaluating R expressions from C
SET_VECTOR_ELT: Vector accessor functions
sign: Numerical Utilities
signrank_free: Distribution functions
sinpi: Numerical Utilities
str2type: Some convenience functions
STRING_ELT: Handling character data
summaryRprof: Memory statistics from Rprof
system: Operating system access
system.time: Operating system access
system2: Operating system access
S_alloc: Transient storage allocation
S_realloc: Transient storage allocation
T
TAG: Calling .External
tanpi: Numerical Utilities
tetragamma: Mathematical functions
trace: Debugging R code
traceback: Debugging R code
tracemem: Tracing copies of an object
translateChar: Character encoding issues
translateCharUTF8: Character encoding issues
trigamma: Mathematical functions
TRUE: Mathematical constants
type2char: Some convenience functions
type2str: Some convenience functions
TYPEOF: Calling .External
U
undebug: Debugging R code
unif_rand: Random numbers
UNPROTECT: Garbage Collection
unprotect: Garbage Collection
UNPROTECT_PTR: Garbage Collection
unprotect_ptr: Garbage Collection
untracemem: Tracing copies of an object
useDynLib: useDynLib
V
VECTOR_ELT: Vector accessor functions
vmaxget: Transient storage allocation
vmaxset: Transient storage allocation
vmmin: Optimization
W
warning: Error signaling
warningcall: Error signaling
warningcall_immediate: Error signaling
wilcox_free: Distribution functions
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. \
A B C D E F G I L M N O P Q R S T U V W
Previous: Function and variable index, Up: Writing R Extensions [Contents][Index]
Concept index
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A B C D E F G H I L M N O P R S T U V W Z
Index Entry
Section
.
.install_extras file: Writing package vignettes
.Rbuildignore file: Building package tarballs
.Rinstignore file: Package subdirectories
\
\linkS4class: Cross-references
A
Allocating storage: Allocating storage
Attributes: Attributes
B
Bessel functions: Mathematical functions
Beta function: Mathematical functions
Building binary packages: Building binary packages
Building source packages: Building package tarballs
C
C++ code, interfacing: Interfacing C++ code
Calling C from Fortran and vice versa: Calling C from Fortran and vice versa
Checking packages: Checking packages
citation: Package subdirectories
citation: Preparing translations
Classes: Classes
Cleanup code: Condition handling and cleanup code
cleanup file: Package structure
Condition handling: Condition handling and cleanup code
conditionals: Conditional text
configure file: Package structure
Copying objects: Named objects and copying
CRAN: Creating R packages
Creating packages: Creating R packages
Creating shared objects: Creating shared objects
Cross-references in documentation: Cross-references
cumulative hazard: Distribution functions
D
Debugging: Debugging compiled code
DESCRIPTION file: The DESCRIPTION file
Details of R types: Details of R types
Distribution functions from C: Distribution functions
Documentation, writing: Writing R documentation files
Dynamic loading: dyn.load and dyn.unload
dynamic pages: Dynamic pages
E
Editing Rd files: Editing Rd files
encoding: Encoding
Error handling: Condition handling and cleanup code
Error signaling from C: Error signaling
Error signaling from Fortran: Error signaling from Fortran
Evaluating R expressions from C: Evaluating R expressions from C
external pointer: External pointers and weak references
F
Figures in documentation: Figures
finalizer: External pointers and weak references
Finding variables: Finding and setting variables
G
Gamma function: Mathematical functions
Garbage collection: Garbage Collection
Generic functions: Generic functions and methods
H
handling character data: Handling character data
Handling lists: Handling lists
Handling R objects in C: Handling R objects in C
I
IEEE special values: Missing and special values
IEEE special values: Missing and IEEE values
INDEX file: The INDEX file
Indices: Indices
Inspecting R objects when debugging: Inspecting R objects
integration: Integration
Interfaces to compiled code: Interface functions .C and .Fortran
Interfaces to compiled code: Interface functions .Call and .External
Interfacing C++ code: Interfacing C++ code
Interrupts: Allowing interrupts
L
LICENCE file: Licensing
LICENSE file: Licensing
Lists and tables in documentation: Lists and tables
M
Marking text in documentation: Marking text
Mathematics in documentation: Mathematics
Memory allocation from C: Memory allocation
Memory use: Profiling R code for memory use
Method functions: Generic functions and methods
Missing values: Missing and special values
Missing values: Missing and IEEE values
N
namespaces: Package namespaces
news: Package subdirectories
Numerical analysis subroutines from C: Numerical analysis subroutines
Numerical derivatives: Calculating numerical derivatives
O
OpenMP: OpenMP support
OpenMP: Platform and version information
Operating system access: Operating system access
optimization: Optimization
P
Package builder: Building package tarballs
Package structure: Package structure
Package subdirectories: Package subdirectories
Packages: Creating R packages
Parsing R code from C: Parsing R code from C
Platform-specific documentation: Platform-specific sections
Printing from C: Printing
Printing from Fortran: Printing from Fortran
Processing Rd format: Processing documentation files
Profiling: Profiling R code for speed
Profiling: Profiling R code for memory use
Profiling: Profiling compiled code
R
Random numbers in C: Random numbers
Random numbers in C: Distribution functions
Random numbers in Fortran: Calling C from Fortran and vice versa
Registering native routines: Registering native routines
S
Setting variables: Finding and setting variables
Sort functions from C: Utility functions
Sweave: Writing package vignettes
T
tarballs: Building package tarballs
Tidying R code: Tidying R code
U
user-defined macros: User-defined macros
V
Version information from C: Platform and version information
vignettes: Writing package vignettes
Visibility: Controlling visibility
W
weak reference: External pointers and weak references
Z
Zero-finding: Zero-finding
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A B C D E F G H I L M N O P R S T U V W Z
Footnotes
(1)
although this is a persistent mis-usage. It seems to stem from S, whose analogues of R’s packages were officially known as library sections and later as chapters, but almost always referred to as libraries.
(2)
This seems to be commonly used for a file in ‘markdown’ format. Be aware that most users of R will not know that, nor know how to view such a file: platforms such as macOS and Windows do not have a default viewer set in their file associations. The CRAN package web pages render such files in HTML: the converter used expects the file to be encoded in UTF-8.
(3)
currently, top-level files .Rbuildignore and .Rinstignore, and vignettes/.install_extras.
(4)
false positives are possible, but only a handful have been seen so far.
(5)
at least if this is done in a locale which matches the package encoding.
(6)
and required by CRAN, so checked by R CMD check --as-cran.
(7)
But it is checked for Open Source packages by R CMD check --as-cran.
(8)
Duplicate definitions may trigger a warning: see User-defined macros.
(9)
bug.report will try to extract an email address from a Contact field if there is no BugReports field.
(10)
CRAN expands them to e.g. GPL-2 | GPL-3.
(11)
even one wrapped in \donttest.
(12)
This includes all packages directly called by library and require calls, as well as data obtained via data(theirdata, package = "somepkg") calls: R CMD check will warn about all of these. But there are subtler uses which it may not detect: e.g. if package A uses package B and makes use of functionality in package B which uses package C which package B suggests or enhances, then package C needs to be in the ‘Suggests’ list for package A. Nor will undeclared uses in included files be reported, nor unconditional uses of packages listed under ‘Enhances’. R CMD check --as-cran will detect more of the subtler uses.
(13)
Extensions .S and .s arise from code originally written for S(-PLUS), but are commonly used for assembler code. Extension .q was used for S, which at one time was tentatively called QPE.
(14)
but they should be in the encoding declared in the DESCRIPTION file.
(15)
This is true for OSes which implement the ‘C’ locale: Windows’ idea of the ‘C’ locale uses the WinAnsi charset.
(16)
More precisely, they can contain the English alphanumeric characters and the symbols ‘$ - _ . + ! ' ( ) , ; = &’.
(17)
either or both of which may not be supported on particular platforms. Their main use is on macOS, but unfortunately recent versions of the macOS SDK have removed much of the support for Objective C v1.0 and Objective C++.
(18)
Using .hpp is not guaranteed to be portable.
(19)
There is also ‘__APPLE_CC__’, but that indicates a compiler with Apple-specific features not the OS, although for historical reasons is is defined by LLVM clang. It is used in Rinlinedfuns.h.
(20)
the POSIX terminology, called ‘make variables’ by GNU make.
(21)
The best way to generate such a file is to copy the .Rout from a successful run of R CMD check. If you want to generate it separately, do run R with options --vanilla --no-echo and with environment variable LANGUAGE=en set to get messages in English. Be careful not to use output with the option --timings (and note that --as-cran sets it).
(22)
e.g. https://tools.ietf.org/html/rfc4180.
(23)
People who have trouble with case are advised to use .rda as a common error is to refer to abc.RData as abc.Rdata!
(24)
For all the CRAN packages tested, either gz or bzip2 provided a very substantial reduction in installed size.
(25)
‘BWidget’ still is on Windows but ‘Tktable’ was not in R 4.0.0.
(26)
The script should only assume a POSIX-compliant /bin/sh – see https://pubs.opengroup.org/onlinepubs/9699919799/utilities/V3_chap02.html. In particular bash extensions must not be used, and not all R platforms have a bash command, let alone one at /bin/bash. All known shells used with R support the use of backticks, but not all support ‘$(cmd)’. However, real-world shells are not fully POSIX-compliant and omissions and idiosyncrasies need to be worked around—which Autoconf will do for you. Arithmetic expansion is a known issue: see https://www.gnu.org/software/autoconf/manual/autoconf.html#Portable-Shell for this and others. Some checks can be done by the checkbashisms Perl script at https://sourceforge.net/projects/checkbaskisms/files, also available in most Linux distributions in a package named either ‘devscripts’ or ‘devscripts-checkbashisms’: a later version can be extracted from Debian sources such as the most recent tar.xz in https://deb.debian.org/debian/pool/main/d/devscripts/ and has been needed for recent versions of Perl.
(27)
https://www.gnu.org/software/autoconf-archive/ax_blas.html. If you include macros from that archive you need to arrange for them to be included in the package sources for use by autoreconf.
(28)
but it is available on the machines used to produce the CRAN binary packages: however as Apple does not ship .pc files for its system libraries such as ‘zlib’, it may well not find information on these.
(29)
It is not wise to check the version of pkg-config as it is sometimes a link to pkgconf, a separate project with a different version series.
(30)
but not all projects get this right when only a static library is installed, so it is often necessary to try in turn pkg-config --libs and pkg-config --static --libs.
(31)
a decade ago Autoconf used configure.in: this is still accepted but should be renamed and autoreconf as used by R CMD check --as-cran will report as such.
(32)
For those using autoconf 2.70 or later there is also AC_CONFIG_MACRO_DIRS which allows multiple directories to be specified.
(33)
in POSIX parlance: GNU make calls these ‘make variables’.
(34)
at least on Unix-alikes: the Windows build currently resolves such dependencies to a static Fortran library when Rblas.dll is built. Also, not if USE_FC_TO_LINK is used.
(35)
https://www.openmp.org/, https://en.wikipedia.org/wiki/OpenMP, https://hpc.llnl.gov/training/tutorials/openmp-tutorial
(36)
Default builds of clang 3.8.0 and later have support for OpenMP, but the libomp run-time library may not be installed.
(37)
In most implementations the _OPENMP macro has value a date which can be mapped to an OpenMP version: for example, value 201307 is the date of version 4.0 (July 2013). However this may be used to denote the latest version which is partially supported, not that which is fully implemented.
(38)
Windows default, not MinGW-w64 default.
(39)
Which it was at the time of writing with GCC, Oracle, Intel and Clang compilers. The count may include the thread running the main process.
(40)
Be careful not to declare nthreads as const int: the Oracle compiler requires it to be ‘an lvalue’.
(41)
some Windows toolchains had the typo ‘_REENTRANCE’ instead.
(42)
A few OSes (AIX, Windows) do not need special flags for such code, but most do—although compilers will often generate PIC code when not asked to do so.
(43)
for versions of R since 3.6.0.
(44)
Some changes are linked from https://isocpp.org/std/standing-documents/sd-6-sg10-feature-test-recommendations: there were also additional deprecations.
(45)
Values 201103L, 201402L and 201703L are most commonly used for C++11, C++14 and C++17 respectively, but some compilers set 1L. At the time of writing there was no official value for C++20, but some compilers are using 202002L, others 201703L.
(46)
See https://isocpp.org/std/standing-documents/sd-6-sg10-feature-test-recommendations or https://en.cppreference.com/w/cpp/experimental/feature_test. It seems a reasonable assumption that any compiler promising some C++14 conformance will provide these—e.g. g++ 4.9.x did but 4.8.5 did not.
(47)
On systems which use sub-architectures, architecture-specific versions such as ~/.R/check.Renviron.i386 take precedence.
(48)
A suitable file.exe is part of the Windows toolset: it checks for gfile if a suitable file is not found: the latter is available in the OpenCSW collection for Solaris at https://www.opencsw.org/. The source repository is http://ftp.astron.com/pub/file/.
(49)
An exception is made for subdirectories with names starting ‘win’ or ‘Win’.
(50)
on most other platforms such runtime libraries are dynamic, but static libraries are currently used on Windows because the toolchain is not a standard part of the OS.
(51)
or if option --use-valgrind is used or environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value or if there are differences from a target output file
(52)
For example, in early 2014 gdata declared ‘Imports: gtools’ and gtools declared ‘Imports: gdata’.
(53)
loading, examples, tests, running vignette code
(54)
called CVS or .svn or .arch-ids or .bzr or .git (but not files called .git) or .hg.
(55)
called .metadata.
(56)
which is an error: GNU make uses GNUmakefile.
(57)
see tools:::.hidden_file_exclusions and tools:::get_exclude_patterns() for further excluded files and file patterns, respectively.
(58)
and to avoid problems with case-insensitive file systems, lower-case versions of all these extensions.
(59)
unless inhibited by using ‘BuildVignettes: no’ in the DESCRIPTION file.
(60)
provided the conditions of the package’s license are met: many, including CRAN, see the omission of source components as incompatible with an Open Source license.
(61)
R_HOME/bin is prepended to the PATH so that references to R or Rscript in the Makefile do make use of the currently running version of R.
(62)
Note that lazy-loaded datasets are not in the package’s namespace so need to be accessed via ::, e.g. survival::survexp.us.
(63)
they will be called with two unnamed arguments, in that order.
(64)
NB: this will only be read in all versions of R if the package contains R code in a R directory.
(65)
Note that this is the basename of the shared object, and the appropriate extension (.so or .dll) will be added.
(66)
This defaults to the same pattern as exportPattern: use something like exportClassPattern("^$") to override this.
(67)
if it does, there will be opaque warnings about replacing imports if the classes/methods are also imported.
(68)
People use dev.new() to open a device at a particular size: that is not portable but using dev.new(noRStudioGD = TRUE) helps.
(69)
Solaris make does not accept CRLF-terminated Makefiles; Solaris warns about and some other makes ignore incomplete final lines.
(70)
This was apparently introduced in SunOS 4, and is available elsewhere provided it is surrounded by spaces.
(71)
GNU make, BSD make and other variants of pmake in FreeBSD, NetBSD and formerly in macOS, AT&T make as implemented on Solaris and ‘Distributed Make’ (dmake), part of Oracle Developer Studio and available in other versions including from Apache OpenOffice.
(72)
For example, test options -a and -e are not portable, and not supported in the AT&T Bourne shell used on Solaris 10/11, even though they are in the POSIX standard. Nor does Solaris support ‘$(cmd)’.
(73)
as from R 4.0.0 the default is bash.
(74)
it was not in the Bourne shell, and is not supported by Solaris 10.
(75)
http://fortranwiki.org/fortran/show/Modernizing+Old+Fortran may help explain some of the warnings from gfortran -Wall -pedantic.
(76)
These are optional because the corresponding types are, but must be provided if the types are.
(77)
or where supported the variants _Exit and _exit.
(78)
This and srandom are in any case not portable. They are in POSIX but not in the C99 standard, and not available on Windows.
(79)
in libselinux.
(80)
At least Linux and Windows, but not macOS.
(81)
except perhaps the simplest kind as used by download.file() in non-interactive use.
(82)
Whereas the GNU linker reorders so -L options are processed first, the Solaris one does not.
(83)
some versions of macOS did not.
(84)
which is /usr/ccs/bin/strip, not /usr/bin/strip.
(85)
If a Java interpreter is required directly (not via rJava) this must be declared and its presence tested like any other external command.
(86)
For example, the ability to handle ‘https://’ URLs.
(87)
Not doing so is the default on Windows, overridden for the R executables. It is also the default on some Solaris compilers.
(88)
These are not needed for the default compiler settings on ‘x86_64’ but are likely to be needed on ‘ix86’.
(89)
Select ‘Save as’, and select ‘Reduce file size’ from the ‘Quartz filter’ menu’: this can be accessed in other ways, for example by Automator.
(90)
except perhaps some special characters such as backslash and hash which may be taken over for currency symbols.
(91)
Typically on a Unix-alike this is done by telling fontconfig where to find suitable fonts to select glyphs from.
(92)
which it is on all known platforms, and is required as from R 4.0.0
(93)
Ubuntu provides 5 years of support (but people were running 14.04 after 7 years) and RHEL provides 10 years full support and up to 14 with extended support.
(94)
This is seen on Linux, Solaris and FreeBSD, although each has other ways to turn on all extensions, e.g. defining _GNU_SOURCE, __EXTENSIONS__ or _BSD_SOURCE: the GCC compilers by default define _GNU_SOURCE unless a strict standard such as -std=c99 is used. On macOS extensions are declared unless one of these macros is given too small a value.
(95)
Solaris 10 does not recognize this value of _POSIX_C_SOURCE, nor values of _XOPEN_SOURCE beyond 600 (700 corresponds to POSIX 2008). Further, the value of 500 is not allowed in C99 mode, R’s default for C code.
(96)
‘SunOS 5.10’ is the most current for Solaris.
(97)
often taken from the toolchain’s headers.
(98)
also part of C++11 and later.
(99)
There is a portable way to do this in Fortran 2003 (ieee_is_nan() in module ieee_arithmetic), but ironically that is not supported in the versions 4.x of GNU Fortran. A pretty robust alternative is to test if(my_var /= my_var).
(100)
which often is the same as the header included by the C compiler, but some compilers have wrappers for some of the C headers.
(101)
including EAX, EBP, EBX, ECX, EDI,EDX, EFL, EIP, ESI and ESP .
(102)
https://stackoverflow.com/questions/32739018/a-replacement-for-stdbind2nd
(103)
it is allowed but ignored in system headers.
(104)
see https://gcc.gnu.org/gcc-10/porting_to.html.
(105)
See https://prereleases.llvm.org/11.0.0/rc2/tools/clang/docs/ReleaseNotes.html#modified-compiler-flags.
(106)
In principle this could depend on the OS, but has been checked on Linux and macOS.
(107)
e.g. \alias, \keyword and \note sections.
(108)
There can be exceptions: for example Rd files are not allowed to start with a dot, and have to be uniquely named on a case-insensitive file system.
(109)
in the current locale, and with special treatment for LaTeX special characters and with any ‘pkgname-package’ topic moved to the top of the list.
(110)
Text between or after list items is discouraged.
(111)
as defined by the R function trimws.
(112)
Currently it is rendered differently only in HTML conversions, and LaTeX conversion outside ‘\usage’ and ‘\examples’ environments.
(113)
There is only a fine distinction between \dots and \ldots. It is technically incorrect to use \ldots in code blocks and tools::checkRd will warn about this—on the other hand the current converters treat them the same way in code blocks, and elsewhere apart from the small distinction between the two in LaTeX.
(114)
See the examples section in the file Paren.Rd for an example.
(115)
R 2.9.0 added support for UTF-8 Cyrillic characters in LaTeX, but on some OSes this will need Cyrillic support added to LaTeX, so environment variable _R_CYRILLIC_TEX_ may need to be set to a non-empty value to enable this.
(116)
R has to be built to enable this, but the option --enable-R-profiling is the default.
(117)
For Unix-alikes these are intervals of CPU time, and for Windows of elapsed time.
(118)
With the exceptions of the commands listed below: an object of such a name can be printed via an explicit call to print.
(119)
Those in some numeric, logical, integer, raw, complex vectors and in memory allocated by R_alloc.
(120)
including using the data sections of R vectors after they are freed.
(121)
small fixed-size arrays by default in gfortran, for example.
(122)
currently on ‘x86_64’/‘ix86’ Linux and FreeBSD, with some support for Intel macOS, NetBSD and Windows, but not with the toolchains normally used with R. (There is a faster variant, HWASAN, for ‘aarch64’ only.) On some platforms the runtime library, libasan, needs to be installed separately, and for checking C++ you may also need libubsan.
(123)
see https://llvm.org/devmtg/2014-04/PDFs/LightningTalks/EuroLLVM%202014%20--%20container%20overflow.pdf.
(124)
part of the LLVM project and distributed in llvm RPMs and .debs on Linux. It is not currently shipped by Apple.
(125)
as Ubuntu has been said to do.
(126)
installed on some Linux systems as asan_symbolize, and obtainable from https://github.com/llvm/llvm-project/blob/main/compiler-rt/lib/asan/scripts/asan_symbolize.py: it makes use of llvm-symbolizer if available.
(127)
including gcc 7.1 and clang 4.0.0: for gcc it is implied by -fsanitize=address.
(128)
for example, X11/GL libraries on Linux, seen when checking package rgl and some others using it—a workaround is to set environment variable RGL_USE_NULL=true.
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| 1,181.8 | 591,327 |
https://cran-r.c3sl.ufpr.br/doc/manuals/r-devel/R-exts.html
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cran-r.c3sl.ufpr.br
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We understand how important it is to find a dentist whom you not only trust but can also love. Here at Cranston Dental Group, our goal is to be that provider. Our practice has been bringing the best in quality care to patients in Cranston and Warwick, RI for over 40 years. We are proud that patients not only trust us with their own smiles but also with the smiles of those they love most. As a comprehensive provider, we offer services that range from preventive care to emergency tooth extractions and can meet all of your family's oral health needs in one convenient facility. For more on what it's like to visit our practice, we invite you to browse recent reviews from some of our patients.
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5.0
Review from S.P. | Source: Google | Sep 29, 2021
There is no one I would trust with the dental needs of my teeth and my children’s’, other than Cranston dental group and specifically, our hygienist Chontelle. She is accommodating, professional and knowledgeable. She thoroughly explains everything and makes us all feel comfortable, in what is often an intimidating atmosphere. My daughters actually look forward to visiting her and having their teeth cleaned. You won’t find a better practice! More
5.0
Review from T.G. | Source: Google | Sep 28, 2021
Dr. M and the whole staff are terrific!!! More
5.0
Review from L.C. | Source: Google | Sep 24, 2021
Great first time experience. My dental hygienist was very sweet, had great bedside manners. I came in for a regular check up/cleaning, and a consultation. The dentist who did my consultation was also very nice and informed me about my treatment plan. I would recommend them to my friends and family. More
5.0
Review from L.K. | Source: Google | Sep 22, 2021
My appointment was at 11 am for a cleaning. Hygienist (Mary) greeted me right on time. She was very friendly and showed concern for the health of my teeth by asking a series of questions. The cleaning was very thorough and complete. Very pleased with the entire experience. Larry K. More
5.0
Review from A.P. | Source: Google | Sep 10, 2021
You can't find a better team! I am very nervous at the dentist and they know exactly how to make me comfortable. I have alot of work that has been done and needs to be done and I feel so much better! More
5.0
Review from S. | Source: Google | Sep 03, 2021
Very friendly people, and great safety precautions for the pandemic. More
5.0
Review from M.H. | Source: Google | Aug 28, 2021
The staff was friendly and very kind! Hygienist and Dentist were very organized and personable! I highly recommend Cranston Dental Group. More
5.0
Review from B.B. | Source: Google | Aug 25, 2021
This dental practice is THE BEST dental practice one can imagine. Everyone is professional, personal, and incredibly skilled. Our family members have been going there for 15 years. I couldn’t recommend a health care office more highly. More
5.0
Review from S.G. | Source: Google | Aug 20, 2021
Always a nice experience going to see Dr. Mastrostefano and his staff. They are always accommodating with changes and issues. More
5.0
Review from L.T. | Source: Google | Aug 19, 2021
Thorough cleaning from Angie, dental hygienist. I recommend her highly. More
5.0
Review from M.D. | Source: Google | Aug 06, 2021
Great place Chantel is a awesome hygienist More
5.0
Review from B.Y. | Source: Google | Aug 05, 2021
This is the most comfortable dentist office I have ever been to. Dr. M is absolutely fantastic professional. The dental assistants are just as gifted. The front desk staff are highly experienced in the management of insurance issues, and are so friendly and helpful explaining everything. I highly doubt I will ever be as satisfied by another dental office. More
5.0
Review from M.A. | Source: Google | Jul 28, 2021
I'm very happy with Cranston Dental. I have found it difficult to find the perfect dentist. They are caring and absolutely make you feel comfortable. I have various medical issues. I feel safe and recommend them to all. More
5.0
Review from J.T. | Source: Facebook | Apr 28, 2021
Excellent staff and expert professional services More
5.0
Review from M.C. | Source: Facebook | Apr 22, 2021
Professional and high quality of cleanings More
5.0
Review from J.D. | Source: Facebook | Apr 12, 2021
I've always not been a fan of dentists. That's until I started going here. Not only are then close to home, but are the most gentle with my sensitive gums. Each time I come home fro them, my wife can instantly see the difference. Thank you! More
5.0
Review from L.D. | Source: Facebook | Apr 07, 2021
Hygienist very thorough and knowledgable Dr Nelson is outstanding More
5.0
Review from B.R. | Source: Facebook | Mar 18, 2021
Michelle, my dental hygienist is THE very best! More
5.0
Review from M.T. | Source: Facebook | Mar 16, 2021
Great customer service! Highly skilled and knowledgeable Doctors Super patient! Excellent results! More
5.0
Review from S.L. | Source: Facebook | Feb 25, 2021
Talk about finding a dentist that provided me a perfect plan to move forward! I’m so happy I chose them! Friendly staff, Doc was awesome in explaining my route. I wonder why I waiting so long! Thank you! More
5.0
Review from P.D. | Source: Facebook | Jan 28, 2021
Great professional care by great people. More
5.0
Review from M.M. | Source: Facebook | Jan 22, 2021
I would highly recommend Advanced Cosmetic Dentistry. Dr Sing was great! I have a beautiful natural smile now and wish I had gone to ACD years ago!! M. Murphy More
5.0
Review from A.D. | Source: Facebook | Jan 14, 2021
They are very kind, polite, and very knowledgeable More
5.0
Review from S.R. | Source: Facebook | Jan 13, 2021
They are clean friendly and professional More
5.0
Review from M.T. | Source: Facebook | Nov 18, 2020
Great high quality service More
5.0
Review from J.R. | Source: Facebook | Nov 11, 2020
Michele in hygeine and Dr Nelson at the helm! Great dentistry in a mediocre world........ More
5.0
Review from V.R. | Source: Facebook | Nov 10, 2020
Dr Nelson took his time to explain in depth everything, he wasn’t in it for money at all like a previous dentist I had gone to was. Beyond appreciated and grateful I made this appointment More
5.0
Review from V.R. | Source: Facebook | Nov 10, 2020
Dr Nelson took his time to explain in depth everything, he wasn’t in it for money at all like a previous dentist I had gone to was. Beyond appreciated and grateful I made this appointment More
5.0
Review from S.P. | Source: Vitals | Mar 20, 2020
Most Honest Dentist I have met! He gave me a conservative treatment and it worked! The staff is very friendly and seems to enjoy working with the doctor. I was seen for my emergency within 24 hours of calling as a new patient More
5.0
Review from K.K. | Source: Google | Oct 10, 2019
Shout out to DR. Mastrostefano & Dr. Glick. Cannot forget Chontelle and the team. DR. M has been my dentist since I arrived in RI 25 years ago. Excellent service More
5.0
Review from A. | Source: Google | Jul 10, 2019
I couldn’t say enough wonderful things about this practice. Dr M was the most understanding and compassionate dentist I have ever had. He took the time to understand my concerns and make recommendations that set my mind at ease. I am so delighted to find a dental practice that I can feel comfortable and confident in! More
5.0
Review from N.T. | Source: Yelp | Dec 04, 2018
This place is fantastic. EVERYONE is so nice, while being professional and personable at the same time. Dr. M took time to explain everything without me even having to ask. He seems very kind, and you can tell he actually ENJOYS his job. I haven’t been to the dentist in 15 years because of fear and he completely puts me at ease. I hope he never retires. He’s got a patient in me for life! More
5.0
Review from N.T. | Source: Yelp | Dec 04, 2018
This place is fantastic. EVERYONE is so nice, while being professional and personable at the same time. Dr. M took time to explain everything without me even having to ask. He seems very kind, and you can tell he actually ENJOYS his job. I haven't been to the dentist in 15 years because of fear and he completely puts me at ease. I hope he never retires. He's got a patient in me for life! More
5.0
Review from S.G. | Source: Google | Mar 10, 2018
On e word "Phenomenal!" Highly recommend. I've never had such a nice experience at a dentist office. Professional, courteous, and customer-focused Thank you Dr. Mastrostefano and staff. More
5.0
Review from J.P. | Source: Google | Mar 10, 2018
Great experience Best Dentists, staff is so nice and helpful! More
5.0
Review from K.R. | Source: Google | Mar 10, 2018
Im truly pleased with the service I had today. Im very picky about dentists and never thought I'd find the right one. I went through a few of them. My first impression is always the receptionist area this usually makes my decision. I did have to wait alittle longer than expected ,but once I was called to the back I understood why. Dr. Mastrostefano was great very knowledgeable, took his time to listen to my concerns and what I had to say. This is such a plus. You are talking about your teeth after all. I dont mind waiting alittle longer at all . Gets to show you that he takes pride in his work.Very pleased. Thank you! Finally!👍👍 More
5.0
Review from G.M. | Source: Google | Mar 10, 2018
found this dentist on the internet called them up and got a appointment fast. told them what i needed and in 1/2 hour i was done and able to eat. got a second appointment a week after and had teeth pulled and didnt even feel a thing. this dentis is awsome and anyone needing a awsome dentist that has the most awsome practice go see him. i highly recomment this place to all. again thankyou for the great service you provided me at a time i needed.... More
5.0
Review from M.C. | Source: Google | Mar 10, 2018
Wonderful group of people, the dentist here really does his job well. He put in a lot of what I feel was extra work getting my bite just right. More
5.0
Review from M.Q. | Source: Google | Mar 14, 2017
I have been going to Advanced Cosmetic Dentistry for practically my entire life. They go above and beyond anything you could imagine! I love this practice. Dr. Glick and Dr. Mastrostefano are wonderful doctors who truly care about their patients. Their staff is amazing and very kind. The front staff is very kind and helpful, and the hygienists and dental assistants are very kind and gentle. I have an extremely complicated sensitivity in my teeth like no other and a fear of work being done on my teeth, but each time I go in there, they go beyond my expectations to calm my fears and make me as comfortable as possible. They are quick to get me in if there is a problem, and do all they can to help me prevent any problems! I would recommend them to anyone, as they are wonderful! (P.S. I lived in a different state for a couple of years, and I would come back to visit just so they could still be my dentist office, that is how good they are) Love Advanced Cosmetic Dentistry!!!! More
5.0
Review from A.Q. | Source: Google | Mar 14, 2017
I have been a patient at this office since I got married 11 years ago, and in that time I have received awesome care. I have very weak teeth, so I have had to be in there for multiple problems, and each time I go in there I am treated with respect and the utmost care. They are quick to get me in when I have a problem, and do all they can to help me prevent problems. Thank you Dr. Glick and Dr. Mastrostefano for all your help, and thank you to your staff for being amazing! More
5.0
Review from B.D. | Source: Google | Mar 14, 2017
I've been a patient of Dr. Glick and Dr. Mastrostefano of Advanced Cosmetic Dentistry for several years. I cannot rave more about Anna at the front desk and both doctors for the professionalism that I have encountered. I highly recommend them for their exceptional quality and care! More
5.0
Review from D.T. | Source: Google | Mar 14, 2017
I have been going to Advanced Cosmetic Dentistry for over 20 years. The staff is amazing. I had a fear of the dentist, then a friend referred me to Dr. Mark Fielding in West Warwick, who changed my fear into a non traumatic experience. Then most of his patients were transferred to Advanced Cosmetic Dentistry, (after learning of his passing) This is where I first met Anna at that West Warwick location, where she was busy transferring all those patients. This wonderful woman, eased my mind right away, (because my stress of the dentist returned right away) I have had many procedures done over the past 20 years and I would highly recommend this office to all. I have had issues and they were always there to help and get me in. Dr. M, Chontelle, and the entire staff are wonderful, knowledgeable and professional. As for Anna, if she ever left this office, I would too. (Mr. Ramos, Anna also has an accent, and I don't know the issue, but the Anna I know never ever made me feel like and "idiot".) She always works to help the patients the best to her ability. I would highly recommend Advanced Cosmetic Dentistry!!! More
5.0
Review from L.B. | Source: Google | Mar 14, 2017
I have been going here for a long time.. Staff is very professional and knowledgeable.. I have had cleanings, dental surgery, fillings, crowns and have never had a problem. Everything has been top notch.. I can safely recommend this group to anyone. !! More
5.0
Review from J.L. | Source: Google | Mar 14, 2017
My husband recently saw Dr Glick for gum surgery. He was very professional and did an outstanding job. He made sure we understand everything before the appointment and even gave us his cell phone if we had any problems. The staff there is also very helpful especially at the front desk. Anna helped us and she was awesome! I would highly recommend this office to any one of my friends or family members. More
5.0
Review from L.Z. | Source: Google | Jan 09, 2017
I have been going here for a long time.. Staff is very professional and knowledgeable.. I have had cleanings, dental surgery, fillings, crowns and have never had a problem. Everything has been top notch.. I can safely recommend this group to anyone. !! More
5.0
Review from Anonymous | Source: Vitals | Nov 10, 2016
Dr. M is great - I'm super anxious at the dentist. He explains everything very well and makes you feel comfortable. My lifelong dentist retired so after searching and searching, I can gladly say I've found my new lifelong dentist. More
5.0
Review from K.F. | Source: Facebook | Sep 08, 2016
I recently went to Dr.Glick,up to this appointment I feared dentist,I received the best treatment I have ever experienced at a dentist He and his assistant Shelly made me comfortable and I did not experience any pain from my procedure and he took the time to make me feel comfortable.I am 63 years old and wish I had that experience younger.Dr Glick is the best Dentist I have been to and will continue to do so.Thank you very much to you and your staff for making me comfortable and taking the time to care More
5.0
Review from L.Z. | Source: Google | Jul 25, 2016
I have been going here for a long time.. Staff is very professional and knowledgeable.. I have had cleanings, dental surgery, fillings, crowns and have never had a problem. Everything has been top notch.. I can safely recommend this group to anyone. !! More
5.0
Review from E.T. | Source: Google | Mar 28, 2016
Drs. Glick and Mastrostefano are incredibly knowledgeable, professional, and skilled. They take care with their patients, and the results of their work is phenomenal! My entire family--husband, myself, and four children (two adult children, two teenagers) go to this dental practice and are all very satisfied. Anna at the front desk is so delightful and helpful as well as all of the staff there. Highly recommend this dental office!! E. Thompson, North Kingstown, R.I. More
5.0
Review from E.T. | Source: Google | Mar 14, 2016
Drs. Glick and Mastrostefano are incredibly knowledgeable, professional, and skilled. They take care with their patients, and the results of their work is phenomenal! My entire family--husband, myself, and four children (two adult children, two teenagers) go to this dental practice and are all very satisfied.Anna at the front desk is so delightful and helpful as well as all of the staff there.Highly recommend this dental office!!E. Thompson, North Kingstown, R.I. More
5.0
Review from Anonymous | Source: Vitals | Mar 12, 2015
Rare Honesty - Most Honest Dentist I have met!He gave me a conservative treatment and it worked! The staff is very friendly and seems to enjoy working with the doctor. I was seen for my emergency within 24 hours of calling as a new patient More
5.0
Review from C.W. | Source: Yelp | Apr 14, 2014
Hmm how has no one written a review of these guys yet? How have I not written one yet? Anyway, I’ve heard that everyone is supposed to hate the dentist. But I LOVE the dentist. I’ve always wanted to move down south to be with family and I gotta say the one thing slowing me down is the thought of leaving these guys for a new dental office.Mastrostefano is a nice guy and very funny and as careful as he can be not to hurt you even on the worst fillings. They remember everything important about you and make you smile even when you’ve got an open mouth full of equipment. Most importantly, they are honest and won’t fill a cavity just to squeeze money out of you if it’s a beginner and doesn’t need to be filled. Even though I moved an hour away I still come here because there’s no one better. Today I came in for a cleaning appointment that was apparently scheduled for another day (my fault) and they squeezed me in anyway! Yelp is kind of a weird place to look for a dentist but I hope if you’re reading this you give them a call. More
5.0
Review from C.W. | Source: Yelp | Apr 14, 2014
Hmm how has no one written a review of these guys yet? How have I not written one yet? Anyway, I've heard that everyone is supposed to hate the dentist. But I LOVE the dentist. I've always wanted to move down south to be with family and I gotta say the one thing slowing me down is the thought of leaving these guys for a new dental office. Mastrostefano is a nice guy and very funny and as careful as he can be not to hurt you even on the worst fillings. They remember everything important about you and make you smile even when you've got an open mouth full of equipment. Most importantly, they are honest and won't fill a cavity just to squeeze money out of you if it's a beginner and doesn't need to be filled. Even though I moved an hour away I still come here because there's no one better. Today I came in for a cleaning appointment that was apparently scheduled for another day (my fault) and they squeezed me in anyway! Yelp is kind of a weird place to look for a dentist but I hope if you're reading this you give them a call. More
5.0
Review from V.W. | Source: Facebook | Jan 09, 2014
Love these people! Great staff and worlds best hygienist Chontelle!!! More
5.0
Review from Anonymous | Source: Vitals | Jun 21, 2012
Five Stars - awesome dentist, awesome staff, and great surroundings. I was very satisfied and would higly recommend and have to others. More
*Individual results are not guaranteed and may vary from person to person. Images may contain models.
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" More 5.0 Review from V.R. | Source: Facebook | Nov 10, 2020 Dr Nelson took his time to explain in depth everything, he wasn't in it for money at all like a previous dentist I had gone to was. Beyond appreciated and grateful I made this appointment More 5.0 Review from V.R. | Source: Facebook | Nov 10, 2020 Dr Nelson took his time to explain in depth everything, he wasn't in it for money at all like a previous dentist I had gone to was. Beyond appreciated and grateful I made this appointment More 5.0 Review from S.P. | Source: Vitals | Mar 20, 2020 Most Honest Dentist I have met! He gave me a conservative treatment and it worked! The staff is very friendly and seems to enjoy working with the doctor. I was seen for my emergency within 24 hours of calling as a new patient More 5.0 Review from K.K. | Source: Google | Oct 10, 2019 Shout out to DR. Mastrostefano & Dr. Glick. Cannot forget Chontelle and the team. DR. M has been my dentist since I arrived in RI 25 years ago. Excellent service More 5.0 Review from A. | Source: Google | Jul 10, 2019 I couldn't say enough wonderful things about this practice. Dr M was the most understanding and compassionate dentist I have ever had. He took the time to understand my concerns and make recommendations that set my mind at ease. I am so delighted to find a dental practice that I can feel comfortable and confident in! More 5.0 Review from N.T. | Source: Yelp | Dec 04, 2018 This place is fantastic. EVERYONE is so nice, while being professional and personable at the same time. Dr. M took time to explain everything without me even having to ask. He seems very kind, and you can tell he actually ENJOYS his job. I haven't been to the dentist in 15 years because of fear and he completely puts me at ease. I hope he never retires. He's got a patient in me for life! More 5.0 Review from N.T. | Source: Yelp | Dec 04, 2018 This place is fantastic. EVERYONE is so nice, while being professional and personable at the same time. Dr. M took time to explain everything without me even having to ask. He seems very kind, and you can tell he actually ENJOYS his job. I haven't been to the dentist in 15 years because of fear and he completely puts me at ease. I hope he never retires. He's got a patient in me for life! More 5.0 Review from S.G. | Source: Google | Mar 10, 2018 On e word "Phenomenal!" Highly recommend. I've never had such a nice experience at a dentist office. Professional, courteous, and customer-focused Thank you Dr. Mastrostefano and staff. More 5.0 Review from J.P. | Source: Google | Mar 10, 2018 Great experience Best Dentists, staff is so nice and helpful! More 5.0 Review from K.R. | Source: Google | Mar 10, 2018 Im truly pleased with the service I had today. Im very picky about dentists and never thought I'd find the right one. I went through a few of them. My first impression is always the receptionist area this usually makes my decision. I did have to wait alittle longer than expected ,but once I was called to the back I understood why. Dr. Mastrostefano was great very knowledgeable, took his time to listen to my concerns and what I had to say. This is such a plus. You are talking about your teeth after all. I dont mind waiting alittle longer at all . Gets to show you that he takes pride in his work.Very pleased. Thank you! Finally!👍👍 More 5.0 Review from G.M. | Source: Google | Mar 10, 2018 found this dentist on the internet called them up and got a appointment fast. told them what i needed and in 1/2 hour i was done and able to eat. got a second appointment a week after and had teeth pulled and didnt even feel a thing. this dentis is awsome and anyone needing a awsome dentist that has the most awsome practice go see him. i highly recomment this place to all. again thankyou for the great service you provided me at a time i needed.... More 5.0 Review from M.C. | Source: Google | Mar 10, 2018 Wonderful group of people, the dentist here really does his job well. He put in a lot of what I feel was extra work getting my bite just right. More 5.0 Review from M.Q. | Source: Google | Mar 14, 2017 I have been going to Advanced Cosmetic Dentistry for practically my entire life. They go above and beyond anything you could imagine! I love this practice. Dr. Glick and Dr. Mastrostefano are wonderful doctors who truly care about their patients. Their staff is amazing and very kind. The front staff is very kind and helpful, and the hygienists and dental assistants are very kind and gentle. I have an extremely complicated sensitivity in my teeth like no other and a fear of work being done on my teeth, but each time I go in there, they go beyond my expectations to calm my fears and make me as comfortable as possible. They are quick to get me in if there is a problem, and do all they can to help me prevent any problems! I would recommend them to anyone, as they are wonderful! (P.S. I lived in a different state for a couple of years, and I would come back to visit just so they could still be my dentist office, that is how good they are) Love Advanced Cosmetic Dentistry!!!! More 5.0 Review from A.Q. | Source: Google | Mar 14, 2017 I have been a patient at this office since I got married 11 years ago, and in that time I have received awesome care. I have very weak teeth, so I have had to be in there for multiple problems, and each time I go in there I am treated with respect and the utmost care. They are quick to get me in when I have a problem, and do all they can to help me prevent problems. Thank you Dr. Glick and Dr. Mastrostefano for all your help, and thank you to your staff for being amazing! More 5.0 Review from B.D. | Source: Google | Mar 14, 2017 I've been a patient of Dr. Glick and Dr. Mastrostefano of Advanced Cosmetic Dentistry for several years. I cannot rave more about Anna at the front desk and both doctors for the professionalism that I have encountered. I highly recommend them for their exceptional quality and care! More 5.0 Review from D.T. | Source: Google | Mar 14, 2017 I have been going to Advanced Cosmetic Dentistry for over 20 years. The staff is amazing. I had a fear of the dentist, then a friend referred me to Dr. Mark Fielding in West Warwick, who changed my fear into a non traumatic experience. Then most of his patients were transferred to Advanced Cosmetic Dentistry, (after learning of his passing) This is where I first met Anna at that West Warwick location, where she was busy transferring all those patients. This wonderful woman, eased my mind right away, (because my stress of the dentist returned right away) I have had many procedures done over the past 20 years and I would highly recommend this office to all. I have had issues and they were always there to help and get me in. Dr. M, Chontelle, and the entire staff are wonderful, knowledgeable and professional. As for Anna, if she ever left this office, I would too. (Mr. Ramos, Anna also has an accent, and I don't know the issue, but the Anna I know never ever made me feel like and "idiot".) She always works to help the patients the best to her ability. I would highly recommend Advanced Cosmetic Dentistry!!! More 5.0 Review from L.B. | Source: Google | Mar 14, 2017 I have been going here for a long time.. Staff is very professional and knowledgeable.. I have had cleanings, dental surgery, fillings, crowns and have never had a problem. Everything has been top notch.. I can safely recommend this group to anyone. !! More 5.0 Review from J.L. | Source: Google | Mar 14, 2017 My husband recently saw Dr Glick for gum surgery. He was very professional and did an outstanding job. He made sure we understand everything before the appointment and even gave us his cell phone if we had any problems. The staff there is also very helpful especially at the front desk. Anna helped us and she was awesome! I would highly recommend this office to any one of my friends or family members. More 5.0 Review from L.Z. | Source: Google | Jan 09, 2017 I have been going here for a long time.. Staff is very professional and knowledgeable.. I have had cleanings, dental surgery, fillings, crowns and have never had a problem. Everything has been top notch.. I can safely recommend this group to anyone. !! More 5.0 Review from Anonymous | Source: Vitals | Nov 10, 2016 Dr. M is great - I'm super anxious at the dentist. He explains everything very well and makes you feel comfortable. My lifelong dentist retired so after searching and searching, I can gladly say I've found my new lifelong dentist. More 5.0 Review from ",
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This is a prime opportunity to enter the prestigious inner south property market with this original condition three bedroom, one bathroom home.Renovate or redevelop on the spacious 764 sqm (RZ1- single residential) parcel of land, which offers a long northern boundary and minimal impact on the building envelope from easements. A new two storey residence would allow for views to Red Hill in the rear and views toward the Jerrabomberra wetlands in the foreground. Super conveniently located to a plethora of Canberra’s top schools, recreational parks , nature reserve and the trendy Griffith Cafe and shopping precinct, this is the opportunity you have been waiting for. Inspect immediately as this may very well be sold prior to auction. The comparable sales are below.
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Once you have a beautifully landscaped Hamptons property, you undoubtedly want to show it off. Whether you have a pool, patio, garden, or water feature, what better way to emphasize it than with landscape lighting? It adds curb appeal and increases property value. Additionally, good landscape light helps make a property safer. Creative Design Landscapes will design, install, and/or repair your Hamptons landscaping lighting as needed.
The first step is to design a Hamptons landscape lighting system that suits your needs. We will meet with you and discuss your specific requirements, then create a design that you will have the opportunity to review before we finalize it and begin installation. Our technicians will work with you to design a system that fits seamlessly into your existing landscaping.
Installation involves putting in place the system, using durable, high quality materials that will not only complement your landscaping but also stand the test of time. You can be assured that our installation won’t interfere with your existing landscaping and will be so low profile that only the parts of the landscape lighting that you specifically want to be visible will be noticeable by visitors. After installation is complete, we’ll walk with you through the breadth of the system, adjusting lamp beams as needed so they’re targeting the areas of your property you want to highlight.
Finally, once your Hamptons landscape lighting is in place, our crews will maintain it for you so it always provides peak performance over the years. This will include a yearly review of wiring and transformers and may include lightly trimming plants if they’re infringing upon the lighting’s full effect. Again, we will never infringe upon the existing landscaping and will always consult with you before taking such steps. If repairs are necessary, including replacing wiring, transformers, bulbs, or lamps, we will quickly take care of them.
Creative Design Landscapes has the most experienced, professional Hamptons landscape lighting team on the market. Don’t hesitate to call us with your questions and to schedule and appointment to discuss specific project needs.
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Creativepool® and Creative pool® are both the trademarks of Creativepool Ltd. All member work copyright of respective owners, otherwise © Creativepool Ltd 0.16s
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A healthy mind is a productive mind. And to nourish the mind, one has to nourish the body. If you think what you put into your body has no effect on your creativity, you’re dead wrong. The brain is an amazing organ. It is able to perceive things that none of our other organs have even encountered. Where the basic senses have a constraint to certain extents, the brain has full access. For as long as you keep eating what is good for your brain, you won’t have to worry about having to wait for some creative stimuli.
You don’t have to be extra careful about choosing what to eat. You also need not worry about having to avoid certain food because it might affect your way of thinking. Just remember that the reason why you are altering your diet is because you want to think clearer. You are not trying to prevent anything, you are only trying to boost something. Feeding your creativity is not limited to finding inspiration. It can, and advisably, be taken literally.
Starving individuals, that’s what most artists are commonly stereotyped as. But that couldn’t be at all true, at least not completely. For how can one be in the best condition for creating when the mind is incapable of producing sensible art? You want a concrete example? There’s Pablo Picasso. He’s one of my favorite painters (the Guernica painting used as the featured image of my previous post may have given that away). He, for one, understands the importance of eating healthy to maintain his creative drive. Well, it was actually his physician who advised him to make sure fish and spinach are always included in his diet in order to enhance his abstract-thinking powers. Although one might argue that he is intrinsically talented and did not rely solely on the food he ate for his creativity, you can’t discredit the fact that the reason for his reaching of the maximum potential may be caused by proper care of the brain.
So what exactly are these creativity enhancers? These are foods that may or may not contain tyrosine, but are good sources of energy . Now, why did I especially mention tyrosine if you may choose to do without it anyway? It is a biochemical precursor of dopamine(the happy hormone) that promotes deep thinking. So you see, its presence is not necessary, but its benefits are noteworthy.
Below are some of the foods that are good creativity boosters:
Anything that is unprocessed – Fuel your brain, first and foremost, by going natural. Studies show that foods which undergo chemical processing lessens the nutrients you are supposed to get from their unprocessed counterparts.
Coldwater fish – Why does it have to be fish from cold regions? It’s because they are highly rich in Omega 3 which is the best brain food. Ever.
Green leafy vegetables – Not only do they help you maintain focus, they are excellent memory enhancers. They slow down the rate of cognitive decline which is an inevitable part of ageing. You’ll never have to worry about losing a bright idea. Plus, they are good antioxidants.
Whole grains – They keep the traffic of glucose energy moderate, so your mental energy remains steady. They are best taken during breakfast because they control the release of energy throughout the day, ensuring consistency in your drive to complete your creation.
Fresh fruits – It’s not only apples that keep doctors away. Any fruit can give you that perk, as long as it’s fresh. Because your brain can’t store glucose, it relies on the bloodstream to provide it with a steady flow. Luckily, fresh fruits are there to the rescue.
Nuts and seeds – Nuts have always been a reputable source food-induced intelligence. The truth is, they are just as beneficial for convergent thinking as they are for divergent thinking. In fact, regular consumers of nuts and seeds get twice, even thrice, the flow of creative juices.
A harmless amount of junk – Feel the need to refresh the page? Go ahead. I did say junk. A little indulgence never hurt, so have some of your favorites. It’s absolutely fine to eat YOLO-style every once in a while. And most junk foods do help release endorphins to your brain, making you feel positive. Just make sure you know when enough is enough, or you may put your creativity limit at risk. After all, a true artist knows when to break the rules from time to time.
Of course, you have the option to not stick to a certain diet, or only eat those mentioned above from now on. Your skills or talents are pretty much safe without following these suggestions. Again, they’re only meant to help boost what you already have. Personally though, I encourage you to think of Picasso when you consider it; painter, sculptor, printmaker, ceramicist, stage designer, poet and playwright. He isn’t even just mediocre at being any of those. How about that?
“One cannot think well, love well, sleep well, if one has not dined well.” – Virginia Woolf
P.S. I know I quoted Virginia Woolf instead of Pablo Picasso, but I feel this is more suited to close the article better. Too much Picasso for one day is art gluttony.
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Individual specimen entries are published in the sample database supplied with The Compleat Botanica for species or varieties of this supra-generic taxon.
For a description of the methodology followed in establishing this hierarchy see the note Nomenclature used in The Compleat Botanica.
For an index to other topics see
Wild & cultivated plants of the world
Last reviewed November 01, 2004
Order your copy here
Semantax basics BLUEPHRASE started symbolic endophrasing Blue file format subject matter without typography BLUEPHRASE Basics [bluephrase · basics] In this short introductory note, you will learn how to author documents with many of the commonly used typographic techniques found in word processors, by using BLUEPHRASE symbolic endophrasing. There are only a few basic rules to learn.
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Mumbai (Maharastra) [India], March 31 (ANI): After being named as the Delhi Capitals skipper for the upcoming Indian Premier League (IPL), wicketkeeper-batsman Rishabh Pant on Wednesday said that he would try to give his 100 per cent in each game to hopefully end the franchise’s title drought.
Pant uploaded a 10-second video on Twitter in which he can be heard saying: “Thank you for giving me this opportunity, I will try to give my 100 per cent each and every day. Hopefully, we will cross the line this year.”
He captioned the post as: “Humbled at the opportunity to lead the franchise I’ve been part of for the past few years! Thank you @RickyPonting, the coaching staff, management, my teammates and the fans for your faith in me. @DelhiCapitals: Let’s do this! #IPL2021.”
Earlier on Wednesday, Delhi Capitals coach Ricky Ponting said that Pant deserves to lead the franchise looking at his recent performances. Ponting’s remarks came as Pant was announced as Delhi Capitals skipper on Tuesday. Shreyas Iyer has been ruled out of IPL 2021 due to a shoulder injury.
“Unfortunate that Shreyas will miss the tournament, but looking forward to seeing @RishabhPant17 grab his opportunity. It’s well deserved for his recent performances and he’s coming in with a lot of confidence. I’m convinced captaincy will make him an even better player,” tweeted Ponting.
Off late, Pant has been in remarkable form and he has given some match-winning performances in the series against Australia and England. Of all his performances, the ones in Brisbane (fourth Test against Australia) and Ahmedabad (fourth Test against England) stand out.
On his new role, Pant in a statement said, “Delhi is where I grew up, and where my IPL journey began six years ago. To lead this team one day is a dream I’ve always harboured. And today, as that dream comes true, I feel humbled. I am truly grateful, especially to our team owners, who considered me capable enough for this role. With an amazing coaching staff, and a plethora of accomplished seniors around me, I can’t wait to give my absolute best for Delhi Capitals.”
Delhi Capitals will begin their IPL 2021 campaign against Chennai Super Kings (CSK) at the Wankhede Stadium in Mumbai on April 10. (ANI)
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Test Debut: South Africa v New Zealand at Kingsmead, Durban - 26, 27, 28, 29, 30 December 1994 (five-day match)
Last Test: South Africa v Pakistan at New Wanderers Stadium, Johannesburg - 19, 20, 21, 22, 23 January 1995 (five-day match)
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The Cri du Chat Support Group of Australia supports those affected by Cri du Chat Syndrome and other anomalies of Chromosome 5.
You may have arrived at this page because you are the parent, family member, or friend of a person affected by Cri du Chat Syndrome or another anomaly of Chromosome 5. We hope that this site will provide you with information and encouragement. The family stories shared by group members are encouraging to read.
Read more
We are grateful to JMA for their generous support with initial installation and design of this website.
About
Learn about Cri du Chat Support Group of Australia Inc.
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Access information about Cri du Chat syndrome.
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Contact us by web form, email, Facebook, or post.
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How can you help?
The Cri du Chat Support Group of Australia is funded by donations. Donations of $2.00 and over are tax deductible.
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Child endangerment or endangering the welfare of a child is defined as placing a child in a situation that would put their health or safety at risk. Actions which amount to child endangerment include having a child in the car and driving while under the influence of drugs or alcohol, leaving a child alone in a hot car, or failing to properly secure a child in a car. Having unsecured firearms or doing or selling drugs around a child are also activities that amount to child endangerment.
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How Do You Prove Child Endangerment?
In order to be prosecuted for child endangerment, the law does not require a showing that the child suffered actual harm. It is enough to show that the behavior of the defendant was such that the child was at risk of being harmed physically, mentally, or emotionally. In addition, it is not necessary to show intent. In cases where the child did suffer actual harm, the defendant may be charged with child endangerment as well as other related crimes.
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What Are the Penalties for Endangering the Welfare of a Child?
In New York there are different levels of child endangerment charges, ranging from misdemeanors to serious felonies. The penalty will depend on whether the conviction was for a misdemeanor or felony. One factor that will determine the level of the charge is whether the child was actually injured. Penalties may include probation, fines, or jail. If a parent or guardian is charged, the child may be removed from that person’s custody.
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Still Have Questions? Contact the Law Offices of Stephen Bilkis & Associates for Help
The attorneys at the Law Offices of Stephen Bilkis & Associates have years of experience successfully defending clients in New York criminal courts who have been charged with drug crimes. Contact us at 800.696.9529 to schedule a free, no obligation consultation regarding your case. We serve those accused of drug crimes in the following locations: the Bronx, Brooklyn, Long Island, Manhattan, Nassau County, Queens, Staten Island, Suffolk County and Westchester County.
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I was in need of legal assistance for a very sensitive matter for a family member. I contacted the law offices of Stephen Bilkis & Associates, PLLC and was met with staff whose demeanor was supportive, compassionate and professional. The lawyer handling our case had many years of experience and treated us as if we were his own family. Our experience was so good, and we became so close to all of the staff and all of the attorneys who assisted us, that we consider them our extended family and continue to send them our home baked gifts for the holidays. P.A.K.
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https://criminaldefense.1800nynylaw.com/new-york-endangering-the-welfare-of-a-child-frequently-asked-que.html
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criminaldefense.1800nynylaw.com
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Posted in Island Life, Lichens, Natural World, Wordless WednesdayTagged lichen hunting, Lichens, nature photography
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Stelara allergy?
Thread starter Pilgrim
Start date Sep 21, 2021
Pilgrim
Sep 21, 2021
#1
About a week after her last monthly injection, H broke out in an itchy rash on her torso front and back. After the pediatrician was unable to diagnose it, I made a few phone calls and it *may* be an allergic response.
As a precaution we're having her next dose in ER this week. She is still itchy, but it is milder now. Hopefully, she has no major reaction.
I am wondering if anyone has had this type of reaction? Did you have to drop your med? I don't want to lose Stelara right away. It's only been 5 months or so.
my little penguin
Moderator
Staff member
Sep 22, 2021
#2
Ds had a delayed rash on his torso from remicade
And his face and … from the start
He eventually had a reaction during the infusion - after 8 months -not once but twice even with solumedrol on board
So they dropped the remicade
anakinra- large welts/hives /bruising would show up on opposite limbs /arms/legs (previous injection sites. )-we gave Benadryl prior to injections
But had anaphylaxis later so it was stopped .
humira only had hand size welts at the injection site if we injected in the back of the arm
No reaction. In the thighs
So kept that one for five years
Was the reaction delayed ?
More than two hours after the injection ?
Has she seen a dermatologist or allergist ?
Dermatologist can help identify rash better and allergist help really determine if it’s Stelara versus a plethora of other things …
My kiddo has broken out in rashes from other things over the years
Allergist and dermatologist are key
Did they give Benadryl and /or steriods to calm the reaction ?
We try Benadryl first for my kiddo per his allergist and if it’s wide spread steriods
Pilgrim
Sep 22, 2021
#3
The reaction was delayed. We are 5-6 months in, and reaction was a week after injection. It is just quieting down now....one day before the next injection.
I treated her myself with benadryl not knowing what was going on. Eventually saw a pediatrician, and have a scheduled appointment for a pediatric dermatologist on Monday.
I feel like we'll know if it's Stelara after this injection if the rash builds up again.
Good to read your experience. Hope as a parent I never have a child in anaphylaxis.
my little penguin
Moderator
Staff member
Sep 22, 2021
#4
Did the rash disappear with Benadryl then come back or no response ?
That is important info for the derm
Ds had horrid looking “hives “ on his arms
Thought he developed a new allergy
Double Zyrtec/Benadryl did nothing
hives do not stay in the same location for more than 24 hours
They can disappear and reappear in a new location but they don’t stay on the same spot
in my kiddos case it was Sweets syndrome not horrid. hives …
anaphylaxis is rare for most
My kiddo had it early on at age 6 to food
So his body tends to go there and has reacted to things (drugs,food ,bees etc..) since he was an infant so a different case
so the rash lasted 3 weeks ????
I hope the dermatologist has answers
We have had better luck with Adult dermatologists than pediatric ones
Simply because the majority of pediatric dermatologist known eczema but have little to no experience with kids on biologics
We saw 3 pediatric dermatologist who were useless
It wasn’t until he saw an adult dermatologist (had a pediatric training as well ) who knew biologics /autoimmune disease did he get evaluated properly
my little penguin
Moderator
Staff member
Sep 22, 2021
#5
Wanted to add dermatologist did not care if Ds had a torso rash from remicade
They only cared if it was turning systemic reaction affecting mouth:throat /breathing etc…,
Pilgrim
Sep 22, 2021
#6
I honestly would ignore the torso rash if it wasn't so itchy- did not respond to the benadryl- so her life was miserable for a few of those weeks.
Ignore it in the sense that Stelara wouldn't be a question.
These are more like small red pinpoint dots spread all over.
my little penguin
Moderator
Staff member
Sep 22, 2021
#7
Sounds like Ds drug eruption rash on his torso from remicade
Not ignore it per say but -dermatologist treats the rash but keeps the patient on the drug if possible as long as possible
Sometimes it is not possible
And reactions get Worse
Sorry
Ds had drug eruption to another pill form drug
Made his skin so itchy
Thankfully the drug can be easily avoided
Reactions: Pilgrim
my little penguin
Moderator
Staff member
Sep 22, 2021
#8
https://www.contemporarypediatrics.com/view/drug-eruptions-benignand-life-threatening
Reactions: Pilgrim
C
crohnsinct
Well-known member
Sep 23, 2021
#9
Hmmm. I am glad you are proceeding cautiously.
I would want to see a derm also.
I by no means am saying this is NOT an allergic reaction BUT wondering if it might be a paradoxical response to Stelara and psoriasis. It sounds a lot like how O andT’s psoriasis started. A bit after Infusion and then as drug wore off rash got better. I would never assume this is it and would still operate as it is a potential allergic reaction but wouldn’t be surprised if this is what it is in the end.
Reactions: Lisa and Pilgrim
Pilgrim
Sep 24, 2021
#10
Ugh. She doesn't need psoriasis! Her injection has been delayed into next week due to a shipping issue, then no room at the hospital for her until next week. So the derm will have to go by pictures and a description.
Reactions: pdx and my little penguin
C
crohnsinct
Well-known member
Sep 24, 2021
#11
No, no, no. No delays! This is unacceptable! We are trying to get this kid back under control. Do they know who they are dealing with?!
Pilgrim
Sep 24, 2021
#12
I have to take occasional breaks from being "that mom". Keeps them guessing. Of course, if it gives her a few more days not miserable (itching is minimal now) I do go a little soft.
Maya142
Moderator
Staff member
Sep 24, 2021
#13
Speaking of psoriasis, have you ever looked at a picture of guttate psoriasis? It's usually found on the torso. But Stelara is a psoriasis drug and I haven't ever heard of paradoxical psoriasis with it - just with anti-TNFs mostly. @crohnsinct do you know if other biologics like Entyvio and Stelara can cause paradoxical psoriasis?
My kiddo stopped IL-17 inhibitors after 3 years - which do not work for IBD but are really good for psoriasis like Stelara (and AS, obviously). She promptly got a rash on the back of her hands that would go away after Cimzia and come back within 10 days before the next shot. Rheumy thought it was eczema, despite the fact that it would appear like clockwork.
We never got to see a dermatologist - when her rheumatologist set it up, she had been put back on an IL-17 inhibitor with Cimzia because her joints were a disaster. The rash went away with 1 shot and did not return and so now her rheumatologist thinks it was psoriasis.
In her case, it wouldn't be paradoxical - it just came about or surfaced when we removed a drug that treated psoriasis well.
Reactions: Lisa
my little penguin
Moderator
Staff member
Sep 27, 2021
#14
Good luck at the appt today !!
Pilgrim
Sep 30, 2021
#15
The appointment was good. The dermatologist wasn't convinced from our photo that the rash was allergic. She gave us a prescription though, to try if it does return. A steroidal cream to control the rash if possible- to help H be able to keep Stelara. I love that she has experience with these drugs (with her psoriasis patients) and knows how relatively safe Stelara is and how important it is not to burn through these drugs.
She did think that H's skin issues she dealt with during her last year on Humira (and that 80mg weekly dose) were very likely psoriasis caused by Humira. Those skin issues have reduced dramatically since H's med change, thankfully.
Her Stelara dose was Tuesday, no problems, and now we wait to see if the rash returns. So far, so good.
Reactions: Lisa, Jo-mom and crohnsinct
Pilgrim
Sep 30, 2021
#16
@Maya142 The dermatologist also never heard of a case of paradoxical psoriasis connected to Stelara - only the Anti-TNF drugs.
my little penguin
Moderator
Staff member
Oct 1, 2021
#17
Sometimes biologics “uncover” /bring to the surface -psoriasis or other autoimmune diseases a person may have developed anyways only faster
So even after you drop the biologics it’s still there
Or shows up after the second biologic simply because the first was in a sense treating it without the person knowing they had it .
They are still learning per my kiddos old derm
So not necessarily paradoxical
Just glad they don’t think it’s allergic drug rash
That’s great
Hope the steriod creams help
Reactions: Lisa
Maya142
Moderator
Staff member
Oct 3, 2021
#18
So glad that it doesn't seem to be Stelara. And I hope the rash hasn't reappeared since her recent dose!
Pilgrim
Oct 4, 2021
#19
Not yet! I'll breathe easy after another day. It was one week after dosing last time. Still have a day to go.
Reactions: my little penguin
my little penguin
Moderator
Staff member
Oct 4, 2021
#20
Fingers crossed
my little penguin
Moderator
Staff member
Oct 25, 2021
#21
@Pilgrim
Just checking in to see how things went after the Stelara injection ?
Any more rashes ?
Pilgrim
Oct 25, 2021
#22
@my little penguin
So we did ending up getting the itchy rash on day 10. First one had been 7 days after injection. We also just did an injection where the rash arrived day 4 after injection (yesterday). It isn't getting more severe, maybe a little less this time, although it came sooner.
The derm had mentioned that sometimes this kind of thing can happen and can be managed with steroid cream and antihistamines. So, this is what we're doing. H felt like she could deal with it in order to continue with Stelara.
Pilgrim
Oct 25, 2021
#23
So to be more clear: 1st reaction - day 7, widespread and lasted 3 weeks.
2nd reaction: day 10 not as bad and lasted a few weeks.
3rd reaction: day 4 and so far not too bad. Worse after showers and sweating.
We had a screwed up injection schedule though where second month was several days late and third month was a day early. She is on a 4 week dosing.
my little penguin
Moderator
Staff member
Oct 25, 2021
#24
Is she on daily Zyrtec ?
Might be worth the ask
My kiddo has taken Zyrtec daily and added Benadryl one hour prior to injecting anakinra (he got welts /hives ) -anakinra has daily injections
He did have anaphylaxis but that was after we stopped the med due to inpatient iv steriods so he built antibodies quickly.
fwiw as a toddler due to severe allergies he was on
Claritin in the am , Zyrtec in the evening and Benadryl during the same day for break through hives .
he was only 2 and 3 at the time so they would not do allergy shots till age 4 .
Definitely ask your pcp /allergist
Allergist tend to me aggressive with meds to calm things down faster .
Calming creams like cerave sometimes help irritated skin after showers
Reactions: Pilgrim
Maya142
Moderator
Staff member
Oct 25, 2021
#25
How is she doing now, besides the rash? Is Stelara working for her?
I’m glad the rash has become more manageable. My daughter has taken Benadryl before several biologics that caused injection site reactions. One of them is actually is approved for psoriasis like Stelara and ironically, she had the worst injection site reactions with that - she’d get a softball sized (or even larger sometimes) welt, that was raised, red, hot to touch and extremely itchy. After a couple shots of trying to deal with it with hydrocortisone cream, M finally emailed her rheumatologist a picture and was immediately told to take Benadryl before the shot and was also given a steroid cream for the itchiness. The Benadryl worked like magic - suddenly the welt that formed was an inch or 1.5 inches wide and not as red or itchy. It also started going away much faster - roughly 1.5-2 days vs 4 before. It was a nice, easy fix (and there is nothing I love more than an easy fix).
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[media id=3012] [media id=3013] (h/t BillW. for videos) There's some doubt as to whether Einstein ever actually said that if honey bees died out, m
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By Nicole Belle — October 28, 2007
Download Download (h/t BillW. for videos)
There's some doubt as to whether Einstein ever actually said that if honey bees died out, mankind only had four years left to live. But no matter the authorship, the truth is that we are very dependent upon bees for our food product and agricultural industry. And bees are dying, at a dangerously fast pace. By some estimates, a full 1/3 of the bee population has died off, in a phenomenon known as "Colony Collapse Disorder":
(A) mystery malady, dubbed "Colony Collapse Disorder," is sweeping through the apiaries, leaving many hives almost completely devoid of adult bees, which appear to abandon their hives and disappear. Apiculturists are looking at a number of potential culprits, from bad weather to bad corn syrup to genetically modified corn to pesticides to miticides, and many suspect the problem is compounded by the presence of the varroa mite, which weakens colonies so that invading pathogens pack a particularly destructive punch. (Scientists suspect the 2005 die-off was exacerbated by a viral event.) While Miller's bees have not, so far, been affected by the colony collapse, beekeepers in 24 states have reported losses as high as 80 and even 90 percent, and many of the afflicted bees have been in the almonds, rubbing shoulders with Miller's relatively healthy ones.
60 Minutes' Steve Kroft looks at the phenomenon with the apiarist credited for sounding the alarm, David Hackenberg. Full transcripts and video available at their website.
Topics:
2005, adult, CBS, collapse, Environment, Food, honey, leaving, losses, Media, number, pesticides, Steve Kroft, transcripts, truth, Video, videos, viral
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In 2002, Mike Pence told the Hill that he never eats alone with a woman other than his wife and that he won’t attend events featuring alcohol without her by his side, either.
Which reminds me of something we learned about the marriage of David French, the National Review writer who was briefly under consideration as an anti-Trump candidate for president last year. French, also a Christian conservative, was was deployed to Iraq in 2007 as a military lawyer.
So when David French was in Iraq, he wouldn't let his wife e-mail men or use Facebook. https://t.co/F1tEOwE4sc pic.twitter.com/ZOOV52QXUN
— Kevin Robillard (@PoliticoKevin) May 31, 2016
I'm not mocking monogamy. But in the modern world, women and men have to be able to interact in a non-sexual way, or we can't function as a society with gender equality. Mike Pence, as a politician, is going to work with women. David French's wife, Nancy, is a writer who's collaborated on a number of books, including a couple written with men.
Conservatives never stop complaining that Muslim societies have extreme restrictions on women. Many right-wingers insist that Islam is incompatible with Western values, citing, among other things, the differences in gender relations. So what's up with the Pences and the Frenches? They seem to be living in a way that's incompatible with Western values, too.
I say it's their right to do as they please -- but shouldn't their conservative compatriots be talking about these marital pacts the way they talk about sharia law? Shouldn't they be calling the Pences and the Frenches un-American?
Crossposted at No More Mr. Nice Blog
Topics:
Christian conservatives, David French, Karen Pence, Mike Pence, Sharia Law
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The West Africa Agricultural Productivity Programme (WAAPP) is a two-phase, 10-year Adaptable Program, each of five years duration.The first phase of WAAPP involves three countries – Ghana, Mali and Senegal.
The West and Central African Council for Agricultural Research and Development (WECARD/CORAF) is the sub-regional co-ordinating institution. WAAPP will concentrate on selected priority commodities.
The priority commodities, which were identified through a study WECARD/CORAF commissioned the International Food Policy Research Institute (IFPRI) to do in 2006, are roots and tubers, livestock, rice and cereals.
The specific country commodities under WAAPP are as follows: roots and tubers for Ghana; rice for Mali; and drought-tolerant cereals for Senegal. These commodities are expected to make the greatest contribution to the region’s agricultural growth and producers’ benefit from research and development.
• Root and Tuber Crops Improvement Project (RTIP) aimed at adaptive research, integrated pest management and planting material multiplication and distribution.
• Food Crops Development Programme (FCDP)
• JICA Rice Project: The objective is to develop improved water management practices, using the Sawah technology for rice production in the inland valleys. The Project is funded by the Japanese government.
• West Africa Seed Development Unit (WASDU). This is a joint project of IITA, GTZ and the CSIR. The objective of the Project is to ensure that farmers receive high quality seed for optimum crop yield.
• Peri-Urban Plantain Dissemination Project – GHANA/INIBAP: The objective is to evaluate some improved varieties of plantain and banana with farmers and train peri-urban farmers, around Kumasi and Takoradi, in planting material production.
• Gold Finger Cooking Banana and Plantain Testing and Dissemination Project (GATSBY) – GHANA/IDRC/CANADA: The objective is to develop a delivery system for healthy improved Musa germplasm, resistant to Black sigatoka disease and the Banana Streak Virus, for Ghanaian farmers.
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Passion 2022 is a gathering for the collegiate generation and is open to anyone who is 18-25 years old at the Mercedes Benz Stadium in Atlanta, GA! This year, the event is from January 2-4, 2022. Register today, spots are limited! Registration closes once spots are filled. This ticket includes one conference ticket, a hotel room near the conference center, and full transportation to and from Atlanta, GA.
Register Here!
PASSION 2022
January 2-4, 2022
Niceville CampusClick for Campus-specific Info
Bluewater Bay CampusClick for Campus-specific Info
North Crestview CampusClick for Campus-specific Info
South Crestview CampusClick for Campus-specific Info
Freeport CampusClick for Campus-specific Info
TheWKND 2022 is our Crosspoint Students Winter Conference focusing on connecting our 6th-12th grade students to Christ on February 18-20. This year TheWKND will be held at the Travelodge (Shalimar Retreat Center) in Panama City Beach! Registration closes at 11:59 p.m. on February 3.
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© Copyrights: Collecting, site design, programing: Leonid Kandinsky, 2001-2021. All trade marks, logos and cap designs are property of respective owners
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https://crowncapcollection.com/list.php?symbol=ZR
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crowncapcollection.com
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[] | false |
[] |
2,608,125 |
Refine your moves at FUNTANGO early Practice time. Leading tangueros and tangueros…. come be part of the Fun.
FUNtango is all about learning to dance Argentine Tango, beautifully and effortlessly. Inspiring improvisation, improving dynamic balance, posture and well-being …and enjoying yourself while mastering this complex dance…
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https://crsny.org/index.php/event/funtango-practice-2/2018-02-05/
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crsny.org
| 0 |
[] |
[] | false |
[] |
2,608,126 |
Cruiser is a multi-spectral image data explorer with precise interactive blending of solar image series across time and wavelength domains. Scrolling in two dimensions enables discovery and investigation of similarities and differences in structure and evolution across multiple wavelengths.
Dragging over the image will scroll time and wavelength
Two finger movement on a trackpad will also scroll time and wavelength
↖︎
↗︎
↘︎
↙︎
☜
wavelength
time
Horizonal cursor movement moves time slider.
Vertical cursor movement moves wavelength slider.
Zooming is controlled with the command key pressed and mouse scroll wheel (two finger vertical gesture on macos trackpad), or pinch zoom on IOS.
Panning is controlled with the command key pressed and mouse drag, or two finger motion on IOS.
blend
Scrolling between wavelength labels blends adjacent channels by proximity to the slider thumb. For example, placing the cursor midpoint between two wavelengths will blend each of the two channels equally. Blending can be toggled with the blend button.
cloud wavelengths: ↓all
To minimize observation load time, Cruiser does not download every wavelength by default. Available wavelengths that have not been downloaded are listed in the 'cloud wavelengths' box. Clicking channels in the 'cloud wavelengths' box will download them. The 'cloud wavelengths' 'all' button will download all available wavelengths for the current observation
2796 IRIS
171 AIA
⇒
171 AIA
2796 IRIS
Channels can be ordered by moving wavelength labels.
2796 IRIS
171 AIA
downloaded wavelengths:
⇒
2796 IRIS
downloaded wavelengths:
171 AIA
Channels can be removed by double clicking, or dragging to the 'downloaded wavelengths' box.
2796 IRIS
downloaded wavelengths:
171 AIA
⇒
2796 IRIS
171 AIA
downloaded wavelengths:
Wavelengths in the 'downloaded wavelengths' box can be double clicked to place them along the wavelength axis, or dragged into the wavelength axis.
Browse Data
Observations can be selected from the Browse Data menu. The Catalog menu menu can be used to selected different observation lists shown in Browse Data.
prev obs
next obs
The "prev obs" and "next obs" buttons in the menu bar can be used to step through the list of observations contained in the Browse Data table.
quick view
When the quick view checkbox is selected, only the first frame of each wavelength is downloaded for each observation. Unchecking quick view will download all frames for wavelengths along the wavelength axis for the current observation.
saved obs:
save
open
The currently viewed observation and all settings (wavelengths, brightness/contrast, x/y offsets etc.) can be saved and retrieved within the browser, with some limitations. Settings are stored only in the browser where they are saved and cannot be retrieved with other browsers or other machines. If the browser storage is cleared, these settings will be erased. Currently, only a single observation can be saved. The default "demo mode" observation cannot be saved.
layers
matrix
↔︎
The layers/matrix button pair control whether the channels are layered and sized to the full image area, or displayed simultaneously as a grid of smaller images. In matrix mode, clicking an image will move the wavelength scrollbar to the corresponding wavelength, and conversely, clicking a wavelength label will momentarily highlight the corresponding image..
play
The play button will play a movie in time or wavelength, depending on the time/wave button selection. The speed slider controls play speed. The numerical value shown next to the speed slider represents playback speed as a multiple of real time.
↔︎
gray
color
The gray/color butttons control whether images are displayed in color (if color table is available) or grayscale. This is a global setting.
rock
loop
↔︎
The rock/loop buttons control play behavior when the movie reaches the last frame. Loop will repeat the movie from the first frame. Rock will play the movie in reverse. The left and right angle buttons are used to step through time or wavelength.
grid
The grid button overlays a grid indicating field-of-view.
brightness
—
+
0
contrast
—
+
0
The brightness and contrast controls apply to individual wavelengths, and are adjustable when the wavelength slider is close to a wavelength label. Clicking a wavelength label, or using the alt + up/down arrow keys (discrete wave jumps) will activate the controls. Brightness and contrast settings remain in effect for the current cruiser session even when new observations are loaded.
align
The align arrows apply an x/y pixel offset to the channel nearest the wavelength cursor. Each wavelength can have different alignment offsets.
blink
Clicking the blink button will bounce the wavelength cursor between a pair of wavelengths closest to the wavelength cursor.
The light gray upward pointing triangles just below the time slider can be used to limit the endpoints for movie playing.
Mutually exclusive button pairs are shown with a small white arrow across their border. A blue background indicates an active selection, while a dark gray background indicates the inactive option.
The media controls are placed below the time axis, or to the right of the wavelength axis depending on the image dimensions, to maximimize image size within the browser window.
Clicking the browser's page reload button will restart cruiser with the currently selected observation as a new session, resetting all selectable options to their default values.
In IOS, the pinch to zoom gesture can be used in the image area to zoom the entire page. If the page is zoomed to an extent that the image covers the entire viewable screen, the page can be scrolled using two fingers (one finger motions on the image moves the time and wavelength scrollbars.)
Observations can be downloaded for offline viewing. Display the desired observation, then hit the lowercase "o" key to save to offline storage. The Browse Data table appends the symbol ☑ to observation rows available for offline viewing. All available wavelengths are downloaded to offline storage. Hitting the uppercase "O" key will remove the currently displayed observation from offline storage. The amount of storage available varies by browser and configurations. This feature is intended to store a small number of observations, not large archives. This feature is not yet supported in current versions of Safari, but based on developer preview versions, will be supported in a future release of Safari.
Keyboard Bindings
control key = lock wavelength
alt/option key = lock time
l = loop/rock movie toggle
t = time mode for playing movie
w = wavelength mode for playing movie
space bar = play/pause movie
+ = increase movie speed
- = decrease movie speed
b = wavelength blending toggle
g = grid toggle
c = color/grayscale toggle
m = layers/matrix toggle
r = record movie as sequence of jpegs, downloaded as zip. Recommend 30 sec max.
blink: hover over blink button to view direction of next wavelength, alt key to change direction.
a = download all wavelengths
n = next observation
N = previous observation
d = Browse Data toggle. When open, up/down arrow keys scroll data table instead of wavelength slider. Use enter key to download selected observation.
q = quick view toggle
u = toggle unlimited frames per wavelength
o = download current observation for offline viewing. Stored within browser.
O = remove from offline storage.
i = refresh cache
h = help toggle. When open, up/down arrow keys scroll help text instead of wavelength slider
F = full screen toggle (or esc to exit full screen) Only the image area is visible in full screen mode.
esc = exit full screen
--> up/down arrow = control wavelength slider, alt-arrow to move between discrete wavelengths.
left/right arrow = step through movie frames
Browser Requirements
Cruiser runs on on a minimum of Firefox 58, Safari 11.0, Chrome 63, and IOS Safari 11.2.5 (iPad only).
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| 1,239.1 | 7,899 |
https://cruiser.lmsal.com/?data=observatory/iris_195_frames/data/20141111_123921_3844259554/cruiser.json
|
cruiser.lmsal.com
| 0 |
[] |
[] | false |
[] |
2,608,127 |
Cruiser is a multi-spectral image data explorer with precise interactive blending of solar image series across time and wavelength domains. Scrolling in two dimensions enables discovery and investigation of similarities and differences in structure and evolution across multiple wavelengths.
Dragging over the image will scroll time and wavelength
Two finger movement on a trackpad will also scroll time and wavelength
↖︎
↗︎
↘︎
↙︎
☜
wavelength
time
Horizonal cursor movement moves time slider.
Vertical cursor movement moves wavelength slider.
Zooming is controlled with the command key pressed and mouse scroll wheel (two finger vertical gesture on macos trackpad), or pinch zoom on IOS.
Panning is controlled with the command key pressed and mouse drag, or two finger motion on IOS.
blend
Scrolling between wavelength labels blends adjacent channels by proximity to the slider thumb. For example, placing the cursor midpoint between two wavelengths will blend each of the two channels equally. Blending can be toggled with the blend button.
cloud wavelengths: ↓all
To minimize observation load time, Cruiser does not download every wavelength by default. Available wavelengths that have not been downloaded are listed in the 'cloud wavelengths' box. Clicking channels in the 'cloud wavelengths' box will download them. The 'cloud wavelengths' 'all' button will download all available wavelengths for the current observation
2796 IRIS
171 AIA
⇒
171 AIA
2796 IRIS
Channels can be ordered by moving wavelength labels.
2796 IRIS
171 AIA
downloaded wavelengths:
⇒
2796 IRIS
downloaded wavelengths:
171 AIA
Channels can be removed by double clicking, or dragging to the 'downloaded wavelengths' box.
2796 IRIS
downloaded wavelengths:
171 AIA
⇒
2796 IRIS
171 AIA
downloaded wavelengths:
Wavelengths in the 'downloaded wavelengths' box can be double clicked to place them along the wavelength axis, or dragged into the wavelength axis.
Browse Data
Observations can be selected from the Browse Data menu. The Catalog menu menu can be used to selected different observation lists shown in Browse Data.
prev obs
next obs
The "prev obs" and "next obs" buttons in the menu bar can be used to step through the list of observations contained in the Browse Data table.
quick view
When the quick view checkbox is selected, only the first frame of each wavelength is downloaded for each observation. Unchecking quick view will download all frames for wavelengths along the wavelength axis for the current observation.
saved obs:
save
open
The currently viewed observation and all settings (wavelengths, brightness/contrast, x/y offsets etc.) can be saved and retrieved within the browser, with some limitations. Settings are stored only in the browser where they are saved and cannot be retrieved with other browsers or other machines. If the browser storage is cleared, these settings will be erased. Currently, only a single observation can be saved. The default "demo mode" observation cannot be saved.
layers
matrix
↔︎
The layers/matrix button pair control whether the channels are layered and sized to the full image area, or displayed simultaneously as a grid of smaller images. In matrix mode, clicking an image will move the wavelength scrollbar to the corresponding wavelength, and conversely, clicking a wavelength label will momentarily highlight the corresponding image..
play
The play button will play a movie in time or wavelength, depending on the time/wave button selection. The speed slider controls play speed. The numerical value shown next to the speed slider represents playback speed as a multiple of real time.
↔︎
gray
color
The gray/color butttons control whether images are displayed in color (if color table is available) or grayscale. This is a global setting.
rock
loop
↔︎
The rock/loop buttons control play behavior when the movie reaches the last frame. Loop will repeat the movie from the first frame. Rock will play the movie in reverse. The left and right angle buttons are used to step through time or wavelength.
grid
The grid button overlays a grid indicating field-of-view.
brightness
—
+
0
contrast
—
+
0
The brightness and contrast controls apply to individual wavelengths, and are adjustable when the wavelength slider is close to a wavelength label. Clicking a wavelength label, or using the alt + up/down arrow keys (discrete wave jumps) will activate the controls. Brightness and contrast settings remain in effect for the current cruiser session even when new observations are loaded.
align
The align arrows apply an x/y pixel offset to the channel nearest the wavelength cursor. Each wavelength can have different alignment offsets.
blink
Clicking the blink button will bounce the wavelength cursor between a pair of wavelengths closest to the wavelength cursor.
The light gray upward pointing triangles just below the time slider can be used to limit the endpoints for movie playing.
Mutually exclusive button pairs are shown with a small white arrow across their border. A blue background indicates an active selection, while a dark gray background indicates the inactive option.
The media controls are placed below the time axis, or to the right of the wavelength axis depending on the image dimensions, to maximimize image size within the browser window.
Clicking the browser's page reload button will restart cruiser with the currently selected observation as a new session, resetting all selectable options to their default values.
In IOS, the pinch to zoom gesture can be used in the image area to zoom the entire page. If the page is zoomed to an extent that the image covers the entire viewable screen, the page can be scrolled using two fingers (one finger motions on the image moves the time and wavelength scrollbars.)
Observations can be downloaded for offline viewing. Display the desired observation, then hit the lowercase "o" key to save to offline storage. The Browse Data table appends the symbol ☑ to observation rows available for offline viewing. All available wavelengths are downloaded to offline storage. Hitting the uppercase "O" key will remove the currently displayed observation from offline storage. The amount of storage available varies by browser and configurations. This feature is intended to store a small number of observations, not large archives. This feature is not yet supported in current versions of Safari, but based on developer preview versions, will be supported in a future release of Safari.
Keyboard Bindings
control key = lock wavelength
alt/option key = lock time
l = loop/rock movie toggle
t = time mode for playing movie
w = wavelength mode for playing movie
space bar = play/pause movie
+ = increase movie speed
- = decrease movie speed
b = wavelength blending toggle
g = grid toggle
c = color/grayscale toggle
m = layers/matrix toggle
r = record movie as sequence of jpegs, downloaded as zip. Recommend 30 sec max.
blink: hover over blink button to view direction of next wavelength, alt key to change direction.
a = download all wavelengths
n = next observation
N = previous observation
d = Browse Data toggle. When open, up/down arrow keys scroll data table instead of wavelength slider. Use enter key to download selected observation.
q = quick view toggle
u = toggle unlimited frames per wavelength
o = download current observation for offline viewing. Stored within browser.
O = remove from offline storage.
i = refresh cache
h = help toggle. When open, up/down arrow keys scroll help text instead of wavelength slider
F = full screen toggle (or esc to exit full screen) Only the image area is visible in full screen mode.
esc = exit full screen
--> up/down arrow = control wavelength slider, alt-arrow to move between discrete wavelengths.
left/right arrow = step through movie frames
Browser Requirements
Cruiser runs on on a minimum of Firefox 58, Safari 11.0, Chrome 63, and IOS Safari 11.2.5 (iPad only).
|
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| 1,239.1 | 7,899 |
https://cruiser.lmsal.com/?data=observatory/iris_jpeg/data/20211023_050640_3880012095/cruiser.json
|
cruiser.lmsal.com
| 0 |
[] |
[] | false |
[] |
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