We at R Views believe that the R ecosystem, centered around CRAN and Bioconductor, is the world’s largest open repository of statistical knowledge. R packages provide implementations and examples for a tremendous number of statistical methods, procedures, and algorithms. Yet, the virtual library of the R ecosystem is far from being complete. There is plenty of room for examples that explore some little travelled path of computational statistics or illuminate a familiar field with clarity. We invite you to contribute to expanding the knowledge available in the R ecosystem through blogging.

Documentation for R packages begins with the package pdf file which provides a detailed description for each individual function, indicates relationships among functions and often includes references to the statistical and scientific literature. Going down an organizational level you can view the source code for each function. Moving up a level, README files and package vignettes provide a coherent overview of the capabilities of the package as a whole and often include example and elaborate use cases.

While the burden to explain what R packages do falls mainly on package authors, all of us data scientists, statisticians, researchers, students and “ordinary” R users can add to the knowledge encoded in R by elaborating on the capabilities of R functions and packages, developing additional examples and contributing statistical analyses in areas where we may have developed some expertise.

We are not talking about Nobel Laureate work here. But nearly everyone who regularly uses R has some gem stashed away somewhere. We are looking to mine those gems and organize them in a way that can be shared with the R Community.

We are asking you to please dig up your gem and organize it into a blog post. Tell us what your gem does, how it works, and why it is of some value. Some of the most popular R Views posts introduce technical topics by providing R based tutorials. Others show how to efficiently accomplish everyday tasks or present an elegant calculation or visualization. Think in terms of a well explained example, or if you are really ambitious, you could write that package vignette that you wish existed. Look below for some guidance on more elaborate ideas.

Call for Posts

Please submit you posts using the following form: rstd.io/rviews-2021. All submissions will appear on the Call for Docs page on the RStudio Community Site, and you will need an RStudio Community profile.

The deadline for submission is Friday, September 23, 2021. We planning awards of a sort to the best entries and would like to announce these by November 1, 2021.

Awards

The award categories and the associated prizes are as follows:

• Honorable Mentions:
  • One year free RStudio Cloud Premium
  • A bunch of hex stickers of RStudio packages
• Runners Up:
  • All prizes listed above, plus
  • Some number of RStudio t-shirts, books, and mugs (worth up to $200)
• Grand Prizes:
  • All prizes listed above, plus
  • Special & persistent recognition by RStudio in the form of a winners page, and a badge that will be publicly visible on your RStudio Community profile
  • A selected group of submissions will be invited to appear on R Views

<sup>* Please note that we may not be able to send t-shirts, books, or other larger items to non-US addresses.
* Please note that all articles on R Views will go through an editorial review before being published there.
* With the author’s permission, some posts may appear on R Views before the awards announcements.</sup> .

Announcement

The names and work of all winners will be highlighted in an announcement on R Views, and we will announce them on RStudio’s social platforms, including RStudio Community (unless the winner prefers not to be mentioned).

Need inspiration?

• Edgar Ruiz Gentle Introduction to tidymodels is a good example of a homemade package vignette.
• Jonathan Regenstein’s series on Reproducible Finance with R presents several masterful examples of Financial use cases.

Guidance

• Posts should be well-written, contain enough code to support the main argument and have a few plots and/or tables.
• Novelty – We are looking for original content. That is, your article should not have appeared on blogging platforms, other than your personal website or blog.
• Contribution – Your article should provide novel insight and utility. For example, an exploration of a package or use case that is currently poorly documented will be better received than one which already has excellent documentation and vignettes either on CRAN or in the R community.
• Reproducible – Your submission will need to link to a repo which includes an .Rmd file that reproduces all code, images, and other output in the submitted article. If there is code that requires special resources, such as access to a private database, it is fine to simply refer to these in your repo.
• Please confine your posts to less than 1,000 words, excluding code and image captions. Your article should be self-contained, but may refer to additional content on your repo or personal website.

Categories

• Homemade Package Vignette – Package authors put in a lot of effort to build their library and the documentation that supports it, but they can’t cover everything. They may not have developed a vignette yet, or written additional documentation explaining the set of functionality that is especially useful. With this type of article, introduce readers to a package – or small group of packages – highlighting interesting features or extensions. It should not replicate any existing documentation, instead it should be distinct from – and complementary to – any existing package documentation.
• Entry Point into a Topic – It’s often daunting to get started as you’re exploring a new field or approaching a new problem or methodology. It’s incredibly pleasing when you find the resource that serves as your entry point into that new domain, demystifying the space, pointing out useful packages and examples, getting you started on your work. With this article type, explore resources centered around one problem, highlighting how R helps you approach it.