by Joseph Rickert

Last week, I was fortunate enough to attend the R Summit & Workshop, an invitation only event, held at the Copenhagen Business School. The abstracts for the public talks presented are online and well worth a look. Collectively they provide a snapshot of the state of development of R and the R Community as well some insight into the directions in which researchers are moving to expand the boundaries of R.

Real highlights of the event were talks by Jennifer Bryan and Mine Çetinkaya-Rundel, two educators who are channeling enormous amounts of energy into teaching statistics and statistical programming, and into developing new pedagogical methods to improve the learning experience for both students and teachers alike. Both Mine and Jennifer are committed to R, as well as to using state of the art developer tools such as RStudio, R Markdown, Git and GitHub.

If you clicked on the link to Jennifer’s university home page above and expected to see more content there you are probably not running with the in crowd. Anybody who aspires to bask in the faintest glow of tech cool is hanging out on GitHub. So go to github.com/jennybc to find a place where social media, software development and best practices collide to generate a state-of-the-art learning platform.

What Jennifer has created there is the R version of a full immersion experience for learning a new language. Just like you can’t separate the highs and awkward lows of human interactions from tripping over the grammar while learning Japanese on the streets of Tokyo, at jennybc you have to cope with GitHub, R Markdown and complying with best practices while doing your homework, seeking help, and learning from your peers.

As Jennifer writes in her synopsis of her R Summit & Workshop talk:

I've formed strong opinions about workflows for R Markdown + GitHub and what the big wins are.

• Make it a habit to render .Rmd and .R files to Markdown and, maybe, HTML. Places those files where people can see them, e.g. on GitHub! They help people decide whether they need or want to obtain and run your code.
• Exploit Markdown and other browsability features of GitHub to make your source repo do double duty as a decent project webpage. Refine your policies about never committing an intermediate or final product.
• Advanced GitHub searching, coupled with Winston Chang's mirror of R source and Gábor Csárdi's mirror of CRAN, helps you implement “read the source” in an efficient manner.
• GitHub Issues are a very versatile way to facilitate conversations that can be, by turns, conversational or technical and code-based.
• Never pick NA as your username for anything.

Mine, who focuses on undergraduate education, points out that: “R is attractive because, unlike software designed specifically for courses at this level, it is relevant beyond the introductory statistics classroom, and is more powerful and flexible.”  Poke around Mine’s GitHub page and you will see that she is all about R, open source, reproducibility and teaching good habits and right values. In addition to her work in the classroom, Mine has developed the Coursera course: Data Analysis and Statistical Inference, is a coauthor of three, free R based textbooks and is a driving force behind the ASA Datafest competitions.

Students are often ambivalent as to whether they are looking for education or training. But if you are a student of either Mime or Jennifer you are going to get some of both, and have a real shot at launching a productive R fueled career.