Experts

Even once you are an expert at R code development, learning some topics in depth will both help you develop better code and share it more effectively with others.

• Dive into the foundations of R. Most R users are not programmers, and therefore much of their R code is not as readable, fast, or efficient as it could be. The second edition of Hadley Wickham’s book, Advanced R (2019) (available for free online and as an O’Reilly paperback from Amazon), unlocks many of the secrets behind how R works the way it does, and gives you new strategies for solving diverse problems. You may also want to bookmark Advanced R Solutions, which provides worked solutions to the exercises in this book.

• Learn how to extend R. While its content sometime overlaps with Hadley’s Advanced R, the R Core Team offers Writing R Extensions (electronic version) on CRAN. This book is particularly useful if you wish to add your own C and C++ routines to R, but also has unique information on debugging, the R API, and runtime profiling that is difficult to find anywhere else.

• Build your own packages. R packages allow you to share your functions with other R programmers in a modular and easy-to-integrate way. The above-mentioned Writing R Extensions documents how to write packages, but you may find Hadley Wickham’s R Packages book (2015) (electronic here (1st edition) and an O’Reilly paperback (1st edition) from Amazon here) a bit easier to read and more step-by-step. The second edition of the R Packages book is currently in progress, written by Hadley and co-author Jenny Bryan. You can read the in-progress book for free online. If you want your R code to reach the widest possible population of developers, you’ll want to know how to build packages.

• Use Python in your analyses. Anyone who insists you must choose between R and Python for doing data science is creating a false choice. R offers an easy way to incorporate Python code in the reticulate package. Sean Lopp’s webinar, R, RStudio 1.2 & Python—a love story, demonstrates how easily developers can integrate Python code into their R workflows and walks through the development of a reticulated Shiny app.

• Try your hand at Tensorflow for deep learning. RStudio hosts a web site dedicated to R and Tensorflow at tensorflow.rstudio.com, where you can learn how to use deep learning in your analyses. J.J. Allaire and Francois Chollet have published a Deep Learning with R book (2018). You can hear J.J. describe the philosophy behind the system in his rstudio::conf 2019 video. For an quick overview of resources for how to get started with deep learning in R, read Sigrid Keydana’s article and subscribe to the Tensorflow for R blog.

• Communicate with R Markdown. The R Markdown family of packages enables you to create and share beautiful data science products like books, blogs, websites, and presentations. Experiment with packages like bookdown, distill, and blogdown; each extends R Markdown to help you publish polished websites for sharing your work. If you want to present analyses or visualizations made with R code, consider making your slides with R Markdown using the xaringan package. If you’ve built an R package, the pkgdown package makes it quick and relatively painless to build a website for your package, using all the work you’ve already done to write and document your functions.