This book is intended to provide students with a resource for learning R while using it during an introductory statistics course. The Introduction section covers common issues that students in a typical statistics course will encounter and provides a simple examples and does not attempt to be exhaustive. The Deeper Details section addresses issues that commonly arise in many data wrangling situations and is intended to give students a deep enough understanding of R that they will be able to use it as their primary computing resource to manipulate, graph and model data.

Other Resources

There are a great number of very good online and physical resources for learning R. Hadley Wickham is the creator of many of the foundational packages we’ll use in this course and he has worked on a number of wonderful teaching resources:

  • Hadley Wickham and Garrett Grolemund’s free online book R for Data Science. This is a wonderful introduction to the tidyverse and is free. If there is any book I’d recommend buying, this would be it. Many of the topics my book covers are perhaps better covered in Hadley and Garrett’s book. For people brand new to R, R for Data Science probably has the wrong presentation order. I love it as a reference, though.

  • Hadley Wickham and Jenny Bryan have a whole book on R packages to effectively manage large projects.

  • Finally Hadley Wickham also has a book about Advanced R programming and is quite helpful in understanding deeper issues relating to Object Oriented programming in R, Environments, Namespaces, and function evaluation.

There are a number of other resources out there that quite good as well:

Source and Error Reports

The source documents for this book live on GitHub at There you can can make bug reports or clone the GitHub repository, and submit fixes via pull requests. I welcome feedback and suggestions for improvement.


I am grateful for the emotional and co-parenting support of my wife Aubrey, and without whom this book would not be possible.