Where to Learn More

This reader provides an introduction to the basics of R, but there’s lots more to learn. Fortunately, many R and data science learning resources are available for free online or through the library.

Here are a few readers and notes created by DataLab staff:

• README, Write Me!, our workshop about how to organize and document computing projects.
• Principles of Data Visualization, our workshop about how to design clear and effective data visualizations.
• Adventures in Data Science, our course introducing humanities undergraduates to data science techniques.
• My personal teaching notes from several years of teaching statistical computing.

And here are a few books created by others that we’ve found useful:

• R for Data Science by Wickham & Grolemund. An introduction to using R for data science, but with a very heavy focus on Tidyverse packages.
• The Art of R Programming by Matloff. A general reference on R programming.
• Advanced R by Wickham. A description of how R works at a deeper level, with many examples of R features that are important for package/software development.
• The R Inferno by Burns. A discussion of the most difficult and confusing parts of R.

Finally, here are some websites popular in the R community:

• R Graph Gallery. Examples of graphs you can make in R, with code.
• RStudio Cheat Sheets. Cheat sheets for a variety of R tools and packages.
• R Weekly. Weekly updates about what’s happening in the R community.
• R-bloggers. An aggregator for blog posts about R.