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.