Chapter 5 R Resources

So far, we have seen a lot of R, and a lot of R quickly. Again, the preceding chapters were in no way meant to be a complete reference for the R language, but rather an introduction to many of the concepts we will need in this text. The following resources are not necessary for the remainder of this text, but you may find them useful if you would like a deeper understanding of R:

5.1 Beginner Tutorials and References

• Try R from Code School.
  • An interactive introduction to the basics of R. Useful for getting up to speed on R’s syntax.
• Quick-R by Robert Kabacoff.
  • A good reference for R basics.
• R Tutorial by Chi Yau.
  • A combination reference and tutorial for R basics.
• R Programming for Data Science by Roger Peng
  • A great text for R programming beginners. Discusses R from the ground up, highlighting programming details we might not discuss.

5.2 Intermediate References

• R for Data Science by Hadley Wickham and Garrett Grolemund.
  • Similar to Advanced R, but focuses more on data analysis, while still introducing programming concepts. Especially useful for working in the tidyverse.
• The Art of R Programming by Norman Matloff.
  • Gentle introduction to the programming side of R. (Whereas we will focus more on the data analysis side.) A free electronic version is available through the Illinois library.

5.3 Advanced References

• Advanced R by Hadley Wickham.
  • From the author of several extremely popular R packages. Good follow-up to The Art of R Programming. (And more up-to-date material.)
• The R Inferno by Patrick Burns.
  • Likens learning the tricks of R to descending through the levels of hell. Very advanced material, but may be important if R becomes a part of your everyday toolkit.
• Efficient R Programming by Colin Gillespie and Robin Lovelace
  • Discusses both efficient R programs, as well as programming in R efficiently.