Chapter 6 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:
6.1 Beginner Tutorials and References
- Try
Rfrom Code School.- An interactive introduction to the basics of
R. Useful for getting up to speed onR’s syntax.
- An interactive introduction to the basics of
- Quick-R by Robert Kabacoff.
- A good reference for
Rbasics.
- A good reference for
RTutorial by Chi Yau.- A combination reference and tutorial for
Rbasics.
- A combination reference and tutorial for
RProgramming for Data Science by Roger Peng- A great text for
Rprogramming beginners. DiscussesRfrom the ground up, highlighting programming details we might not discuss.
- A great text for
6.2 Intermediate References
Rfor 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.
- Similar to Advanced
- The Art of
RProgramming 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.
- Gentle introduction to the programming side of
6.3 Advanced References
- Advanced
Rby Hadley Wickham.- From the author of several extremely popular
Rpackages. Good follow-up to The Art ofRProgramming. (And more up-to-date material.)
- From the author of several extremely popular
- The
RInferno by Patrick Burns.- Likens learning the tricks of
Rto descending through the levels of hell. Very advanced material, but may be important ifRbecomes a part of your everyday toolkit.
- Likens learning the tricks of
- Efficient
RProgramming by Colin Gillespie and Robin Lovelace- Discusses both efficient
Rprograms, as well as programming inRefficiently.
- Discusses both efficient
6.4 Quick Comparisons to Other Languages
Those who are familiar with other languages may find the following “cheatsheets” helpful for transitioning to R.
- MATLAB, NumPy, Julia
- Python pandas comparison with R/R Libraries
- Stata
- SAS - Look for a resource still! Suggestions welcome.
6.5 RStudio and RMarkdown Videos
The following video playlists were made as an introduction to R, RStudio, and RMarkdown for STAT 420 at UIUC. If you are currently using this text for a Coursera course, you can also find updated videos there.
Note that RStudio and RMarkdown are constantly receiving excellent support and updates, so these videos may already contain some outdated information.
RStudio provides their own tutorial for RMarkdown. They also have an excellent RStudio “cheatsheet” which visually identifies many of the features available in the IDE. You may also explore some additional “cheatsheets” on their website.
6.6 RMarkdown Template
This .zip file contains the files necessary to produce this rendered document. This document is a more complete version of a template than what is seen in the above videos.