36-402, Section A, Spring 2017
Resources for Learning R, or Learning It Better
R is a free, open-source software
package/programming language for statistical computing. You should have begun
to learn it in 36-401 (if not before), and this class presumes that you have.
Here are some resources for learning R, or refreshing your memory, or
learning to use it better. It makes no attempt to be comprehensive.
Even if you know how to do some basic coding (or more), you should read the
page of Minimal Advice on
- The official intro, "An Introduction to R", available online in
- John Verzani, "simpleR",
- Quick-R. This is
primarily aimed at those who already know a commercial statistics package like
SAS, SPSS or Stata, but it's very clear and well-organized, and others may find
it useful as well.
Burns, The R
Inferno. "If you are using R and you think you're in hell, this is a map
- Thomas Lumley, "R Fundamentals and Programming Techniques"
- Paul Teetor, The R Cookbook, explains how to use R to
do many, many common tasks. (It's like the inverse to R's help: "What command
does X?", instead of "What does command Y do?"). It is one of the required
texts, and is available at the campus bookstore.
- The notes for 36-350, Introduction to
- There are now many books about R. Some recommendable ones:
- Joseph Adler R in a Nutshell
ISBN 9780596801700). Probably most useful for those with previous experience programming in another language.
- W. John Braun and
J. Murdoch, A
First Course in Statistical Programming with R (Cambridge University Press, 2008; ISBN 978-0-521-69424-7)
- John M. Chambers, Software for Data Analysis:
Programming with R
The best book on writing clean and reliable R programs; probably more advanced
than you will need.
Matloff, The Art of R Programming (No Starch Press, 2011,
Good introduction to programming for complete novices using R. Less statistics
than Braun and Murdoch, more programming skills.