R programming books (updated)

January 28, 2011
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(This article was first published on Why? » R, and kindly contributed to R-bloggers)

In a recent post, I asked for suggestions for introductory R computing books. In particular, I was looking for books that:

  • Assume no prior knowledge of programming.
  • Assume very little knowledge of statistics. For example, no regression.
  • Are cheap, since they are for undergraduate students.

Some of my cons aren’t really downsides as such. Rather, they just indicate that the books aren’t suitable for this particular audience. A prime example is “R in a Nutshell”.

I ended up recommending five books to the first year introductory R class.

Recommended Books

  • A first course in statistical programming with R (Braun & Murdoch)
    • Pros: I quite like this book (hence the reason I put it on my list). It has a nice collection of exercises, it “looks nice” and doesn’t assume knowledge of programming. It also doesn’t assume (or try to teach) any statistics.
    • Cons: When describing for loops and functions the examples aren’t very statistical. For example, it uses Fibonacci sequences in the while loop section and the sieve of Eratosthenes for if statements.
  • An introduction to R (Venables & Smith)
    • Pros: Simple, short and to the point. Free copies available. Money from the book goes to the R project.
    • Cons: More a R reference guide than a textbook.
  • A Beginner´s Guide to R by Zuur.
    • Pros: Assumes not prior knowledge. Proceeds through concepts slowly and carefully.
    • Cons: Proceeds through concepts very slowly and carefully.
  • R in a Nutshell by Adler.
    • I completely agree with a recent review by Robin Wilson: “Very comprehensive and very useful, but not good for a beginner. Great book though – definitely has a place on my bookshelf.”
    • Pros: An excellent reference.
    • Cons: Only suitable for students with a previous computer background.
  • Introduction to Scientific Programming and Simulation Using R by Jones, Maillardet and Robinson.
    • Pros: A nice book that teaches R programming. Similar to the Braun & Murdoch book.
    • Cons: A bit pricey in comparison to the other books

Books not being recommended

These books were mentioned in the comments of the previous post.

  • The Basics of S-PLUS by Krause & Olson.
    • Most students struggle with R. Introducing a similar, but slightly different language is too sadistic.
  • Software for Data Analysis: Programming with R by Chambers.
    • Assumed some previous statistical knowledge.
  • Bayesian Computation with R by Albert.
    • Not suitable for first year students who haven’t taken any previous statistics courses.
  • R Graphics by Paul Murrell
    • I know graphics are important, but a whole book for an undergraduate student might be too much. I did toy with the idea of recommending this book, but I thought that five recommendations were more than sufficient.
  • ggplot2 by Hadley Wickham.
    • Great book, but our students don’t encounter ggplot2 in their undergraduate course.

Online Resources

  • Introduction to Probability and Statistics by Kerns
    • Suitable for a combined R and statistics course. But I don’t really do much stats in this module.
  • The R Programming wikibook (a work in progress).
    • Will give the students this link.
  • Biological Data Analysis Using R by Rodney J. Dyer. Available under the CC license.
    • Nice resource. Possibly a little big for this course (I know that this is very picky, but I had to draw the line somewhere). Will probably use it for future courses.
  • Hadley Wickham’s devtools wiki (a work in progress).
    • Assumes a good working knowledge of R
  • The R Inferno by Patrick Burns
    • Good book, but too advanced for students who have never programmed before.
  • Introduction to S programming
    • It’s in french – this may or may not be a good thing depending on your point of view ;)

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