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.
- 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 no 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 the 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.
- 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