(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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