I’ve been looking for tools for data analysis lately, and remembered R, a language primarily geared toward statistics and graphing. R is a little bit unusual in how it works compared to other languages, so a quick skim of the manual wasn’t giving me a good understanding. A book was called for. Most of the books on R assume you’re a statistician already, or are trying to teach you statistics using R. While getting a better grip on statistics is high on my list of things to do, I wanted to learn R first. Hence, this book.
The book did what I hoped it would – gave me a basic understanding of R’s syntax and how to perform common operations. It’s also a slim volume at 217 pages, which is always appreciated. I found myself coming back to it for reference, rereading chapters that I had skimmed and getting more out of it the second time. So on the whole, it’s a good and useful introduction to R. It gave me the necessary footing to go out and explore more on my own. Do beware, however, that some of the examples have typos or other errors. Also, it leaves large swathes of R’s functionality completely unexplored (no discussion of R’s object orientation, for example). Nevertheless, you’ll be well prepared to go out and track down whatever further information you require after reading this book.

I took a great course a few years back based on a book called ‘Linear Models in R’, which has since been superceded by ‘Practical Regression and Anova in R’, both of which appear to be available online:
http://www.maths.bath.ac.uk/~jjf23/book/
Reading between the lines of the code in the book, you can learn a lot about how to use R effectively.