W.N. Venables, B.D. Ripley

Mentioned 1

A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.

Mentioned in questions and answers.

I have a book called Statistics for Computer Scientists as well as my engineering statistics textbook, so I'm thinking about using various problems and examples in those to learn R, which is probably a good start. But can anyone recommend books and web sites that have information about R, especially if they are designed for people with some knowledge in statistics? Are there any medium to large projects or real-world situations where I, as a college student studying software engineering, might be able to use R to get a feel for it?

- Understandable documentation about R, which has some links to R documentation. There are also some basic information/tutorial sites.
- Books for learning the R language, which focuses on books for learning R.

Years ago I used R in an undergrad statistics course which used Modern Applied Statistics with S-PLUS as its text (that edition is now out of print, but this book seems equivalent).

R is compatible enough with S in general that you can use a lot of the S resources out there.

My favorite R book is R Programming for Bioinformatics, by Robert Gentlemen. It doesn't try to teach you statistics at the same time as you learn the language, but rather presents the language from a programmer's viewpoint. I thought the book gave much better background than any of the online resources did. This is from the perspective of a biologist/programmer who didn't know much statistics when first learning R.

This is essentially a dump of my bookmarks, and what I have on my desk.

Getting started:

- A tutorial video on R
- John Cook's introduction to R for programmers
- R reference card
- Interactive tutorial: Introduction to R

Advanced:

Books:

- The R Book (Covers the basics, classical statisitical tests, basic statistical modeling (ANOVA, ANCOVA, GLM, non-linear models, etc.), advanced statistical modeling (tree models, time-series analysis, spatial statistics, survival analysis, simulation), and twiddling with the graphics output.
- R Graphics (How to make R graphics look sharp)

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