The R Book
This book was for my son and it was just what he wanted. He was very happy with it!
It's about time...
It's about time that software like R became available and popular, it is clearly the way to go. I've been learning it on my own for a few months now with the help of about a dozen online .pdf file manuals, and sometimes using the Nabble forum for R. I finally broke down and bought this book, and wish I would've bought it in the beginning. If I were to write a book on this subject, this is pretty much exactly what I would do...except I'd like to see a companion volume that explores the numerous packages, maybe with an emphasis on Bayesian methods, such as in the packages arm, boa, coda, MCMCpack, MNP, R2WinBUGS, etc., but hey, that's me.
If you've had it with other software that doesn't let you do everything you'd like to do, then I highly recommend R, and The R Book for starters.
Worth the money
This is a good book, if you are a lousy programmer and just want something to get you started in R. And that was me a couple of months ago.The style is conversational, the exposition patient. That's just what you need if you have been put off by the on-line documentation.
In its overall architecture, the book is a bit scatty. It spends a little too much time on statistical theory and, as other reviewers have said, not enough on the more advanced programming features of R.
But if you just want something to help you take those first few steps, you really can't go wrong with this, and it will remain a great reference for the basic functionality. Expect that at some stage will have to supplement this with material that iscloser to your specific interests, and you won't be disappointed.
Throurough and insightful.
This book is an excellent introduction to the R language and the statistical theory underlying it. It requires some patience as there is a considerable deal of repetition (the exercises are all very similar but gradually increase in complexity as one progresses from two way anova to generalized additive models and more). Also there are a few small errors (I did not mind these as they helped me realized that I was still concentrating) - the book could have used a keen editorial eye. Am very happy with my purchase.
Good content, disorganized presentation
Given the length of this book, and the list of contents covered, I had the highest expectations about it.
After spending 2 intensive months reading it, I have mixed feelings. Positive points are the large number of statistical models and methods described. The R examples are useful to follow the explanations, and the writing style is comprehensive. I agree with some reviewers in that the linear models section (Chaps. 9-19) is the most useful one. The last Chapter also presents useful tricks for dealing with graphs in R.
Unfortunately, I have 2 important complaints. The first one is about the presentation of contents: simply CHAOTIC. The author systematically abuses of cross-references. You will find sentences like "here we present an example of [method XX] that will be introduced on page XXX" throughout the entire book. This is disappointing, since it forces the reader to constantly move back and forth, looking for the relevant info. There is no point in presenting an example based on a method that you haven't introduced yet. Examples should be autonomous, and not frequently taken from previous data sets "already used in page YYY".
The second complaint derives from the previous one. The book is hard to use as both a reference manual and a companion for undergraduate or graduate students. Disregarding the comments from the author, if you don't have a solid theoretical background in statistical inference, regression analysis and linear models, you won't get very much benefit of this book. The author completely lacks of a rigorous, structured method for presenting new concepts. Even worse, important definitions and concepts are usually hidden in between of examples that has nothing to do with them.
In summary, if you already have a good theoretical background in statistics, this could be a useful add-on to your bookshelf (though be ready to spend a lot of side tags to map important concepts for later).
If you're looking for a introductory book with R, Springer has just published a second, expanded edition of the classic book by Dalgaard. If you're looking for a definitive reference manual of statistical methods illustrated with R, you will have to wait for something else, or look for specific titles (Like Faraway's "Linear Models with R"). For Ph.D. students looking for a comprehensive an up-to-date book on statistics with R, to improve their skills quickly, I still recommend the second edition of "Data Analysis and Graphics Using R", by Maindonald and Brown.
(13 reviews)
