The R Book
The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the author?s bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines. - Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities.
- Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test.
- Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more.
The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.
The R Book Accessories
 R Graphics (Computer Science and Data Analysis)
Bayesian Computation with R (Use R)
A Handbook of Statistical Analyses Using R
Introductory Statistics with R (Statistics and Computing)
Data Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics)
Data Manipulation with R (Use R)
Statistics: An Introduction using R
A First Course in Statistical Programming with R
Lattice: Multivariate Data Visualization with R (Use R)
The R Book Reviews
He was very happy with it. This book was for my son and it was just what he wanted.
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. It's about time that software like R became available and popular, it is clearly the way to go. I finally broke down and bought this book, and wish I would've bought it in the beginning. 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. 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.
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. And that was me a couple of months ago.The style is conversational, the exposition patient. In its overall architecture, the book is a bit scatty. Expect that at some stage will have to supplement this with material that iscloser to your specific interests, and you won't be disappointed. This is a good book, if you are a lousy programmer and just want something to get you started in R. That's just what you need if you have been put off by the on-line documentation.
Am very happy with my purchase. This book is an excellent introduction to the R language and the statistical theory underlying it. 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. 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).
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). Given the length of this book, and the list of contents covered, I had the highest expectations about it. You will find sentences like "here we present an example of [method XX] that will be introduced on page XXX" throughout the entire book. 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. This is disappointing, since it forces the reader to constantly move back and forth, looking for the relevant info. I agree with some reviewers in that the linear models section (Chaps. 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.
There is no point in presenting an example based on a method that you haven't introduced yet. If you're looking for a introductory book with R, Springer has just published a second, expanded edition of the classic book by Dalgaard. The R examples are useful to follow the explanations, and the writing style is comprehensive. Even worse, important definitions and concepts are usually hidden in between of examples that has nothing to do with them. Unfortunately, I have 2 important complaints. Positive points are the large number of statistical models and methods described. The author completely lacks of a rigorous, structured method for presenting new concepts.
9-19) is the most useful one. After spending 2 intensive months reading it, I have mixed feelings. 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").
The second complaint derives from the previous one. Examples should be autonomous, and not frequently taken from previous data sets "already used in page YYY". For Ph.D.
The book is hard to use as both a reference manual and a companion for undergraduate or graduate students. The last Chapter also presents useful tricks for dealing with graphs in R. The first one is about the presentation of contents: simply CHAOTIC.
The author systematically abuses of cross-references.
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