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.

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.

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.

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.

The graduate student as well as PhD researchers and Industry consultants are often faced with learning programs in double quick time and need references which are clear, concise, and have numerous worked out examples of how the program works. Furthermore it is often a daunting task not only to search the web for references, help, and worked out examples but searching through the numerous available books on the subject "using R."

I took a wild stab on this title from an advertisement I received from Wiley. In my opinion this text is not only has a great introduction to the essentials of the R language but a well rounded amount of information for nearly all foreseeable tasks I would be using R for.

To put it short, the title should be called "De 'ARGGHH!!!'ing R"

Statisticians like author Crawley bring their data in Excel spreadsheets,
and want spreadsheet outputs. They may be happy with this book. Others
bring their data from C or Fortran programs, and need an .eps file output
in order to get their graphics-containing manuscript reviewed. They will
find this book completely inadequate. The lack of figure numbers shows
little concern for the reader. Missing: sprintf, gsview, .eps files,
dev.off, ... . R's Unix-like "man" pages do help. There may be 5-15
poorly-explained options, and one example for all the options. Thus, the
"man" pages are inadequate backup for a book of this title. There are
variations in the singlar value decomposition of a matrix, depending upon
whether the Sigma matrix is square. Crawley omits such details.

Very helpful book when learning to program in R. Covers about everything I could think of concerning programming with R. Some topics were a bit wordy, others I wish were more detailed. But overall, it is a very good manual for R.

It's probably really just 4 stars, but compared to other R books I've seen it's 5 stars. It's comprehensive and relatively easy to follow. It covers a lot of topics. The code is easy to follow. The index could be better, but it's not bad.

It is an excellent book if you want something both to bring you up to speed, and then to serve as a comprehensive reference.

A good approach to collecting R books would be to start with this book, and then if you outgrow it in certain areas, obtain topic-specific R books in such areas modeling, data manipulation, or graphics as supplements.

The "R Book" is an excellent reference companion for both learning how to apply R code and for great explanations about the statistical techniques covered. Statistical coverage is appropriate at the beginning to mid level statistical user. I highly recommend this book.

This book is both ponderous and expensive, so my decision to buy it was predicated on the dual claim that it's 'the first comprehensive reference manual for the R language' and `ideal for novice and accomplished user alike'. As an R beginner and non-statistician (with some long-ago training therein) pressed into scientific data analysis on a regular basis, I wanted a comprehensive reference that covers both the R language and theory behind modern applied statistical methods.This is no small undertaking, but Crawley succeeds reasonably well at the task.

The book contains 27 chapters. The first 5 chapters cover subjects like getting started, essentials of the R language, data input, data frames, and graphics. A lot of the information in these chapters is freely available online at CRAN, or may be queried from within R itself. Still, I find it useful to have this info as part of any desktop reference, and most books on R are similarly equipped. I found nothing lacking here.

Chapters 6-8 cover tables, mathematics, and classical tests. In the mathematics chapter, you'll be introduced to a wealth of math and probability functions, as well as the basics of matrix algebra. If your statistical training centered mainly on the basic normal, student's t, Fisher's F, poisson, and chi-square distributions, get ready for an education. The author's presentation of this material is both in-depth and well articulated.
Chapters 9-20 cover statistical modeling, regression, ANOVA, ANCOVA, GLM, count data, count data in tables, proportion data, binary response variables, GAMs, non-linear models, and mixed effects models.Chapters 21-26 address more advanced topics of tree models, time series analysis, spatial statistics, multivariate statistics, survival analysis and simulation. The author's discussion of statistical models, ANOVA, GLM, and mixed effects models (the four chapters I have dug into thus far) covers theory as well as practical application inside R. Chapters are supplemented with worked examples drawn from various R data libraries. The R code used to generate solutions is presented as well, although I found it difficult to integrate because Crawley is using the R console interactively and snippets of code are spread out over many pages. Yes, you can download a data library, type in the code presented in the book, and get the same output. The difficulty arises in making the transition from textbook example to efficient and statistically valid processing of real- world data. If you're new to object oriented programming, this book will not teach you how to program in R. Only practice and good example can do that. I still struggle with some R programming basics and this book did not help at all.

Oddly, the book ends with a final chapter 'Changing the Look of Graphics'. Seems like this should be part of chapter 5 'Graphics'; it's a mystery why this was broken out as a separate chapter and stuck at the end.

The book contains numerous typos that suggest a lack of proofreading. Also annoying is the author's predilection for cross-referencing, such that one is constantly being advised to 'refer to page ...' for more info. Furthermore, the author profanely suggests Word as a text editor (yikes!). There are excellent text editors freely available for R, but Word isn't one of them. I use TINN-R, but there are other options. Also, options for managing R output are given short shrift. I use Notepad++, a tabbed, free text editor which is similar to TINN-R, but external to R. FYI, Notepad++ will also read SAS output in its native format, so one can easily review, compare, and extract information without invoking an R or SAS session.

Be advised, this book has created some controversy within the elite, tight-knit R Core Development group. The book was reviewed in the October 2007 issue of R News, available online (thumbs down). Crawley evidently is not part of the R Core Development 'inner sanctum', so the book's rather grandiose claim as 'the first comprehensive R reference manual' has engendered some criticism from that group. Other criticism about R expressions, the author's advice regarding use of certain R functions, and use of specific R packages may be found therein. Read the review then make your own judgment. As it stands, I don't consider this book to be an authoritative reference on either statistics or the R language, but it does offer an inclusive survey of both. If you already own a good statistics text, are familiar with object oriented programming, and only need a reference explaining how to get started programming in R, you'll save money by buying An Introduction to R by Venables and Smith. Amazon's wallet- friendly price: $13.57. Or you may download a free PDF version from the CRAN website.

I'll give the book four stars. It has some flaws (a second edition would be welcome), but overall constitutes a useful addition to the R literature. As for programming, I'm eagerly awaiting Braun and Murdoch's 'A First Course in Statistical Programming in R'. There are enough books on R-based statistical analysis in the vein of Crawley and others; we need a book that teaches programming and the latter should fill the gap nicely.

This is a great book for someone new to R, who also has some background in mathematics and programming. But, it is also helpful for the complete beginner because it shows you, step-by-step, how to load data into the R environment so you can actually get started using R, even if you don't have a nearby R mentor to help you out.

The writing is clear and accessible with examples provided for nearly all of the R software tools discussed. Also useful is that the author not only tells you which tools to use, but he also often says why they are important.

It's a thick book, but if you take the time to work your way through it, you should actually be able to use R to solve real world problems without external guidance from a R veteran. Check it out!

Crawley is the best guide I've run across to help one navigate the strange but powerful R language. This book is thick, not because it's wordy or overly ambitious, but because it goes into adequate detail to cover its material.