Average Reviews:
(More customer reviews)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.
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