The R Book | 
 enlarge | Author: Michael J. Crawley
Publisher: Wiley
Category: Book
List Price: $110.00
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Avg. Customer Rating:  9 reviews
Sales Rank: 16138
Media: Hardcover
Number Of Items: 1
Pages: 950
Shipping Weight (lbs): 3.7
Dimensions (in): 9.8 x 6.9 x 2.2
ISBN: 0470510242
Dewey Decimal Number: 519.502855133
EAN: 9780470510247
ASIN: 0470510242
Publication Date: June 15, 2007
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Product Description
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.
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| Customer Reviews: Read 4 more reviews...
 Good content, disorganized presentation August 30, 2008
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 R Book August 16, 2008
0 out of 1 found this review helpful
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"
 Very incomplete. July 25, 2008
4 out of 8 found this review helpful
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.
Big book with lots of good information May 21, 2008
0 out of 3 found this review helpful
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.
 The best R book I've found so far. March 25, 2008
6 out of 6 found this review helpful
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.
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