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Product Details

• Amazon Sales Rank: #17729 in Books
• Published on: 2007-06-15
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 950 pages

Amazon.com Review
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.

Excerpts from Chapter 4 of The R Book

Chapter 4: Level Set Trees and Code
Learn how to make a volume plot and a barycenter plot, and calculate level set trees with the algorithm LeafsFirst, which is implemented in function ``leafsfirst''. This function takes as an argument a piecewise constant function object.

The multimodal 2D example

(Click on image to enlarge)

We consider the density shown in the 2D three-modal density, and calculate first a piecewise constant function object representing this function, and then calculate the level set tree.

N<-c(35,35)                      # size of the grid  pcf<-sim.data(N=N,type="mulmod") # piecewise constant function  lst.big<-leafsfirst(pcf)         # level set tree  

We may make the volume plot with the command ''plotvolu(lst)''. However, it is faster first to prune the level set tree, and then plot the reduced level set tree. Function ''treedisc'' takes as the first argument a level set tree, as the second argument the original piecewise constant function, and the 3rd argument ''ngrid'' gives the number of levels in the pruned level set tree. We try the number of levels ngrid=100.

lst<-treedisc(lst.big,pcf,ngrid=100)  

Now we may make a volume plot with the function ''plotvolu''.

plotvolu(lst)  

We draw barycenter plots with the function ''plotbary''.

  plotbary(lst,coordi=2)  # 2nd coordinate  

Note: We may find the number and the location of the modes with the ''modecent'' function, which takes as argument a level set tree. Function ''locofmax'' takes as argument a piecewise constant function and calculates the location of the maximum.

modecent(lst)  locofmax(pcf)  

The 3D tetrahedron example

(Click on image to enlarge)

We consider the 3-dimensional example. The calculation is much more time consuming this time.

N<-c(32,32,32)                    # the size of the grid  pcf<-sim.data(N=N,type="tetra3d") # piecewise constant function  lst.big<-leafsfirst(pcf)             # level set tree  lst<-treedisc(lst.big,pcf,ngrid=200) # pruned level set tree    plotvolu(lst,modelabel=FALSE)        # volume plot  plotvolu(lst,cutlev=0.010,ptext=0.00045,colo=TRUE) # zooming    coordi<-1                   # coordinate, coordi = 1, 2, 3  plotbary(lst,coordi=coordi,ptext=0.0006) # barycenter plot     

This time we have used parameter ''cutlev'' to make a zoomed volume plot. When this parameter is given, then only the part of the level set tree is shown which is above the value ''cutlev''. Typically it is better to zoom in to the volume plot by cutting the tails of the volume function away. This is achieved by the parameter ''xlim''. We may us for example the following command to make a ``vertically zoomed'' volume plot.

plotvolu(lst,xlim=c(140,220),ptext=0.00045,           colo=TRUE,modelabel=FALSE)   

Additional parameters which we have used are the ''modelabel'', which is used to suppress the plotting of the mode labels, ''ptext'', which lifts the mode labels with the given amount, and ''colo'', which colors the graph of the volume function to make a comparison with the barycenter plots easier.

The 4D pentahedron example

(Click on image to enlarge)

We consider the 4-dimensional example.

N<-c(16,16,16,16)    pcf<-sim.data(N=N,type="penta4d")  lst.big<-leafsfirst(pcf)  lst<-treedisc(lst.big,pcf,ngrid=100)    plotvolu(lst,modelabel=F)  # volume plot  plotvolu(lst,cutlev=0.0008,ptext=0.00039,colo=TRUE) # zooming    coordi<-1               # coordinate, coordi = 1, 2, 3, 4  plotbary(lst,coordi=coordi,ptext=0.0003) # barycenter plot  

Review
"There is a tremendous amount of information in the book, and it will be very helpful … .This is a potentially very useful book." (Journal of Applied Science, December 2008)

"…if you are an R user or wannabe R user, this text is the one that should be on your shelf.  The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R." (The American Statistician, Aug 2008)

"The R Book; provides the first comprehensive reference manual for the R language." (Statistica 2008)

"…a 950-page comprehensive reference manual for what is perhaps becoming the most powerful and flexible statistical software environment…" (CHOICE, December 2007)

‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, 19th July 2007)  

"There is a tremendous amount of information in the book, and it will be very helpful … .This is a potentially very useful book." (Journal of Applied Science, Dec 2008)

Review
"This heavy statistics book, by the author of The Flora of Berkshire, proved to be one of the best sellers at every conference in Europe that NHBS attended in 2007 - probably the only book on the subject you will ever need." 
– Anneli Meeder, Title Researcher & Cataloguer

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Customer Reviews

some flaws, but useful overall
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.

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.

The best R book I've found so far.
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.