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Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) 2nd Edition

4.1 out of 5 stars 14 customer reviews
ISBN-13: 978-0521861168
ISBN-10: 0521861160
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Editorial Reviews


From reviews of previous edition: "The strength of the book is in the extensive examples of practical data analysis with complete examples of the R code necessary to carry out the analyses. I would strongly recommend the book to scientists who have already had a regression or a linear models course and who wish to learn to use R. I give it a strong recommendation to the scientist or data analyst who wishes to an easy-to-read and an understandable reference on the use of R for practical data analysis."
R News

From reviews of previous edition: "The text includes a wealth of practical examples, drawn from a variety of practical applications which should be easily understood by the reader. The methods demonstrated are suitable for use in areas such as biology, social science, medicine and engineering. The core of the book is taken up with detailed discussion of regression methods which leads onto more advanced statistical concepts."
ISI Short Book Reviews

From reviews of previous edition: "This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines; the book's writing style is very readable, with clear explanations and precise introductions of all topics and terminology; the book also provides a wealth of examples from various physical and social sciences, engineering, and medicine that have been effectively chosen to illustrate not only the basics of the statistical methods, but also some of the interesting subtleties of the analyses that may require careful interpretation and discussion. I believe that they have created a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web page of resources complements the book's usefulness for a road audience of statisticians and practitioners."

From Previous Edition: "Provide considerable insight into very powerful procedures."
A. Ralph Henderson, Clinical Chemistry

"There are many books published in applied statistics that explain the R language. However, the book under review stands out due to its versatility and because it is easy to follow and understand the context."
Ita Cirovic Donev, The Mathematical Association of America

"...A gentle tour guide for new R users, aiming to help them navigate through many powerful tools that the open source R system offers."
Zhaohui Steve Qin, Center for Statistical Genetics, BioInformatics

"The style of the book is a commendable "learn by example" - each of the many statistical techniques is centered on real-world examples. The collective of topics is eclectic and the book also comes with extensive R code."
Carl James Schwarz, Biometrics

Book Description

Emphasising hands-on analysis, graphical display and interpretation of data, this is a guide to the practical tools that the R system provides. A website provides computer code and data sets, allowing readers to reproduce all analyses. For research scientists, final-year undergraduate or graduate students of applied statistics, and practising statisticians.
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Product Details

  • Series: Cambridge Series in Statistical and Probabilistic Mathematics (Book 10)
  • Hardcover: 528 pages
  • Publisher: Cambridge University Press; 2 edition (December 26, 2006)
  • Language: English
  • ISBN-10: 0521861160
  • ISBN-13: 978-0521861168
  • Product Dimensions: 6.8 x 1.3 x 9.7 inches
  • Shipping Weight: 2.4 pounds
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #1,901,846 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By R. Krause on October 2, 2004
Format: Hardcover
This is a good introductory book to R, covering basics of the language, statistical models, inference, regression (linear and logistic), experimental design, time series, classification, multivariate analysis, etc.

The book uses liberally examples and in most cases has the code for the output or graphics as footnotes at the bottom of the page. The book also tries to teach the statistics to a degree, which one can see as an annoyance (just teach me R!) or helpful if you are shaky on your stats. The book also lists a fair number of references to other books on S-plus and R to help point you in the direction towards achieving a higher level of adeptness and other references to learn more about the topics covered in the book.

The book also has exercises at the end of each chapter to get you into R and using the system. The answers to the exercises are not in the book, but are available in pdf format on the books corresponding website.
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Format: Hardcover
The authors have written a very good and somewhat unique book on statistical data analysis. The emphasis is on linear models. graphics and diagnostics for identifying violations of modeling assumprions. They build up from the basics starting with simple one variable linear regression and correlation and then moving to multiple regression. Special cases of linear models suchas polynomial regression are presened. They then move on to various generalizations. When the residuals are correlated they consider time series models for the correlation structure of the residuals. Other specialized and important problems such as repeated measures for longitudinal data are covered.

Logistic regression is also introduced and shown to be a member of a larger class of models called generalized linear models which differ from linear models in that the dependent variable is a transformation of the basic dependent variable. The transformation is called the link function. For logistic regression the transformation is called the logit function. Hierarchical (or multi-level)models are also considered.

There is also a chapter on classification and regression trees. The final methods chapter covers multvariate analysis including classifcation, principal components,and propensity scores. These are topics not commonly seen in a first course on regression or data analysis.

What makes the book unique is a thorough introduction to the R programming language and the presntation of every technique with examples in R that both motivate the need for the technique and the details of the implementation in R. There is a lot of R code given and references to a variety of sources for R that can be found on the internet.
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Format: Hardcover
This is a great book for people under pressure (I'm a first-year biostat grad student, so I know whereof I speak) who want to get into doing serious data analysis quickly using R. It's also a good reference once you know the language better. The only reason I didn't give it five stars is that the organization is a little confusing, particularly when you're trying to find sample code to produce a particular figure or analysis -- overall, though, I think it's the best R-specific book out there for the general user.
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Format: Hardcover Verified Purchase
I found this book to be quite useful for learning R, and for pointing out the pitfalls for new users. It's especially good to know that there is a website associated with the book that will allow you to download the code used in the book.

There are several good free R resources out there, but in the end I think you get what you pay for. In this case it was nice to have a hard-bound reference with an index and appendix that I could highlight and dog-ear.

I mostly used it as a book for learning R, and not as a stats book. I did notice that there were many good examples of common statistical applications, such as t-stat tests, residual plotting, and the like. In other words, I feel like I got my money's worth by just using a few chapters and the appendix.
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Format: Hardcover Verified Purchase
I am using this book as my main resource to learn R from scratch. I had no prior experience with the program. The text is easy to read, and gives you just enough statistical theory to understand the operations without weighing you down with overly-difficult concepts. Many useful 'references for further reading' are scattered throughout if you want to know more about any particular method or operation. The book has an accompanying website with examples of code used for all the figures in the book, solutions to selected exercises, and other helpful things. Advanced topics/sections are marked with an asterisk, indicating that a first-time reader may skip over them until a later date. Overall, the book is very explicit about the code used for all the examples, allowing for easy adaptation to the users' purposes. The exercises at the end of every chapter can be quite challenging, as they often build on concepts presented in the chapter rather than simply reviewing the material. The index is very good (there is one for terms and one for R symbols and functions). Overall, the book is pretty user-friendly for a novice like me, and it covers a broad range of methods of data analysis.
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