Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) [Hardcover]

John Maindonald (Author), John Braun (Author)

Book Description

ISBN-10: 0521813360 | ISBN-13: 978-0521813365 | Publication Date: August 4, 2003 | Edition: 1

Using modern statistical software systems requires training both in the software itself and in the underlying statistical methods. Concentrating on the freely available R system, this volume demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from their experience as teachers and as consultants. R code and data sets for all examples are available on the Internet. This emphasis on practical methodology combined with a tutorial approach makes the book accessible to anyone with a knowledge of undergraduate-level statistics. The methods demonstrated are suitable for use in a wide variety of disciplines, from social sciences to medicine, engineering and science.

Editorial Reviews

Review

"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

"I would strongly recommend the book to scientists who have already had a regression or a linear models course who wish to learn to use R."
R News

"There is a comprehensive introduction, a very useful chapter-by-chapter summary, and 12 chapters, supported by an appendix listing S-plus differences, references, indices of R symbols, functions, and terms."
Clinical Chemistry

"... An excellent intermediate-level text highly relevant to the BI world and suitable for readers with little more than an intro to stats background... Maindonald and Braun's exposition of the R language is nonetheless first rate."
Steve Miller, DM Review Online

"The book remains an excellent summary of R tools and its presented in a readable and clear manner that balances how-to with interpretation of the results obtained. This is an excellent reference book to have on your bookshelf, and would also be a good book from which to teach a course."
Christine M. Anderson-Cook, Los Alamos National Laboratory

Book Description

The R system is a powerful statistics system for researchers needing tools for data manipulation and presentation. Concentrating on examples drawn from the authors' teaching and consulting experience, this book is both a tutorial to the R system and an introduction to the underlying statistical methods. Starting with elementary concepts accessible to anyone who has taken a first undergraduate course in statistics the book leads on to more advanced topics of use to practising scientists and researchers as well as students in medicine, science, engineering and the social sciences.

See all Editorial Reviews

Product Details

• Hardcover: 386 pages
• Publisher: Cambridge University Press; 1 edition (August 4, 2003)
• Language: English
• ISBN-10: 0521813360
• ISBN-13: 978-0521813365
• Product Dimensions: 10.1 x 6.9 x 1.3 inches
• Shipping Weight: 1.8 pounds
• Average Customer Review: 3.9 out of 5 stars  See all reviews (11 customer reviews)
• Amazon Best Sellers Rank: #1,455,016 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

77 of 82 people found the following review helpful:

5.0 out of 5 stars Good introduction to R book, October 2, 2004

By 

R. Krause (Tempe, AZ) - See all my reviews
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This review is from: Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) (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.

50 of 52 people found the following review helpful:

4.0 out of 5 stars data analysis presented through R, February 19, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
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This review is from: Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) (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. The book can serve both as an introduction to data analysis and a tutorial on the R programming language. This can be useful as a text for undergraduate and graduate students. It is also an excellent reference for researchers who want to use R and its application to practical problems. The book also has an appendix that shows the relationship between R and S and SPlus, highlighting the differences. The first chapter is a careful introduction to R and the last chapter covers advanced applications in R.

The graphics used throughout the book are excellently presented and there are even a few color graphs. This text has just had a second edition published but my review is based on the 2003 version which is the one I purchased.

28 of 28 people found the following review helpful:

4.0 out of 5 stars Good both for reference and for learning R, March 28, 2006

By 

Daniel Dvorkin (Denver, CO) - See all my reviews
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This review is from: Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) (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.