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 Bookis 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 Bookis 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.

After reviewing the basic syntax of the R language for data analysis, this reference explains the R functions for calculating a variety of statistical techniques spanning all the way from elementary classical tests, through regression and analysis of variance and generalized linear modeling, up to spatial statistics, multivariate methods, tree models, and time series analysis. Crawley (Imperial College) focuses on the assumptions behind the tests, fostering a crucial approach to statistical modeling without requiring statistical theory and advanced mathematics. The final chapter provides instructions for changing the look of graphics. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

"…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." (Statistica2008)

"…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)

"This heavy statistics book, by the author of#LINK<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.

"…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." (Statistica2008)

"…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)

"This heavy statistics book, by the author of#LINK<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.

"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)

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 Bookis 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 Bookis 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.

Michael Crawley is Professor at Imperial College at Silwood Park. He is a fellow of the Royal Society and author of the bestselling titles Statistics: An Introduction using R and Statistical Computing: An Introduction to Data Analysis Using S-Plus.

Preface.

1 Getting Started.

2 Essentials of the R Language.

3 Data Input.

4 Dataframes.

5 Graphics.

6 Tables.

7 Mathematics.

8 Classical Tests.

9 Statistical Modelling.

10 Regression.

11 Analysis of Variance.

12 Analysis of Covariance.

13 Generalized Linear Models.

14 Count Data.

15 Count Data in Tables.

16 Proportion Data.

17 Binary Response Variables.

18 Generalized Additive Models.

19 Mixed-Effects Models.

20 Non-linear Regression.

21 Tree Models.

22 Time Series Analysis.

23 Multivariate Statistics.

24 Spatial Statistics.

25 Survival Analysis.

26 Simulation Models.

27 Changing the look of graphics.

References and Further Reading.

Index.