"Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition:' ... 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, August 2008)'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, July 2007) "-- Provided by publisher.

"This edition introduces the advantages of the R environment, in a user-friendly format, to beginners and intermediate users in a range of disciplines, from science and engineering to medicine and economics"-- Provided by publisher.

Includes bibliographical references and index.

Machine generated contents note: Preface vii 1 Getting Started 1 2 Essentials of the R Language 9 3 Data Input 97 4 Dataframes 107 5 Graphics 135 6 Tables 183 7 Mathematics 195 8 Classical Tests 279 9 Statistical Modelling 323 10 Regression 387 11 Analysis of Variance 449 12 Analysis of Covariance 489 13 Generalized Linear Models 511 14 Count Data 527 15 Count Data in Tables 549 16 Proportion Data 569 17 Binary Response Variables 593 18 Generalized Additive Models 611 19 Mixed-Effects Models 627 20 Non-linear Regression 661 21 Meta-analysis xxx 22 Bayesian statistics xxx 23 Tree Models 685 24 Time Series Analysis 701 25 Multivariate Statistics 731 26 Spatial Statistics 749 27 Survival Analysis 787 28 Simulation Models 811 29 Changing the Look of Graphics 827 References and Further Reading 873 Index 877s.

Print version record and CIP data provided by publisher.

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. Meta-analysis -- 22. Bayesian statistics -- 23. Tree Models -- 24. Time Series Analysis -- 25. Multivariate Statistics -- 26. Spatial Statistics -- 27. Survival Analysis -- 28. Simulation Models -- 29. Changing the Look of Graphics.