Synopses & Reviews
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.
- 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)
The R language is recognized as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R is becoming essential both to carry out research and to understand it, as more and more people present their results in the context of R.
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. The format enables it to be either read as a text, or dipped-into as a reference manual.
The early chapters assume no background in statistics or computing, and introduce the reader to the basic concepts involved. In this way the reader is introduced to the assumptions that lie behind the tests, fostering a critical approach to statistical modeling. These early chapters have been thoroughly updated to take account of the way language has evolved since the publication of the first edition. Subsequent chapters examine more advanced topics, cementing what is learnt in the opening chapters, as well as benefiting more intermediate readers. Throughout the book, the reader's experience is furthered by practical guidance and the inclusion of numerous worked examples.
Table of Contents
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