1051 pages, colour & b/w illustrations, tables
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.
The R 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. The R Book is 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.
The second edition of The R Book:
- 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.
This replaces the 1st edition.
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
1 Getting Started 1
2 Essentials of the R Language 12
3 Data Input 137
4 Dataframes 159
5 Graphics 189
6 Tables 244
7 Mathematics 258
8 Classical Tests 344
9 Statistical Modelling 388
10 Regression 449
11 Analysis of Variance 498
12 Analysis of Covariance 537
13 Generalized Linear Models 557
14 Count Data 579
15 Count Data in Tables 599
16 Proportion Data 628
17 Binary Response Variables 650
18 Generalized Additive Models 666
19 Mixed-Effects Models 681
20 Non-linear Regression 715
21 Meta-analysis 740
22 Bayesian statistics 752
23 Tree Models 768
24 Time Series Analysis 785
25 Multivariate Statistics 809
26 Spatial Statistics 825
27 Survival Analysis 869
28 Simulation Models 893
29 Changing the Look of Graphics 907
References and Further Reading 971
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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.