950 pages, Illustrations
Please note: The second edition of The R Book has been published.
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 Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. 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.
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 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.a
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
2 Essentials of the R Language
3 Data Input
8 Classical Tests
9 Statistical Modelling
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
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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.