The R Book (Google eBook)The highlevel 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 cuttingedge 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 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. 
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Review: The R Book
User Review  Sam  GoodreadsIt's hard to find a good RBook. This one did the job for me while I was learning, but you will be lost if you are not on rseek constantly filling the gaps. This book also requires a strong stomach ... Read full review
Review: The R Book
User Review  Peter Flom  GoodreadsThe best thing about this large book on R is its breadth: No one book can cover all of R, but this covers a lot. The biggest problem is the index and contents, which could have been better done and which could have made the book more usable Read full review
Contents
vii  
1  
9  
3 Data Input  97 
4 Dataframes  107 
5 Graphics  135 
6 Tables  183 
7 Mathematics  195 
16 Proportion Data  569 
17 Binary Response Variables  593 
18 Generalized Additive Models  611 
19 MixedEffects Models  627 
20 Nonlinear Regression  661 
21 Tree Models  685 
22 Time Series Analysis  701 
23 Multivariate Statistics  731 
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 
24 Spatial Statistics  749 
25 Survival Analysis  787 
26 Simulation Models  811 
27 Changing the Look of Graphics  827 
873  
877  