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The R Book

Michael J. Crawley
John Wiley
Publication Date: 
Number of Pages: 
[Reviewed by
Ita Cirovic Donev
, on

The R language is extremely popular within the statistical community. There are numerous reasons for this; probably the most important are the versatility of the language (and R software for that matter), its similarity with the S language, and the fact that it is free, which enables a much larger user community.

The R Book is sort of a reference book on the usage of R language. It is not really a manual, as it does not go into great details of the usage of R; it just explains the main concepts (the basics). However, as the book is intended for beginners, I think this is a more than suitable approach. The author covers almost all concepts needed for a beginner or intermediate user. There are many statistical methods introduced, from regression to simulation models. The style of presentation is very clear. It includes little bit of theory (just enough to explain the following R code), then the R code demonstrating the main topic of the chapter and suitable small examples embedded in the code. The output is presented either graphically or numerically. depending on the subject at hand. An explanation of the output, including statistical methods, is given in every chapter.

This is an excellent book for a beginner and for an intermediate reader who whishes to use R language for statistical analysis. With such clear presentation of the topics, one can really be handy in R after completing this book. Thus, this could be an extremely useful reference for undergraduate statistical projects. The R Book will provide the reader with the necessary background on the R language and it usage. However, for any advanced computations the reader should look for other references, the main one being the help files of R itself.

Ita Cirovic Donev is a PhD candidate at the University of Zagreb. She hold a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical mehods of credit and market risk. Apart from the academic work she does consulting work for financial institutions.


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 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.