Data Analysis and Graphics Using R: An Example-Based Approach

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Cambridge University Press, May 6, 2010 - Computers
2 Reviews
Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.
 

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Seems this is the one book that comes to the top of the list when you start leaning R. So I started here. It's a review of the language and not data modelling. But obviously you need to learn the first one before you can use it to do the second one.
My only complaint is where can you find the datasets? For example:
package ‘MAAS’ is not available (for R version 3.2.1)
Not sure why someone would mess up R so that you can't use the Animals and other data sets mentioned in the book. Same with (austpop) but you can find that one the internet.
 

User Review - Flag as inappropriate

This book is very interesting, easily understood and provide good guidance
for statistics, especially in the exploration of data using graphs.

Contents

1 A brief introduction to R
1
2 Styles of data analysis
43
3 Statistical models
77
4 A review of inference concepts
102
5 Regression with a single predictor
142
6 Multiple linear regression
170
7 Exploiting the linear model framework
217
8 Generalized linear models and survival analysis
244
12 Multivariate data exploration and discrimination
377
13 Regression on principal component or discriminant scores
410
14 The R system additional topics
427
15 Graphs in R
472
Epilogue
493
References
495
Index of R symbols and functions
507
Index of terms
514

9 Time series models
283
10 Multilevel models and repeated measures
303
11 Treebased classification and regression
351

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About the author (2010)

John Maindonald is Visiting Fellow at the Mathematical Sciences Institute at the Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health to population genetics, machine learning, economic history, and forensic linguistics.

W. John Braun is Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists, and most recently has become involved with a network of forestry researchers.

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