500 pages, 58 illustrations, 26 tables
Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, Data Analysis and Graphics Using R guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available.
Assuming only basic statistical knowledge, Data Analysis and Graphics Using R 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 revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.
From reviews of the previous edition
"The strength of the book is in the extensive examples of practical data analysis with complete examples of the R code necessary to carry out the analyses [...] I would strongly recommend the book to scientists who have already had a regression or a linear models course and who wish to learn to use R [...] I give it a strong recommendation to the scientist or data analyst who wishes to have an easy-to-read and an understandable reference on the use of R for practical data analysis."
- R News
"This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines [...] the book's writing style is very readable, with clear explanations and precise introductions of all topics and terminology [...] the book also provides a wealth of examples from various physical and social sciences, engineering, and medicine that have been effectively chosen to illustrate not only the basics of the statistical methods, but also some of the interesting subtleties of the analyses that may require careful interpretation and discussion [...] I believe that they have [...] created a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a road audience of statisticians and practitioners."
"This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines [...] With its focus on ideas and concepts, rather than an extensive formula-based presentation, the book finds a nice balance between discussing statistical concepts and teaching the basics of the freely-available statistical package R [...] a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a broad audience of statisticians and practitioners."
- Journal of the American Statistical Association
"[...] a very useful book that can be recommended for applied statisticians and other scientists who want to use R for data analysis, and as a textbook for an applied statistics course using R."
- Journal of Applied Statistics
"[...] an excellent intermediate-level text [...] Though a bit more terse than Dalgaard's Introductory Statistics with R, Maindonald and Braun's exposition of the R language is nonetheless first rate."
- DM Review Online
1. A brief introduction to R
2. Styles of data analysis
3. Statistical models
4. An introduction to formal inference
5. Regression with a single predictor
6. Multiple linear regression
7. Exploiting the linear model framework
8. Generalized linear models and survival analysis
9. Time series models
10. Multi-level models and repeated measures
11. Tree-based classification and regression
12. Multivariate data exploration and discrimination
13. Regression on principal component or discriminant scores
14. The R system - additional topics
Epilogue - models
Index of R symbols and functions
Index of terms
Index of names
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John Maindonald is Visiting Fellow at the Mathematical Sciences Institute, 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.
John Braun is Associate Professor of Statistical and Actuarial Sciences, University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists and most recently has become involved with a network of forestry researchers.