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A start-to-finish guide to one of the most useful programming languages for researchers in a variety of fields
In the newly revised Third Edition of The R Book, a team of distinguished teachers and researchers delivers a user-friendly and comprehensive discussion of foundational and advanced topics in the R software language, which is used widely in science, engineering, medicine, economics, and other fields. The book is designed to be used as both a complete text – readable from cover to cover – and as a reference manual for practitioners seeking authoritative guidance on particular topics.
This latest edition offers instruction on the use of the RStudio GUI, an easy-to-use environment for those new to R. It provides readers with a complete walkthrough of the R language, beginning at a point that assumes no prior knowledge of R and very little previous knowledge of statistics. Readers will also find:
- A thorough introduction to fundamental concepts in statistics and step-by-step roadmaps to their implementation in R;
- Comprehensive explorations of worked examples in R;
- A complementary companion website with downloadable datasets that are used in the book;
- In-depth examination of essential R packages.
Perfect for undergraduate and postgraduate students of science, engineering, medicine economics, and geography, The R Book will also earn a place in the libraries of social sciences professionals.
Preface
1. Getting started 1
2. Technical background 17
3. Essentials of the R language 55
4. Data input and dataframes 195
5. Graphics 235
6. Graphics in more detail 289
7. Tables 357
8. Probability distributions in R 369
9. Testing 401
10. Regression 433
11. Generalised Linear Models 495
12. Generalised Additive Models 575
13. Mixed-effect models 599
14. Non-linear regression 627
15. Survival analysis 651
16. Designed experiments 669
17. Meta-analysis 701
18. Time Series 717
19. Multivariate Statistics 743
20. Classification and regression trees 765
21. Spatial Statistics 785
22. Bayesian Statistics 807
23. Simulation models 833
Dr Elinor Jones graduated in Mathematics and Statistics from the University of Warwick before completing a PhD in Probability Theory at the University of Manchester. Her thesis examined the large deviations of random walks and Lévy processes. Prior to joining University College London, where she is now a Senior Teaching Fellow, Elinor worked as a Research Associate in Genetic Epidemiology at the University of Leicester and as a Statistician at the University of Reading.
Dr Simon Harden originally studied Mathematics at Warwick and worked in finance and IT for a few years before taking an MSc and PhD at UCL, where he is now a Senior Teaching Fellow in the Department of Statistical Science, University College London.
Both Jones and Harden have taught R at all levels from school children to PhD students, to statisticians and non-statisticians, and have covered virtually all the chapters.
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
