Elinor Jones, PhD, is an Associate Professor (Teaching) in the Department of Statistical Science at University College London. She is an experienced teacher with a background in statistics consultancy in a range of fields.
 
Simon Harden, PhD, is an Associate Professor (Teaching) in the Department of Statistical Science at University College London. He has taught R and statistics to people with a wide range of backgrounds, and has experience working in finance and IT.
 
Michael J Crawley FRS is Emeritus Professor of Plant Ecology at Imperial College London.

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

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.