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.

Autorentext

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.

Inhalt

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