contents

preface xv
acknowledgments xvii

## Part I Getting Started

1 Introduction to R
1.1 Why use R?
1.2 Obtaining and installing R
1.3 Working with R
1.4 Packages
1.5 Batch processing
1.6 Using output as input—reusing results
1.7 Working with large datasets
1.8 Working through an example
1.9 Summary
2 Creating a dataset
2.1 Understanding datasets
2.2 Data structures
2.3 Data input
2.4 Annotating datasets
2.5 Useful functions for working with data objects
2.6 Summary
3 Getting started with graphs
3.1 Working with graphs
3.2 A simple example
3.3 Graphical parameters
3.4 Adding text, customized axes, and legends
3.5 Combining graphs
3.6 Summary
4 Basic data management
4.1 A working example
4.2 Creating new variables
4.3 Recoding variables
4.4 Renaming variables
4.5 Missing values
4.6 Date values
4.7 Type conversions
4.8 Sorting data
4.9 Merging datasets
4.10 Subsetting datasets
4.11 Using SQL statements to manipulate data frames
4.12 Summary
5.1 A data management challenge
5.2 Numerical and character functions
5.3 A solution for our data management challenge
5.4 Control flow
5.5 User-written functions
5.6 Aggregation and restructuring
5.7 Summary

## Part II Basic Methods

6 Basic graphs
6.1 Bar plots
6.2 Pie charts
6.3 Histograms
6.4 Kernel density plots
6.5 Box plots
6.6 Dot plots
6.7 Summary
7 Basic statistics
7.1 Descriptive statistics
7.2 Frequency and contingency tables
7.3 Correlations
7.4 t-tests
7.5 Nonparametric tests of group differences
7.6 Visualizing group differences
7.7 Summary

## Part III Intermediate Methods

8 Regression
8.1 The many faces of regression
8.2 OLS regression
8.3 Regression diagnostics
8.4 Unusual observations
8.5 Corrective measures
8.6 Selecting the “best” regression model
8.7 Taking the analysis further
8.8 Summary
9 Analysis of variance
9.1 A crash course on terminology
9.2 Fitting ANOVA models
9.3 One-way ANOVA
9.4 One-way ANCOVA
9.5 Two-way factorial ANOVA
9.6 Repeated measures ANOVA
9.7 Multivariate analysis of variance (MANOVA)
9.8 ANOVA as regression
9.9 Summary
10 Power analysis
10.1 A quick review of hypothesis testing
10.2 Implementing power analysis with the pwr package
10.3 Creating power analysis plots
10.4 Other packages
10.5 Summary
11 Intermediate graphs
11.1 Scatter plots
11.2 Line charts
11.3 Correlograms
11.4 Mosaic plots
11.5 Summary
12 Resampling statistics and bootstrapping
12.1 Permutation tests
12.2 Permutation test with the coin package
12.3 Permutation tests with the lmPerm package
12.5 Bootstrapping
12.6 Bootstrapping with the boot package
12.7 Summary

13 Generalized linear models
13.1 Generalized linear models and the glm() function
13.2 Logistic regression
13.3 Poisson regression
13.4 Summary
14 Principal components and factor analysis
14.1 Principal components and factor analysis in R
14.2 Principal components
14.3 Exploratory factor analysis
14.4 Other latent variable models
14.5 Summary
15 Advanced methods for missing data
15.1 Steps in dealing with missing data
15.2 Identifying missing values
15.3 Exploring missing values patterns
15.4 Understanding the sources and impact of missing data
15.5 Rational approaches for dealing with incomplete data
15.6 Complete-case analysis (listwise deletion)
15.7 Multiple imputation
15.8 Other approaches to missing data
15.9 Summary
16.1 The four graphic systems in R
16.2 The lattice package
16.3 The ggplot2 package
16.4 Interactive graphs
16.5 Summary

afterword Into the rabbit hole
appendix A Graphic user interfaces
appendix B Customizing the startup environment
appendix C Exporting data from R
appendix D Creating publication-quality output
appendix E Matrix Algebra in R
appendix F Packages used in this book
appendix G Working with large datasets
appendix H Updating an R installation
index