- 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 Advanced data management
- 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

- 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

- 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
- 12.4 Additional comments on permutation tests
- 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 Advanced graphics
- 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*