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 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

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

12.4 Additional comments on permutation tests

12.5 Bootstrapping

12.6 Bootstrapping with the boot package

12.7 Summary

Part IV Advanced Methods

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