Essential to anyone doing data analysis with R, whether in industry or academia.

*R in Action, Second Edition* presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines.

## praise for the first edition

## preface

## acknowledgments

## about this book

## about the cover illustration

# Part 1 Getting started

## 1. Introduction to R

### 1.1. Why use R?

### 1.2. Obtaining and installing R

### 1.3. Working with R

#### 1.3.1. Getting started

#### 1.3.2. Getting help

#### 1.3.3. The workspace

#### 1.3.4. Input and output

### 1.4. Packages

#### 1.4.1. What are packages?

#### 1.4.2. Installing a package

#### 1.4.3. Loading a package

#### 1.4.4. Learning about a package

### 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

#### 2.2.1. Vectors

#### 2.2.2. Matrices

#### 2.2.3. Arrays

#### 2.2.4. Data frames

#### 2.2.5. Factors

#### 2.2.6. Lists

### 2.3. Data input

#### 2.3.1. Entering data from the keyboard

#### 2.3.2. Importing data from a delimited text file

#### 2.3.3. Importing data from Excel

#### 2.3.4. Importing data from XML

#### 2.3.5. Importing data from the web

#### 2.3.6. Importing data from SPSS

#### 2.3.7. Importing data from SAS

#### 2.3.8. Importing data from Stata

#### 2.3.9. Importing data from NetCDF

#### 2.3.10. Importing data from HDF5

#### 2.3.11. Accessing database management systems (DBMSs)

#### 2.3.12. Importing data via Stat/Transfer

### 2.4. Annotating datasets

#### 2.4.1. Variable labels

#### 2.4.2. Value labels

### 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

#### 3.3.1. Symbols and lines

#### 3.3.2. Colors

#### 3.3.3. Text characteristics

#### 3.3.4. Graph and margin dimensions

### 3.4. Adding text, customized axes, and legends

#### 3.4.1. Titles

#### 3.4.2. Axes

#### 3.4.3. Reference lines

#### 3.4.4. Legend

#### 3.4.5. Text annotations

#### 3.4.6. Math annotations

### 3.5. Combining graphs

#### 3.5.1. Creating a figure arrangement with fine control

### 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

#### 4.5.1. Recoding values to missing

#### 4.5.2. Excluding missing values from analyses

### 4.6. Date values

#### 4.6.1. Converting dates to character variables

#### 4.6.2. Going further

### 4.7. Type conversions

### 4.8. Sorting data

### 4.9. Merging datasets

#### 4.9.1. Adding columns to a data frame

#### 4.9.2. Adding rows to a data frame

### 4.10. Subsetting datasets

#### 4.10.1. Selecting (keeping) variables

#### 4.10.2. Excluding (dropping) variables

#### 4.10.3. Selecting observations

#### 4.10.4. The subset() function

#### 4.10.5. Random samples

### 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

#### 5.2.1. Mathematical functions

#### 5.2.2. Statistical functions

#### 5.2.3. Probability functions

#### 5.2.4. Character functions

#### 5.2.5. Other useful functions

#### 5.2.6. Applying functions to matrices and data frames

### 5.3. A solution for the data-management challenge

### 5.4. Control flow

#### 5.4.1. Repetition and looping

#### 5.4.2. Conditional execution

### 5.5. User-written functions

### 5.6. Aggregation and reshaping

#### 5.6.1. Transpose

#### 5.6.2. Aggregating data

#### 5.6.3. The reshape2 package

### 5.7. Summary

# Part 2 Basic methods

## 6. Basic graphs

### 6.1. Bar plots

#### 6.1.1. Simple bar plots

#### 6.1.2. Stacked and grouped bar plots

#### 6.1.3. Mean bar plots

#### 6.1.4. Tweaking bar plots

#### 6.1.5. Spinograms

### 6.2. Pie charts

### 6.3. Histograms

### 6.4. Kernel density plots

### 6.5. Box plots

#### 6.5.1. Using parallel box plots to compare groups

#### 6.5.2. Violin plots

### 6.6. Dot plots

### 6.7. Summary

## 7. Basic statistics

### 7.1. Descriptive statistics

#### 7.1.1. A menagerie of methods

#### 7.1.2. Even more methods

#### 7.1.3. Descriptive statistics by group

#### 7.1.4. Additional methods by group

#### 7.1.5. Visualizing results

### 7.2. Frequency and contingency tables

#### 7.2.1. Generating frequency tables

#### 7.2.2. Tests of independence

#### 7.2.3. Measures of association

#### 7.2.4. Visualizing results

### 7.3. Correlations

#### 7.3.1. Types of correlations

#### 7.3.2. Testing correlations for significance

#### 7.3.3. Visualizing correlations

### 7.4. T-tests

#### 7.4.1. Independent t-test

#### 7.4.2. Dependent t-test

#### 7.4.3. When there are more than two groups

### 7.5. Nonparametric tests of group differences

#### 7.5.1. Comparing two groups

#### 7.5.2. Comparing more than two groups

### 7.6. Visualizing group differences

### 7.7. Summary

# Part 3 Intermediate methods

## 8. Regression

### 8.1. The many faces of regression

#### 8.1.1. Scenarios for using OLS regression

#### 8.1.2. What you need to know

### 8.2. OLS regression

#### 8.2.1. Fitting regression models with lm()

#### 8.2.2. Simple linear regression

#### 8.2.3. Polynomial regression

#### 8.2.4. Multiple linear regression

#### 8.2.5. Multiple linear regression with interactions

### 8.3. Regression diagnostics

#### 8.3.1. A typical approach

#### 8.3.2. An enhanced approach

#### 8.3.3. Global validation of linear model assumption

#### 8.3.4. Multicollinearity

### 8.4. Unusual observations

#### 8.4.1. Outliers

#### 8.4.2. High-leverage points

#### 8.4.3. Influential observations

### 8.5. Corrective measures

#### 8.5.1. Deleting observations

#### 8.5.2. Transforming variables

#### 8.5.3. Adding or deleting variables

#### 8.5.4. Trying a different approach

### 8.6. Selecting the "best" regression model

#### 8.6.1. Comparing models

#### 8.6.2. Variable selection

### 8.7. Taking the analysis further

#### 8.7.1. Cross-validation

#### 8.7.2. Relative importance

### 8.8. Summary

## 9. Analysis of variance

### 9.1. A crash course on terminology

### 9.2. Fitting ANOVA models

#### 9.2.1. The aov() function

#### 9.2.2. The order of formula terms

### 9.3. One-way ANOVA

#### 9.3.1. Multiple comparisons

#### 9.3.2. Assessing test assumptions

### 9.4. One-way ANCOVA

#### 9.4.1. Assessing test assumptions

#### 9.4.2. Visualizing the results

### 9.5. Two-way factorial ANOVA

### 9.6. Repeated measures ANOVA

### 9.7. Multivariate analysis of variance (MANOVA)

#### 9.7.1. Assessing test assumptions

#### 9.7.2. Robust 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

#### 10.2.1. t-tests

#### 10.2.2. ANOVA

#### 10.2.3. Correlations

#### 10.2.4. Linear models

#### 10.2.5. Tests of proportions

#### 10.2.6. Chi-square tests

#### 10.2.7. Choosing an appropriate effect size in novel situations

### 10.3. Creating power analysis plots

### 10.4. Other packages

### 10.5. Summary

## 11. Intermediate graphs

### 11.1. Scatter plots

#### 11.1.1. Scatter-plot matrices

#### 11.1.2. High-density scatter plots

#### 11.1.3. 3D scatter plots

#### 11.1.4. Spinning 3D scatter plots

#### 11.1.5. Bubble plots

### 11.2. Line charts

### 11.3. Corrgrams

### 11.4. Mosaic plots

### 11.5. Summary

## 12. Resampling statistics and bootstrapping

### 12.1. Permutation tests

### 12.2. Permutation tests with the coin package

#### 12.2.1. Independent two-sample and k-sample tests

#### 12.2.2. Independence in contingency tables

#### 12.2.3. Independence between numeric variables

#### 12.2.4. Dependent two-sample and k-sample tests

#### 12.2.5. Going further

### 12.3. Permutation tests with the lmPerm package

#### 12.3.1. Simple and polynomial regression

#### 12.3.2. Multiple regression

#### 12.3.3. One-way ANOVA and ANCOVA

#### 12.3.4. Two-way ANOVA

### 12.4. Additional comments on permutation tests

### 12.5. Bootstrapping

### 12.6. Bootstrapping with the boot package

#### 12.6.1. Bootstrapping a single statistic

#### 12.6.2. Bootstrapping several statistics

### 12.7. Summary

# Part 4 Advanced methods

## 13. Generalized linear models

### 13.1. Generalized linear models and the glm() function

#### 13.1.1. The glm() function

#### 13.1.2. Supporting functions

#### 13.1.3. Model fit and regression diagnostics

### 13.2. Logistic regression

#### 13.2.1. Interpreting the model parameters

#### 13.2.2. Assessing the impact of predictors on the probability of an outcome

#### 13.2.3. Overdispersion

#### 13.2.4. Extensions

### 13.3. Poisson regression

#### 13.3.1. Interpreting the model parameters

#### 13.3.2. Overdispersion

#### 13.3.3. Extensions

### 13.4. Summary

## 14. Principal components and factor analysis

### 14.1. Principal components and factor analysis in R

### 14.2. Principal components

#### 14.2.1. Selecting the number of components to extract

#### 14.2.2. Extracting principal components

#### 14.2.3. Rotating principal components

#### 14.2.4. Obtaining principal components scores

### 14.3. Exploratory factor analysis

#### 14.3.1. Deciding how many common factors to extract

#### 14.3.2. Extracting common factors

#### 14.3.3. Rotating factors

#### 14.3.4. Factor scores

#### 14.3.5. Other EFA-related packages

### 14.4. Other latent variable models

### 14.5. Summary

## 15. Time series

### 15.1. Creating a time-series object in R

### 15.2. Smoothing and seasonal decomposition

#### 15.2.1. Smoothing with simple moving averages

#### 15.2.2. Seasonal decomposition

### 15.3. Exponential forecasting models

#### 15.3.1. Simple exponential smoothing

#### 15.3.2. Holt and Holt-Winters exponential smoothing

#### 15.3.3. The ets() function and automated forecasting

### 15.4. ARIMA forecasting models

#### 15.4.1. Prerequisite concepts

#### 15.4.2. ARMA and ARIMA models

#### 15.4.3. Automated ARIMA forecasting

### 15.5. Going further

### 15.6. Summary

## 16. Cluster analysis

### 16.1. Common steps in cluster analysis

### 16.2. Calculating distances

### 16.3. Hierarchical cluster analysis

### 16.4. Partitioning cluster analysis

#### 16.4.1. K-means clustering

#### 16.4.2. Partitioning around medoids

### 16.5. Avoiding nonexistent clusters

### 16.6. Summary

## 17. Classification

### 17.1. Preparing the data

### 17.2. Logistic regression

### 17.3. Decision trees

#### 17.3.1. Classical decision trees

#### 17.3.2. Conditional inference trees

### 17.4. Random forests

### 17.5. Support vector machines

#### 17.5.1. Tuning an SVM

### 17.6. Choosing a best predictive solution

### 17.7. Using the rattle package for data mining

### 17.8. Summary

## 18. Advanced methods for missing data

### 18.1. Steps in dealing with missing data

### 18.2. Identifying missing values

### 18.3. Exploring missing-values patterns

#### 18.3.1. Tabulating missing values

#### 18.3.2. Exploring missing data visually

#### 18.3.3. Using correlations to explore missing values

### 18.4. Understanding the sources and impact of missing data

### 18.5. Rational approaches for dealing with incomplete data

### 18.6. Complete-case analysis (listwise deletion)

### 18.7. Multiple imputation

### 18.8. Other approaches to missing data

#### 18.8.1. Pairwise deletion

#### 18.8.2. Simple (nonstochastic) imputation

### 18.9. Summary

# Part 5 Expanding your skills

## 19. Advanced graphics with ggplot2

### 19.1. The four graphics systems in R

### 19.2. An introduction to the ggplot2 package

### 19.3. Specifying the plot type with geoms

### 19.4. Grouping

### 19.5. Faceting

### 19.6. Adding smoothed lines

### 19.7. Modifying the appearance of ggplot2 graphs

#### 19.7.1. Axes

#### 19.7.2. Legends

#### 19.7.3. Scales

#### 19.7.4. Themes

#### 19.7.5. Multiple graphs per page

### 19.8. Saving graphs

### 19.9. Summary

## 20. Advanced programming

### 20.1. A review of the language

#### 20.1.1. Data types

#### 20.1.2. Control structures

#### 20.1.3. Creating functions

### 20.2. Working with environments

### 20.3. Object-oriented programming

#### 20.3.1. Generic functions

#### 20.3.2. Limitations of the S3 model

### 20.4. Writing efficient code

### 20.5. Debugging

#### 20.5.1. Common sources of errors

#### 20.5.2. Debugging tools

#### 20.5.3. Session options that support debugging

### 20.6. Going further

### 20.7. Summary

## 21. Creating a package

### 21.1. Nonparametric analysis and the npar package

#### 21.1.1. Comparing groups with the npar package

### 21.2. Developing the package

#### 21.2.1. Computing the statistics

#### 21.2.2. Printing the results

#### 21.2.3. Summarizing the results

#### 21.2.4. Plotting the results

#### 21.2.5. Adding sample data to the package

### 21.3. Creating the package documentation

### 21.4. Building the package

### 21.5. Going further

### 21.6. Summary

## 22. Creating dynamic reports

### 22.1. A template approach to reports

### 22.2. Creating dynamic reports with R and Markdown

### 22.3. Creating dynamic reports with R and LaTeX

### 22.4. Creating dynamic reports with R and Open Document

### 22.5. Creating dynamic reports with R and Microsoft Word

### 22.6. Summary

## 23. Advanced graphics with the lattice package — *bonus chapter online only*

### 23.1. The lattice package

### 23.2. Conditioning variables

### 23.3. Panel functions

### 23.4. Grouping variables

### 23.5. Graphic parameters

### 23.6. Customizing plot strips

### 23.7. Page arrangement

### 23.8. Going further

## afterword Into the rabbit hole

## Appendix A: Graphical user interfaces

## Appendix B: Customizing the startup environment

## Appendix C: Exporting data from R

## Appendix D: Matrix algebra in R

## Appendix E: Packages used in this book

## Appendix F: Working with large datasets

### F.1. Efficient programming

### F.2. Storing data outside of RAM

### F.3. Analytic packages for out-of-memory data

### F.4. Comprehensive solutions for working with enormous datasets

## Appendix G: Updating an R installation

### G.1. Automated installation (Windows only)

### G.2. Manual installation (Windows and Mac OS X)

### G.3. Updating an R installation (Linux)

## references

## index

## About the Technology

Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you’re likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide.

## About the book

*R in Action, Second Edition* teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You’ll also master R’s extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing.

## What's inside

- Complete R language tutorial
- Using R to manage, analyze, and visualize data
- Techniques for debugging programs and creating packages
- OOP in R
- Over 160 graphs

## About the reader

This book is designed for readers who need to solve practical data analysis problems using the R language and tools. Some background in mathematics and statistics is helpful, but no prior experience with R or computer programming is required.

## About the author

**Dr. Rob Kabacoff** is a seasoned researcher who specializes in data analysis. He has taught graduate courses in statistical programming and manages the Quick-R website at statmethods.net.