R in Action, Second Edition
Data analysis and graphics with R
Robert I. Kabacoff
  • May 2015
  • ISBN 9781617291388
  • 608 pages
  • printed in black & white

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

Cristofer Weber, NeoGrid

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.

Listen to this book in liveAudio! liveAudio integrates a professional voice recording with the book’s text, graphics, code, and exercises in Manning’s exclusive liveBook online reader. Use the text to search and navigate the audio, or download the audio-only recording for portable offline listening. You can purchase or upgrade to liveAudio here or in liveBook.

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.

Table of Contents detailed table of contents

praise for the first edition



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

14.4. 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)



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.

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