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.
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.
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.
- customers also bought these items
- Big Data
- Deep Learning with R
- Beyond Spreadsheets with R
- Think Like a Data Scientist
- The Quick Python Book, Third Edition
- Introducing Data Science
placing your order...
Don't refresh or navigate away from the page.FREE domestic shipping on three or more pBooks