Foreword
Preface
Acknowledgments
About the Author
1 Getting R
1.1 Downloading R
1.2 R Version
1.3 32-bit vs. 64-bit
1.4 Installing
1.5 Microsoft R Open
1.6 Conclusion
2 The R Environment
2.1 Command Line Interface
2.2 RStudio
2.3 Microsoft Visual Studio
2.4 Conclusion
3 R Packages
3.1 Installing Packages
3.2 Loading Packages
3.3 Building a Package
3.4 Conclusion
4 Basics of R
4.1 Basic Math
4.2 Variables
4.3 Data Types
4.4 Vectors
4.5 Calling Functions
4.6 Function Documentation
4.7 Missing Data
4.8 Pipes
4.9 Conclusion

5 Advanced Data Structures
5.1 data.frames
5.2 Lists
5.3 Matrices
5.4 Arrays
5.5 Conclusion
6 Reading Data into R
6.1 Reading CSVs
6.2 Excel Data
6.3 Reading from Databases
6.4 Data from Other Statistical Tools
6.5 R Binary Files
6.6 Data Included with R
6.7 Extract Data from Web Sites
6.8 Reading JSON Data
6.9 Conclusion
7 Statistical Graphics
7.1 Base Graphics
7.2 ggplot2
7.3 Conclusion
8 Writing R functions
8.1 Hello, World!
8.2 Function Arguments
8.3 Return Values
8.4 do.call
8.5 Conclusion
9 Control Statements
9.1 if and else
9.2 switch
9.3 ifelse
9.4 Compound Tests
9.5 Conclusion
10 Loops, the Un-R Way to Iterate
10.1 for Loops
10.2 while Loops
10.3 Controlling Loops

10.4 Conclusion
11 Group Manipulation
11.1 Apply Family
11.2 aggregate
11.3 plyr
11.4 data.table
11.5 Conclusion
12 Faster Group Manipulation with dplyr
12.1 Pipes
12.2 tbl
12.3 select
12.4 filter
12.5 slice
12.6 mutate
12.7 summarize
12.8 group_by
12.9 arrange
12.10 do
12.11 dplyr with Databases
12.12 Conclusion
13 Iterating with purrr
13.1 map
13.2 map with Specified Types
13.3 Iterating over a data.frame
13.4 map with Multiple Inputs
13.5 Conclusion
14 Data Reshaping
14.1 cbind and rbind
14.2 Joins
14.3 reshape2
14.4 Conclusion
15 Reshaping Data in the Tidyverse
15.1 Binding Rows and Columns
15.2 Joins with dplyr
15.3 Converting Data Formats

15.4 Conclusion
16 Manipulating Strings
16.1 paste
16.2 sprintf
16.3 Extracting Text
16.4 Regular Expressions
16.5 Conclusion
17 Probability Distributions
17.1 Normal Distribution
17.2 Binomial Distribution
17.3 Poisson Distribution
17.4 Other Distributions
17.5 Conclusion
18 Basic Statistics
18.1 Summary Statistics
18.2 Correlation and Covariance
18.3 T-Tests
18.4 ANOVA
18.5 Conclusion
19 Linear Models
19.1 Simple Linear Regression
19.2 Multiple Regression
19.3 Conclusion
20 Generalized Linear Models
20.1 Logistic Regression
20.2 Poisson Regression
20.3 Other Generalized Linear Models
20.4 Survival Analysis
20.5 Conclusion
21 Model Diagnostics
21.1 Residuals
21.2 Comparing Models
21.3 Cross-Validation
21.4 Bootstrap
21.5 Stepwise Variable Selection

21.6 Conclusion
22 Regularization and Shrinkage
22.1 Elastic Net
22.2 Bayesian Shrinkage
22.3 Conclusion
23 Nonlinear Models
23.1 Nonlinear Least Squares
23.2 Splines
23.3 Generalized Additive Models
23.4 Decision Trees
23.5 Boosted Trees
23.6 Random Forests
23.7 Conclusion
24 Time Series and Autocorrelation
24.1 Autoregressive Moving Average
24.2 VAR
24.3 GARCH
24.4 Conclusion
25 Clustering
25.1 K-means
25.2 PAM
25.3 Hierarchical Clustering
25.4 Conclusion
26 Model Fitting with Caret
26.1 Caret Basics
26.2 Caret Options
26.3 Tuning a Boosted Tree
26.4 Conclusion
27 Reproducibility and Reports with knitr
27.1 Installing a LaTeX Program
27.2 LaTeX Primer
27.3 Using knitr with LaTeX
27.4 Conclusion
28 Rich Documents with RMarkdown
28.1 Document Compilation
28.2 Document Header
28.3 Markdown Primer
28.4 Markdown Code Chunks
28.5 htmlwidgets
28.6 RMarkdown Slideshows
28.7 Conclusion
29 Interactive Dashboards with Shiny
29.1 Shiny in RMarkdown
29.2 Reactive Expressions in Shiny
29.3 Server and UI
29.4 Conclusion
30 Building R Packages
30.1 Folder Structure
30.2 Package Files
30.3 Package Documentation
30.4 Tests
30.5 Checking, Building and Installing
30.6 Submitting to CRAN
30.7 C++ Code
30.8 Conclusion
A Real-Life Resources
A.1 Meetups
A.2 Stack Overflow
A.3 Twitter
A.4 Conferences
A.5 Web Sites
A.6 Documents
A.7 Books
A.8 Conclusion
B Glossary
List of Figures
List of Tables
General Index
Index of Functions
Index of Packages