R in a Nutshell

Preface

Part 1: R Basics
1. Getting and Installing R
-R Versions
-Getting and Installing Interactive R Binaries

2. The R User Interface
-The R Graphical User Interface
-The R Console
-Batch Mode
-Using R Inside Microsoft Excel
-RStudio
-Other Ways to Run R

3. A Short R Tutorial
-Basic Operations in R
-Functions
-Variables
-Introduction to Data Structures
-Objects and Classes
-Models and Formulas
-Charts and Graphics
-Getting Help

4. R Packages
-An Overview of Packages
-Listing Packages in Local Libraries
-Loading Packages
-Exploring Package Repositories
-Installing Packages From Other Repositories
-Custom Packages

Part 2: The R Language
5. An Overview of the R Language
-Expressions
-Objects
-Symbols
-Functions
-Objects Are Copied in Assignment Statements
-Everything in R Is an Object
-Special Values
-Coercion
-The R Interpreter
-Seeing How R Works

6. R Syntax
-Constants
-Operators
-Expressions
-Control Structures
-Accessing Data Structures
-R Code Style Standards

7. R Objects
-Primitive Object Types
-Vectors
-Lists
-Other Objects
-Attributes

8. Symbols and Environments
-Symbols
-Working with Environments
-The Global Environment
-Environments and Functions
-Exceptions

9. Functions
-The Function Keyword
-Arguments
-Return Values
-Functions as Arguments
-Argument Order and Named Arguments
-Side Effects

10. Object-Oriented Programming
-Overview of Object-Oriented Programming in R
-Object-Oriented Programming in R: S4 Classes
-Old-School OOP in R: S3

Part 3: Working with Data
11. Saving, Loading, and Editing Data
-Entering Data Within R
-Saving and Loading R Objects
-Importing Data from External Files
-Exporting Data
-Importing Data From Databases
-Getting Data from Hadoop

12. Preparing Data
-Combining Data Sets
-Transformations
-Binning Data
-Subsets
-Summarizing Functions
-Data Cleaning
-Finding and Removing Duplicates
-Sorting

Part 4: Data Visualization
13. Graphics
-An Overview of R Graphics
-Graphics Devices
-Customizing Charts

14. Lattice Graphics
-History
-An Overview of the Lattice Package
-High-Level Lattice Plotting Functions
-Customizing Lattice Graphics
-Low-Level Functions

15. ggplot2
-A Short Introduction
-The Grammar of Graphics
-A More Complex Example: Medicare Data
-Quick Plot
-Creating Graphics with ggplot2
-Learning More

Part 5: Statistics with R
16. Analyzing Data
-Summary Statistics
-Correlation and Covariance
-Principal Components Analysis
-Factor Analysis
-Bootstrap Resampling

17. Probability Distributions
-Normal Distribution
-Common Distribution-Type Arguments
-Distribution Function Families

18. Statistical Tests
-Continuous Data
-Discrete Data

19. Power Tests
-Experimental Design Example
-t-Test Design
-Proportion Test Design
-ANOVA Test Design

20. Regression Models
-Example: A Simple Linear Model
-Details About the lm Function
-Subset Selection and Shrinkage Methods
-Nonlinear Models
-Survival Models
-Smoothing
-Machine Learning Algorithms for Regression

21. Classification Models
-Linear Classification Models
-Machine Learning Algorithms for Classification

22. Machine Learning
-Market Basket Analysis
-Clustering

23. Time Series Analysis
-Autocorrelation Functions
-Time Series Models

Part 6: Additional Topics
24. Optimizing R Programs
-Measuring R Program Performance
-Optimizing Your R Code
-Other Ways to Speed Up R

25. Bioconductor
-An Example
-Key Bioconductor Packages
-Data Structures
-Where to Go Next

26. R and Hadoop
-R and Hadoop
-Other Packages for Parallel Computation with R
-Where to Learn More

Appendix: R Reference

Bibliography

Index