Part I. R Basics
1. Getting and Installing R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
R Versions 3
Getting and Installing Interactive R Binaries 3
Windows 4
Mac OS X 5
Linux and Unix Systems 5
2. The R User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
The R Graphical User Interface 7
Windows 8
Mac OS X 8
Linux and Unix 9
The R Console 11
Command-Line Editing 13
Batch Mode 14
Using R Inside Microsoft Excel 14
Other Ways to Run R 16
3. A Short R Tutorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Basic Operations in R 19
Functions 21
Variables 22

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Part III. Working with Data
12. Saving, Loading, and Editing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Entering Data Within R 155
Entering Data Using R Commands 155
Using the Edit GUI 156
Saving and Loading R Objects 160
Saving Objects with save 160
Importing Data from External Files 161
Text Files 161
Other Software 170
Exporting Data 170
Importing Data from Databases 171
Export Then Import 171
Database Connection Packages 172
RODBC 173
DBI 184
TSDBI 188
13. Preparing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
Combining Data Sets 189
Pasting Together Data Structures 190
Merging Data by Common Fields 193
Transformations 195
Reassigning Variables 195
The Transform Function 196
Applying a Function to Each Element of an Object 196
Binning Data 200
Shingles 200
Cut 200
Combining Objects with a Grouping Variable 201
Subsets 202
Bracket Notation 202
subset Function 203
Random Sampling 203
Summarizing Functions 205
tapply, aggregate 205
Aggregating Tables with rowsum 208
Counting Values 209
Reshaping Data 211
Data Cleaning 217
Part IV. Statistics with R
16. Analyzing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
Summary Statistics 345
Correlation and Covariance 347
Principal Components Analysis 350
Factor Analysis 354
Bootstrap Resampling 355
17. Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357
Normal Distribution 357
Common Distribution-Type Arguments 360
Distribution Function Families 361
18. Statistical Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365
Continuous Data 365
Normal Distribution-Based Tests 366
Non-Parametric Tests 380
Discrete Data 383
Proportion Tests 383
Binomial Tests 384
Tabular Data Tests 385
Non-Parametric Tabular Data Tests 391
19. Power Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393
Experimental Design Example 393
t-Test Design 394
Proportion Test Design 395
ANOVA Test Design 396
20. Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399
Example: A Simple Linear Model 399
Fitting a Model 401
Helper Functions for Specifying the Model 402
Getting Information About a Model 402
Refining the Model 408
Details About the lm Function 408
Assumptions of Least Squares Regression 411
Robust and Resistant Regression 412
Subset Selection and Shrinkage Methods 415
Stepwise Variable Selection 415
Ridge Regression 416
Lasso and Least Angle Regression 417
Principal Components Regression and Partial Least Squares
Regression 418
Nonlinear Models 419
Generalized Linear Models 419
Nonlinear Least Squares 422
Survival Models 423
Smoothing 429
Splines 429
Fitting Polynomial Surfaces 431
Kernel Smoothing 432
Machine Learning Algorithms for Regression 432
Regression Tree Models 434
MARS 446
Neural Networks 452
Project Pursuit Regression 456
Generalized Additive Models 459
Support Vector Machines 461
21. Classification Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465
Linear Classification Models 465
Logistic Regression 465
Linear Discriminant Analysis 470
Log-Linear Models 474
Machine Learning Algorithms for Classification 475
k Nearest Neighbors 475
Classification Tree Models 477
Neural Networks 480
SVMs 481
Random Forests 482
22. Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483
Market Basket Analysis 483
Clustering 488
Distance Measures 488
Clustering Algorithms 489
23. Time Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493
Autocorrelation Functions 493
Time Series Models 494
24. Bioconductor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499
An Example 499
Loading Raw Expression Data 500
Loading Data from GEO 5

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