R in Action: Data Analysis and Graphics with R

Front Cover......Page 1
brief contents......Page 6
contents......Page 8
preface......Page 16
acknowledgments......Page 18
about this book......Page 20
Code conventions......Page 23
About the author......Page 24
Roadmap......Page 21
about the cover illustration......Page 25
Part 1 Getting started......Page 26
1 Introduction to R......Page 28
1.1 Why use R?......Page 30
1.3 Working with R......Page 32
1.3.1 Getting started......Page 33
1.3.3 The workspace......Page 36
1.3.4 Input and output......Page 38
1.4 Packages......Page 39
1.4.1 What are packages?......Page 40
1.4.4 Learning about a package......Page 41
1.5 Batch processing......Page 42
1.8 Working through an example......Page 43
1.9 Summary......Page 45
2 Creating a dataset......Page 46
2.1 Understanding datasets......Page 47
2.2 Data structures......Page 48
2.2.2 Matrices......Page 49
2.2.3 Arrays......Page 51
2.2.4 Data frames......Page 52
2.2.5 Factors......Page 55
2.2.6 Lists......Page 57
2.3 Data input......Page 58
2.3.1 Entering data from the keyboard......Page 59
2.3.2 Importing data from a delimited text file......Page 60
2.3.3 Importing data from Excel......Page 61
2.3.5 Webscraping......Page 62
2.3.8 Importing data from Stata......Page 63
2.3.11 Accessing database management systems (DBMSs)......Page 64
2.3.12 Importing data via Stat/Transfer......Page 66
2.5 Useful functions for working with data objects......Page 67
2.6 Summary......Page 68
3 Getting started with graphs......Page 70
3.1 Working with graphs......Page 71
3.2 A simple example......Page 73
3.3 Graphical parameters......Page 74
3.3.1 Symbols and lines......Page 75
3.3.2 Colors......Page 77
3.3.3 Text characteristics......Page 78
3.3.4 Graph and margin dimensions......Page 79
3.4 Adding text, customized axes, and legends......Page 81
3.4.2 Axes......Page 82
3.4.4 Legend......Page 85
3.4.5 Text annotations......Page 87
3.5 Combining graphs......Page 90
3.5.1 Creating a figure arrangement with fine control......Page 94
3.6 Summary......Page 96
4.1 A working example......Page 98
4.2 Creating new variables......Page 100
4.3 Recoding variables......Page 101
4.4 Renaming variables......Page 103
4.5 Missing values......Page 104
4.5.2 Excluding missing values from analyses......Page 105
4.6 Date values......Page 106
4.7 Type conversions......Page 108
4.8 Sorting data......Page 109
4.9.2 Adding rows......Page 110
4.10.2 Excluding (dropping) variables......Page 111
4.10.3 Selecting observations......Page 112
4.10.4 The subset() function......Page 113
4.11 Using SQL statements to manipulate data frames......Page 114
4.12 Summary......Page 115
5 Advanced data management......Page 116
5.1 A data management challenge......Page 117
5.2.1 Mathematical functions......Page 118
5.2.2 Statistical functions......Page 119
5.2.3 Probability functions......Page 121
5.2.4 Character functions......Page 124
5.2.5 Other useful functions......Page 126
5.2.6 Applying functions to matrices and data frames......Page 127
5.3 A solution for our data management challenge......Page 128
5.4.1 Repetition and looping......Page 132
5.4.2 Conditional execution......Page 133
5.5 User-written functions......Page 134
5.6.2 Aggregating data......Page 137
5.6.3 The reshape package......Page 138
5.7 Summary......Page 141
Part 2 Basic methods......Page 142
6 Basic graphs......Page 144
6.1.1 Simple bar plots......Page 145
6.1.2 Stacked and grouped bar plots......Page 146
6.1.3 Mean bar plots......Page 147
6.1.4 Tweaking bar plots......Page 148
6.1.5 Spinograms......Page 149
6.2 Pie charts......Page 150
6.3 Histograms......Page 153
6.4 Kernel density plots......Page 155
6.5 Box plots......Page 158
6.5.1 Using parallel box plots to compare groups......Page 159
6.5.2 Violin plots......Page 162
6.6 Dot plots......Page 163
6.7 Summary......Page 165
7 Basic statistics......Page 166
7.1.1 A menagerie of methods......Page 167
7.1.2 Descriptive statistics by group......Page 171
7.2 Frequency and contingency tables......Page 174
7.2.1 Generating frequency tables......Page 175
7.2.2 Tests of independence......Page 181
7.2.3 Measures of association......Page 182
7.2.5 Converting tables to flat files......Page 183
7.3 Correlations......Page 184
7.3.1 Types of correlations......Page 185
7.3.2 Testing correlations for significance......Page 187
7.4.1 Independent t-test......Page 189
7.4.2 Dependent t-test......Page 190
7.5.1 Comparing two groups......Page 191
7.5.2 Comparing more than two groups......Page 193
7.7 Summary......Page 195
Part 3 Intermediate methods......Page 196
8 Regression......Page 198
8.1 The many faces of regression......Page 199
8.1.1 Scenarios for using OLS regression......Page 200
8.1.2 What you need to know......Page 201
8.2 OLS regression......Page 202
8.2.1 Fitting regression models with lm()......Page 203
8.2.2 Simple linear regression......Page 204
8.2.3 Polynomial regression......Page 206
8.2.4 Multiple linear regression......Page 209
8.2.5 Multiple linear regression with interactions......Page 211
8.3 Regression diagnostics......Page 213
8.3.1 A typical approach......Page 214
8.3.2 An enhanced approach......Page 217
8.3.4 Multicollinearity......Page 224
8.4.1 Outliers......Page 225
8.4.2 High leverage points......Page 226
8.4.3 Influential observations......Page 227
8.5.2 Transforming variables......Page 230
8.6 Selecting the \"best\" regression model......Page 232
8.6.1 Comparing models......Page 233
8.6.2 Variable selection......Page 234
8.7.1 Cross-validation......Page 238
8.7.2 Relative importance......Page 240
8.8 Summary......Page 243
9 Analysis of variance......Page 244
9.1 A crash course on terminology......Page 245
9.2.1 The aov() function......Page 247
9.2.2 The order of formula terms......Page 248
9.3 One-way ANOVA......Page 250
9.3.1 Multiple comparisons......Page 252
9.3.2 Assessing test assumptions......Page 254
9.4 One-way ANCOVA......Page 255
9.4.2 Visualizing the results......Page 257
9.5 Two-way factorial ANOVA......Page 259
9.6 Repeated measures ANOVA......Page 262
9.7 Multivariate analysis of variance (MANOVA)......Page 264
9.7.1 Assessing test assumptions......Page 266
9.7.2 Robust MANOVA......Page 267
9.8 ANOVA as regression......Page 268
9.9 Summary......Page 270
10 Power analysis......Page 271
10.1 A quick review of hypothesis testing......Page 272
10.2 Implementing power analysis with the pwr package......Page 274
10.2.1 t-tests......Page 275
10.2.2 ANOVA......Page 277
10.2.4 Linear models......Page 278
10.2.5 Tests of proportions......Page 279
10.2.6 Chi-square tests......Page 280
10.2.7 Choosing an appropriate effect size in novel situations......Page 282
10.3 Creating power analysis plots......Page 283
10.4 Other packages......Page 285
10.5 Summary......Page 286
11 Intermediate graphs......Page 288
11.1 Scatter plots......Page 289
11.1.1 Scatter plot matrices......Page 292
11.1.2 High-density scatter plots......Page 296
11.1.3 3D scatter plots......Page 299
11.1.4 Bubble plots......Page 303
11.2 Line charts......Page 305
11.3 Correlograms......Page 308
11.4 Mosaic plots......Page 313
11.5 Summary......Page 315
12 Resampling statistics and bootstrapping......Page 316
12.1 Permutation tests......Page 317
12.2 Permutation test with the coin package......Page 319
12.2.1 Independent two-sample and k-sample tests......Page 320
12.2.2 Independence in contingency tables......Page 321
12.2.4 Dependent two-sample and k-sample tests......Page 322
12.3 Permutation tests with the lmPerm package......Page 323
12.3.1 Simple and polynomial regression......Page 324
12.3.2 Multiple regression......Page 325
12.3.2 One-way ANOVA and ANCOVA......Page 326
12.4 Additional comments on permutation tests......Page 327
12.5 Bootstrapping......Page 328
12.6 Bootstrapping with the boot package......Page 329
12.6.1 Bootstrapping a single statistic......Page 330
12.6.2 Bootstrapping several statistics......Page 332
12.7 Summary......Page 334
Part 4 Advanced methods......Page 336
13 Generalized linear models......Page 338
13.1 Generalized linear models and the glm() function......Page 339
13.1.1 The glm() function......Page 340
13.1.2 Supporting functions......Page 341
13.2 Logistic regression......Page 342
13.2.1 Interpreting the model parameters......Page 345
13.2.2 Assessing the impact of predictors on the probability of an outcome......Page 346
13.2.3 Overdispersion......Page 347
13.2.4 Extensions......Page 348
13.3 Poisson regression......Page 349
13.3.1 Interpreting the model parameters......Page 351
13.3.2 Overdispersion......Page 352
13.3.3 Extensions......Page 353
13.4 Summary......Page 355
14 Principal components and factor analysis......Page 356
14.1 Principal components and factor analysis in R......Page 358
14.2 Principal components......Page 359
14.2.1 Selecting the number of components to extract......Page 360
14.2.2 Extracting principal components......Page 361
14.2.3 Rotating principal components......Page 364
14.2.4 Obtaining principal components scores......Page 366
14.3 Exploratory factor analysis......Page 367
14.3.1 Deciding how many common factors to extract......Page 368
14.3.2 Extracting common factors......Page 369
14.3.3 Rotating factors......Page 370
14.4 Other latent variable models......Page 374
14.5 Summary......Page 375
15 Advanced methods for missing data......Page 377
15.1 Steps in dealing with missing data......Page 378
15.2 Identifying missing values......Page 380
15.3 Exploring missing values patterns......Page 381
15.3.2 Exploring missing data visually......Page 382
15.3.3 Using correlations to explore missing values......Page 385
15.4 Understanding the sources and impact of missing data......Page 387
15.5 Rational approaches for dealing with incomplete data......Page 388
15.6 Complete-case analysis (listwise deletion)......Page 389
15.7 Multiple imputation......Page 390
15.8.1 Pairwise deletion......Page 395
15.9 Summary......Page 396
16 Advanced graphics......Page 398
16.1 The four graphic systems in R......Page 399
16.2 The lattice package......Page 400
16.2.1 Conditioning variables......Page 404
16.2.2 Panel functions......Page 406
16.2.3 Grouping variables......Page 408
16.2.4 Graphic parameters......Page 412
16.2.5 Page arrangement......Page 413
16.3 The ggplot2 package......Page 415
16.4.2 playwith......Page 419
16.4.3 latticist......Page 421
16.4.4 Interactive graphics with the iplots package......Page 422
16.5 Summary......Page 424
afterword: Into the rabbit hole......Page 425
appendix A Graphic user interfaces......Page 428
appendix B Customizing the startup environment......Page 431
C.1 Delimited text file......Page 433
C.3 Statistical applications......Page 434
D.1 High-quality typesetting with Sweave (R + LaTeX)......Page 435
D.3 Comments......Page 443
D.2 Joining forces with OpenOffice using odfWeave......Page 440
appendix E Matrix Algebra in R......Page 444
appendix F Packages used in this book......Page 446
G.1 Efficient programming......Page 454
G.2 Storing data outside of RAM......Page 455
G.3 Analytic packages for large datasets......Page 456
appendix H Updating an R installation......Page 457
A......Page 460
C......Page 461
D......Page 462
F......Page 463
G......Page 464
L......Page 465
M......Page 466
O......Page 467
P......Page 468
R......Page 469
S......Page 470
U......Page 471
Y......Page 472
Back Cover......Page 474