R in Action英文版
preface xv
acknowledgments xvii
about this book xix
about the cover illustration xxiv
Part I Getting started .............................................1
1 Introduction to R 3
1.1 Why use R? 5
1.2 Obtaining and installing R 7
1.3 Working with R 7
Getting started 8 ■ Getting help 11 ■ The workspace 11
Input and output 13
1.4 Packages 14
What are packages? 15 ■ Installing a package 16
Loading a package 16 ■ Learning about a package 16
1.5 Batch processing 17
1.6 Using output as input—reusing results 18
1.7 Working with large datasets 18
viii CONTENTS
1.8 Working through an example 18
1.9 Summary 20
2 Creating a dataset 21
2.1 Understanding datasets 22
2.2 Data structures 23
Vectors 24 ■ Matrices 24 ■ Arrays 26 ■ Data frames 27
Factors 30 ■ Lists 32
2.3 Data input 33
Entering data from the keyboard 34 ■ Importing data from a delimited text
file 35 ■ Importing data from Excel 36 ■ Importing data from XML 37
Webscraping 37 ■ Importing data from SPSS 38 ■ Importing data from SAS 38
Importing data from Stata 38 ■ Importing data from netCDF 39
Importing data from HDF5 39 ■ Accessing database management systems
(DBMSs) 39 ■ Importing data via Stat/Transfer 41
2.4 Annotating datasets 42
Variable labels 42 ■ Value labels 42
2.5 Useful functions for working with data objects 42
2.6 Summary 43
3 Getting started with graphs 45
3.1 Working with graphs 46
3.2 A simple example 48
3.3 Graphical parameters 49
Symbols and lines 50 ■ Colors 52 ■ Text characteristics 53
Graph and margin dimensions 54
3.4 Adding text, customized axes, and legends 56
Titles 57 ■ Axes 57 ■ Reference lines 60 ■ Legend 60
Text annotations 62
3.5 Combining graphs 65
Creating a figure arrangement with fine control 69
3.6 Summary 71
4 Basic data management 73
4.1 A working example 73
4.2 Creating new variables 75
4.3 Recoding variables 76
CONTENTS ix
4.4 Renaming variables 78
4.5 Missing values 79
Recoding values to missing 80 ■ Excluding missing values from analyses 80
4.6 Date values 81
Converting dates to character variables 83 ■ Going further 83
4.7 Type conversions 83
4.8 Sorting data 84
4.9 Merging datasets 85
Adding columns 85 ■ Adding rows 85
4.10 Subsetting datasets 86
Selecting (keeping) variables 86 ■ Excluding (dropping) variables 86
Selecting observations 87 ■ The subset() function 88 ■ Random samples 89
4.11 Using SQL statements to manipulate data frames 89
4.12 Summary 90
5 Advanced data management 91
5.1 A data management challenge 92
5.2 Numerical and character functions 93
Mathematical functions 93 ■ Statistical functions 94 ■ Probability functions 96
Character functions 99 ■ Other useful functions 101 ■ Applying functions to
matrices and data frames 102
5.3 A solution for our data management challenge 103
5.4 Control flow 107
Repetition and looping 107 ■ Conditional execution 108
5.5 User-written functions 109
5.6 Aggregation and restructuring 112
Transpose 112 ■ Aggregating data 112 ■ The reshape package 113
5.7 Summary 116
Part II Basic methods ............................................117
6 Basic graphs 119
6.1 Bar plots 120
Simple bar plots 120 ■ Stacked and grouped bar plots 121 ■ Mean bar plots 122
Tweaking bar plots 123 ■ Spinograms 124
6.2 Pie charts 125
6.3 Histograms 128
x CONTENTS
6.4 Kernel density plots 130
6.5 Box plots 133
Using parallel box plots to compare groups 134 ■ Violin plots 137
6.6 Dot plots 138
6.7 Summary 140
7 Basic statistics 141
7.1 Descriptive statistics 142
A menagerie of methods 142 ■ Descriptive statistics by group 146
Visualizing results 149
7.2 Frequency and contingency tables 149
Generating frequency tables 150 ■ Tests of independence 156
Measures of association 157 ■ Visualizing results 158
Converting tables to flat files 158
7.3 Correlations 159
Types of correlations 160 ■ Testing correlations for significance 162
Visualizing correlations 164
7.4 t-tests 164
Independent t-test 164 ■ Dependent t-test 165 ■ When there are more than two
groups 166
7.5 Nonparametric tests of group differences 166
Comparing two groups 166 ■ Comparing more than two groups 168
7.6 Visualizing group differences 170
7.7 Summary 170
Part III Intermediate methods ............................171
8 Regression 173
8.1 The many faces of regression 174
Scenarios for using OLS regression 175 ■ What you need to know 176
8.2 OLS regression 177
Fitting regression models with lm() 178 ■ Simple linear regression 179
Polynomial regression 181 ■ Multiple linear regression 184
Multiple linear regression with interactions 186
8.3 Regression diagnostics 188
A typical approach 189 ■ An enhanced approach 192 ■ Global validation of
linear model assumption 199 ■ Multicollinearity 199
8.4 Unusual observations 200
Outliers 200 ■ High leverage points 201 ■ Influential observations 202
CONTENTS xi
8.5 Corrective measures 205
Deleting observations 205 ■ Transforming variables 205 ■ Adding or deleting
variables 207 ■ Trying a different approach 207
8.6 Selecting the “best” regression model 207
Comparing models 208 ■ Variable selection 209
8.7 Taking the analysis further 213
Cross-validation 213 ■ Relative importance 215
8.8 Summary 218
9 Analysis of variance 219
9.1 A crash course on terminology 220
9.2 Fitting ANOVA models 222
The aov() function 222 ■ The order of formula terms 223
9.3 One-way ANOVA 225
Multiple comparisons 227 ■ Assessing test assumptions 229
9.4 One-way ANCOVA 230
Assessing test assumptions 232 ■ Visualizing the results 232
9.5 Two-way factorial ANOVA 234
9.6 Repeated measures ANOVA 237
9.7 Multivariate analysis of variance (MANOVA) 239
Assessing test assumptions 241 ■ Robust MANOVA 242
9.8 ANOVA as regression 243
9.9 Summary 245
10 Power analysis 246
10.1 A quick review of hypothesis testing 247
10.2 Implementing power analysis with the pwr package 249
t-tests 250 ■ ANOVA 252 ■ Correlations 253 ■ Linear models 253
Tests of proportions 254 ■ Chi-square tests 255 ■ Choosing an appropriate effect
size in novel situations 257
10.3 Creating power analysis plots 258
10.4 Other packages 260
10.5 Summary 261
11 Intermediate graphs 263
11.1 Scatter plots 264
Scatter plot matrices 267 ■ High-density scatter plots 271 ■ 3D scatter plots 274
Bubble plots 278
xii CONTENTS
11.2 Line charts 280
11.3 Correlograms 283
11.4 Mosaic plots 288
11.5 Summary 290
12 Resampling statistics and bootstrapping 291
12.1 Permutation tests 292
12.2 Permutation test with the coin package 294
Independent two-sample and k-sample tests 295 ■ Independence in contingency
tables 296 ■ Independence between numeric variables 297
Dependent two-sample and k-sample tests 297 ■ Going further 298
12.3 Permutation tests with the lmPerm package 298
Simple and polynomial regression 299 ■ Multiple regression 300
One-way ANOVA and ANCOVA 301 ■ Two-way ANOVA 302
12.4 Additional comments on permutation tests 302
12.5 Bootstrapping 303
12.6 Bootstrapping with the boot package 304
Bootstrapping a single statistic 305 ■ Bootstrapping several statistics 307
12.7 Summary 309
Part IV Advanced methods ...................................311
13 Generalized linear models 313
13.1 Generalized linear models and the glm() function 314
The glm() function 315 ■ Supporting functions 316 ■ Model fit and regression
diagnostics 317
13.2 Logistic regression 317
Interpreting the model parameters 320 ■ Assessing the impact of predictors on the
probability of an outcome 321 ■ Overdispersion 322 ■ Extensions 323
13.3 Poisson regression 324
Interpreting the model parameters 326 ■ Overdispersion 327 ■ Extensions 328
13.4 Summary 330
14 Principal components and factor analysis 331
14.1 Principal components and factor analysis in R 333
14.2 Principal components 334
Selecting the number of components to extract 335
CONTENTS xiii
Extracting principal components 336 ■ Rotating principal components 339
Obtaining principal components scores 341
14.3 Exploratory factor analysis 342
Deciding how many common factors to extract 343 ■ Extracting common
factors 344 ■ Rotating factors 345 ■ Factor scores 349 ■ Other EFA-related
packages 349
14.4 Other latent variable models 349
14.5 Summary 350
15 Advanced methods for missing data 352
15.1 Steps in dealing with missing data 353
15.2 Identifying missing values 355
15.3 Exploring missing values patterns 356
Tabulating missing values 357 ■ Exploring missing data visually 357 ■ Using
correlations to explore missing values 360
15.4 Understanding the sources and impact of missing data 362
15.5 Rational approaches for dealing with incomplete data 363
15.6 Complete-case analysis (listwise deletion) 364
15.7 Multiple imputation 365
15.8 Other approaches to missing data 370
Pairwise deletion 370 ■ Simple (nonstochastic) imputation 371
15.9 Summary 371
16 Advanced graphics 373
16.1 The four graphic systems in R 374
16.2 The lattice package 375
Conditioning variables 379 ■ Panel functions 381 ■ Grouping variables 383
Graphic parameters 387 ■ Page arrangement 388
16.3 The ggplot2 package 390
16.4 Interactive graphs 394
Interacting with graphs: identifying points 394 ■ playwith 394
latticist 396 ■ Interactive graphics with the iplots package 397 ■ rggobi 399
16.5 Summary 399
afterword Into the rabbit hole 400
xiv CONTENTS
appendix A Graphic user interfaces 403
appendix B Customizing the startup environment 406
appendix C Exporting data from R 408
appendix D Creating publication-quality output 410
appendix E Matrix Algebra in R 419
appendix F Packages used in this book 421
appendix G Working with large datasets 429
appendix H Updating an R installation 432
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发表于 2013-2-15 17:43:06