R in action: data analysis and graphics with R
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Shelter Island, NY
Manning
2015
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxviii, 579 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9781617291388 |
Internformat
MARC
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| 008 | 150428s2015 ad|| |||| 00||| eng d | ||
| 020 | |a 9781617291388 |c pbk: 50.24 |9 978-1-61729-138-8 | ||
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| 100 | 1 | |a Kabacoff, Robert |e Verfasser |0 (DE-588)14294372X |4 aut | |
| 245 | 1 | 0 | |a R in action |b data analysis and graphics with R |c Robert I. Kabacoff |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Shelter Island, NY |b Manning |c 2015 | |
| 300 | |a xxviii, 579 S. |b Ill., graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
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Datensatz im Suchindex
| _version_ | 1804153288838348800 |
|---|---|
| adam_text | Titel: R in action
Autor: Kabacoff, Robert
Jahr: 2015
brief contents
vrt1 Getting started 1
Introduction to R 3
Creating a dataset 20
Getting started with graphs 46
Basic data management 71
Advanced data management 89
art 2 Basic: methods 115
6 ¦ Basic graphs 117
7 ¦ Basic statistics 137
art 3 Intermediate methods
8 • Regression 167
9 ¦ Analysis of variance 212
10 ¦ Power analysis 239
11 ¦ Intermediate graphs 255
12 ¦ Resampling statistics and bootstrapping 279
165
vi
BRIEF CONTENTS
Part 4 Advanced methods 299
13 ¦ Generalized linear models 301
14 ¦ Principal components and factor analysis 319
15 ¦ Time series 340
16 ¦ Cluster analysis 369
17 ¦ Classification 389
18 ¦ Advanced methods for missing data 414
Part 5 Expanding your skills 435
19 ¦ Advanced graphics with ggplot2 437
20 ¦ Advanced programming 463
21 ¦ Creating a package 491
22 ¦ Creating dynamic reports 513
23 ¦ Advanced graphics with the lattice package online only
Contents
preface xvii
acknowledgments xix
about this book xxi
about the cover illustration xxvii
Part 1 Getting started 1
J 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 10 ¦ The workspace 11
Input and output 13
1.4 Packages 15
What are packages? 15 • Installing a package 15
Loading a package 15 • Learning about a
package 16
1.5 Batch processing 16
1.6 Using output as input: reusing results 17
1.7 Working with large datasets 17
vii
CONTENTS
1.8 Working through an example 18
1.9 Summary 19
Creating a dataset 20
2.1 Understanding datasets 21
2.2 Data structures 22
Vectors 22 ¦ Motrices 23 ¦ Arrays 24 ¦ Data frames 25
Factors 28 ¦ Lists 30
2.3 Data input 32
Entering data from the keyboard 33 ¦ Importing data from a
delimited text file 34 ¦ Importing data from Excel 37
Importing data from XML 38 ¦ Importing data from the
web 38 ¦ Importing data from SPSS 38 • Importing data
from .SAS 39 ¦ Importing data from Stata 40 ¦ Importing
data from NetCDF 40 ¦ Importing data from HDF5 40
Accessing database management systems (.DBMSs) 40
Importing data via Stat/Transfer 42
2.4 Annotating datasets 43
Variable labels 43 ¦ Value labels 43
2.5 Useful functions for working with data objects 43
2.6 Summary 44
Getting started with graphs 46
3.1 Working with graphs 47
3.2 A simple example 49
3.3 Graphical parameters 50
Symbols and lines 51 - Colors 52 Text characteristics 53
Graph and margin dimensions 54
3.4 Adding text, customized axes, and legends 56
Titles 56 Axes 57 - Reference lines 60 ¦ Legend 60
Text annotations 61 • Math annotations 63
3.5 Combining graphs 64
Creating a figure arrangement with fine control 68
3.6 Summary 70
Basic data management 71
4.1 A working example 71
4.2 Creating new variables 73
CONTENTS
ix
4.3 Recoding variables 75
4.4 Renaming variables 76
4.5 Missing values 77
Recoding values to missing 78 ¦ Excluding missing values
from analyses 78
4.6 Date values 79
Converting dates to character variables 81 ¦ Going
further 81
4.7 Type conversions 81
4.8 Sorting data 82
4.9 Merging datasets 83
Adding columns to a data frame 83 ¦ Adding rows to
a data frame 84
4.10 Subsetting datasets 84
Selecting (keeping) variables 84 ¦ Excluding (dropping)
variables 84 * Selecting observations 85 ¦ The subsetQ
function 86 ¦ Random samples 87
4.11 Using SQL statements to manipulate data
frames 87
4.12 Summary 88
y Advanced data management 89
5.1 A data-management challenge 90
5.2 Numerical and character functions 91
Mathematical functions 91 ¦ Statistical functions 92
Probability functions 94 ¦ Character functions 97
Other useful functions 98 ¦ Applying functions to matrices
and data frames 99
5.3 A solution for the data-management challenge 101
5.4 Control flow 105
Repetition and looping 105 ¦ Conditional
execution 106
5.5 User-written functions 107
5.6 Aggregation and reshaping 109
Transpose 110 ¦ Aggregating data 110 ¦ The reshape!
package 111
5.7 Summary 113
X
CONTENTS
Part 2 Basic methods 115
Basic graphs 117
6.1 Bar plots 118
Simple bar plots 118 ¦ Stacked and grouped bar plots 119
i Mean bar plots 120 ¦ Tweaking bar plots 121
Spinograms 122
6.2 Pie charts 123
6.3 Histograms 125
6.4 Kernel density plots 127
6.5 Box plots 129
Using parallel box plots to compare groups 129 ¦ Violin
plots 132
6.6 Dot plots 133
6.7 Summary 136
ƒ
Basic statistics 13 7
7.1 Descriptive statistics 138
A menagerie of methods 138 ¦ Even more methods 140
Descriptive statistics by group 142 • Additional methods
by group 143 ¦ Visualizing results 144
7.2 Frequency and contingency tables 144
Generating frequency tables 145 ¦ Tests of
independence 151 ¦ Measures of association 152
Visualizing results 153
7.3 Correlations 153
Types of correlations 153 ¦ Testing correlations for
significance 156 • Visualizing correlations 158
7.4 T-tests 158
Independent t-test 158 ¦ Dependent t-test 159
When there are more than two groups 160
7.5 Nonparametric tests of group differences 160
Comparing two groups 160 ¦ Comparing more than two
groups 161
7.6 Visualizing group differences 163
7.7 Summary 164
CONTENTS
XI
Part 3 Intermediate methods 165
ßf Regression 167
8.1 The many faces of regression 168
Scenarios for using OLS regression 169¦ What you need to
know 170
8.2 OLS regression 171
Fitting regression models with lm() 172 ¦ Simple linear
regression 173 ¦ Polynomial regression 175
Multiple linear regression 178 ¦ Multiple linear regression
with interactions 180
8.3 Regression diagnostics 182
A typical approach 183 ¦ An enhanced approach 187
Global validation of linear model assumption 193
Multicollinearity 193
8.4 Unusual observations 194
Outliers 194 ¦ High-leverage points 195 ¦ Influential
observations 196
8.5 Corrective measures 198
Deleting observations 199 • Transforming variables 199
Adding or deleting variables 201 ¦ Trying a different
approach 201
8.6 Selecting the best regression model 201
Comparing models 202 ¦ Variable selection 203
8.7 Taking the analysis further 206
Cross-validation 206 ¦ lielative importance 208
8.8 Summary 211
(} Analysis of variance 212
9.1 A crash course on terminology 213
9.2 Fitting ANOVA models 215
The aov() function 215 ¦ The order of formula terms 216
9.3 One-way ANOVA 218
Multiple comparisons 219 ¦ Assessing test assumptions 222
9.4 One-way ANCOVA 223
Assessing test assumptions 225 ¦ Visualizing the results 225
9.5 Two-way factorial ANOVA 226
xii
CONTENTS
9.6 Repeated measures ANOVA 229
9.7 Multivariate analysis of variance (MANOVA) 232
Assessing test assumptions 234 Robust MANOVA 235
9.8 ANOVA as regression 236
9.9 Summary 238
/ ƒ ) Power analysis 239
^ 10.1 A quick review of hypothesis testing 240
10.2 Implementing power analysis with the pwr package 242
t-tests 243 ¦ ANOVA 245 ¦ Correlations 245
Linear models 246 ¦ Tests of proportions 24 7
Chi-square tests 248 ¦ Choosing an appropriate effect size
in novel situations 249
10.3 Creating power analysis plots 251
10.4 Other packages 252
10.5 Summary 253
Intermediate graphs 255
11.1 Scatter plots 256
Scatter-plot matrices 259 ¦ High-density scatter plots 261
3D scatter plots 263 ¦ Spinning 3D scatter plots 265
Bubble plots 266
11.2 Line charts 268
11.3 Corrgrams 271
11.4 Mosaic plots 276
11.5 Summary 278
Ç) Resampling statistics and bootstrapping 279
12.1 Permutation tests 280
12.2 Permutation tests with the coin package 282
Independent two-sample and k-sample tests 283
Independence in contingency tables 285 ¦ Independence
between numeric variables 285 • Dependent two-sample,
and k-sample tests 286 • Going further 286
12.3 Permutation tests with the ImPerm package 287
Simple and polynomial regression 287 ¦ Multiple
regression 288 One-way ANOVA and ANCOVA 289
Two-way ANO VA 2 90
CONTENTS
12.4 Additional comments on permutation tests 291
12.5 Bootstrapping 291
12.6 Bootstrapping with the boot package 292
Bootstrapping a single statistic 294 ¦ Bootstrapping several
statistics 296
12.7 Summary 298
Advanced methods
Generalized linear models 301
13.1 Generalized linear models and the glm() function 302
The glm() function 303 * Supporting functions 304
Model fit and regression diagnostics 305
13.2 Logistic regression 306
Interpreting the model parameters 308 ¦ Assessing the impact
of predictors on the probability of an outcome 309
Overdispersion 310 ¦ Extensions 311
13.3 Poisson regression 312
Interpreting the model parameters 314 * Overdispersion 315
Extensions 317
13.4 Summary 318
Principal components and factor analysis 319
14.1 Principal components and factor analysis in R 321
14.2 Principal components 322
Selecting the number of components to extract 323
Extracting principal components 324 ¦ Rotating principal
components 327 * Obtaining principal components scores 328
14.3 Exploratory factor analysis 330
Deciding how many common factors to extract 331
Extracting common factors 332 ¦ Rotating factors 333
Factor scores 336 • Other EFA-related packages 337
14.4 Other latent variable models 337
14.5 Summary 338
Time series 340
15.1 Creating a time-series object in R 343
xiv
CONTENTS
15.2 Smoothing and seasonal decomposition 345
Smoothing with simple moving averages 345 • Seasonal
decomposition 347
15.3 Exponential forecasting models 352
Simple exponential smoothing 353 ¦ Holt and Holt-Winters
exponential smoothing 355 ¦ The ets() junction and
automated forecasting 358
15.4 ARIMA forecasting models 359
Prerequisite concepts 359 * ARALA and ARIMA models 361
Automated ARIMA forecasting 366
15.5 Going further 367
15.6 Summary 367
/ /Ç Cluster analysis 369
16.1 Common steps in cluster analysis 370
16.2 Calculating distances 372
16.3 Hierarchical cluster analysis 374
16.4 Partitioning cluster analysis 378
K-means clustering 378 ¦ Partitioning around medoids 382
16.5 Avoiding nonexistent clusters 384
16.6 Summary 387
ƒ 7 Classification 389
17.1 Preparing the data 390
17.2 Logistic regression 392
17.3 Decision trees 393
Classical decision trees 393 ¦ Conditional inference trees 397
17.4 Random forests 399
17.5 Support vector machines 401
Tuning an SVM 403
17.6 Choosing a best predictive solution 405
17.7 Using the rattle package for data mining 408
17.8 Summary 413
ƒ Advanced methods for missing data 414
18.1 Steps in dealing with missing data 415
18.2 Identifying missing values 417
CONTENTS
XV
18.3 Exploring missing-values patterns 418
Tabulating missing values 419 ¦ Exploring missing data
visually 419 Using correlations to explore missing
values 422
18.4 Understanding the sources and impact of missing data 424
18.5 Rational approaches for dealing with incomplete data 425
18.6 Complete-case analysis (listwise deletion) 426
18.7 Multiple imputation 428
18.8 Other approaches to missing data 432
Pairwise deletion 432 ¦ Simple (nonstochastic)
imputation 433
18.9 Summary 433
Expanding your skills 435
Advanced graphics xvith ggplot2 437
19.1 The four graphics systems in R 438
19.2 An introduction to the ggplot2 package 439
19.3 Specifying the plot type with geoms 443
19.4 Grouping 447
19.5 Faceting 450
19.6 Adding smoothed lines 453
19.7 Modifying the appearance of ggplot2 graphs 455
Axes 455 ¦ Legends 457 • Scales 458 • Themes 460
Multiple graphs per page 461
19.8 Saving graphs 462
19.9 Summary 462
Advanced programming 463
20.1 A review of the language 464
Datatypes 464 Control structures 470 Creating
functions 473
20.2 Working with environments 475
20.3 Object-oriented programming 477
Generic functions 477 ¦ Limitations of the S3 model 479
20.4 Writing efficient code 479
xvi
CONTENTS
20.5 Debugging 483
Common sources of errors 483 ¦ Debugging tools 484
Session options that support debugging 486
20.6 Going further 489
20.7 Summary 490
p ƒ Creating a package 491
21.1 Nonparametric analysis and the npar package 492
Comparing groups with the npar package 494
21.2 Developing the package 496
Computing the statistics 497 ¦ Printing the results 500
, i Summarizing the results 501 ¦ Plotting the results 504
Adding sample data to the package 505
21.3 Creating the package documentation 506
21.4 Building the package 508
21.5 Going further 512
21.6 Summary 512
pp) Creating dynamic reports 513
22.1 A template approach to reports 515
22.2 Creating dynamic reports with R and Markdown 517
22.3 Creating dynamic reports with R and LaTeX 522
22.4 Creating dynamic reports with R and Open Document 525
22.5 Creating dynamic reports with R and Microsoft Word 527
22.6 Summary 531
afterword Into the rabbit hole 532
appendix A Graphical user interfaces 535
appendix B Customizing the startup environment 538
appendix C Exporting data from R 540
appendix D Matrix algebra in R 542
appendix E Packages used in this book 544
appendix F Working with large datasets 551
appendix G Updating an R installation 555
references 558
index 563
bonus chapter 23 Advanced graphics with the lattice package
available online at nianning.com/RinActionSecondEfIition
|
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| author | Kabacoff, Robert |
| author_GND | (DE-588)14294372X |
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| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-search | 519.502855133 |
| dewey-sort | 3519.502855133 |
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| discipline | Informatik Psychologie Mathematik |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV042529967 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:24:14Z |
| institution | BVB |
| isbn | 9781617291388 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027964216 |
| oclc_num | 903301719 |
| open_access_boolean | |
| owner | DE-29T DE-11 DE-523 DE-210 |
| owner_facet | DE-29T DE-11 DE-523 DE-210 |
| physical | xxviii, 579 S. Ill., graph. Darst. 24 cm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Manning |
| record_format | marc |
| spelling | Kabacoff, Robert Verfasser (DE-588)14294372X aut R in action data analysis and graphics with R Robert I. Kabacoff 2. ed. Shelter Island, NY Manning 2015 xxviii, 579 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf R Programm (DE-588)4705956-4 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027964216&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kabacoff, Robert R in action data analysis and graphics with R Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4056995-0 (DE-588)4705956-4 |
| title | R in action data analysis and graphics with R |
| title_auth | R in action data analysis and graphics with R |
| title_exact_search | R in action data analysis and graphics with R |
| title_full | R in action data analysis and graphics with R Robert I. Kabacoff |
| title_fullStr | R in action data analysis and graphics with R Robert I. Kabacoff |
| title_full_unstemmed | R in action data analysis and graphics with R Robert I. Kabacoff |
| title_short | R in action |
| title_sort | r in action data analysis and graphics with r |
| title_sub | data analysis and graphics with R |
| topic | Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Datenanalyse Statistik R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027964216&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kabacoffrobert rinactiondataanalysisandgraphicswithr |