R in action: data analysis and graphics with R
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Shelter Island, NY
Manning
2011
|
| Ausgabe: | 2., corr. print. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XXII, 450 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9781935182399 1935182390 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
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| 008 | 121122s2011 ad|| |||| 00||| eng d | ||
| 020 | |a 9781935182399 |c pbk. : GBP 39.99 |9 978-1-935182-39-9 | ||
| 020 | |a 1935182390 |9 1-935182-39-0 | ||
| 035 | |a (OCoLC)820419494 | ||
| 035 | |a (DE-599)BVBBV040574440 | ||
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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., corr. print. | ||
| 264 | 1 | |a Shelter Island, NY |b Manning |c 2011 | |
| 300 | |a XXII, 450 S. |b Ill., graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-025401715 | |
Datensatz im Suchindex
| DE-BY-863_location | 1000 |
|---|---|
| DE-BY-FWS_call_number | 1000/SK 850 K11 |
| DE-BY-FWS_katkey | 539327 |
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| adam_text |
contents
preface
xiii
acknowledgments
xv
about this book
xvii
about the cover illustration
xxii
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
CONTENTS
1.8
Working through an example
18
1.9
Summary
20
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
Getting started zvith 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
afigure
arrangement with fine control
69
3.6
Summary
71
Basic data management
73
4.1
A working example
73
4.2
Creating new variables
75
4.3
Recoding variables
76
CONTENTS
vii
4.4
Renaming
variables 78
4.5
Missing values
79
Receding 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 subsetQ function
88 *
Random samples
89
4.11
Using SQL statements to manipulate data frames
89
4.12
Summary
90
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
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
VIU
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
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
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
Λ
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 ix
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
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
Mul
tivariate analysis of variance
(MÁNOVA)
239
Assessing test assumptions
241 ·
Robust
MÁNOVA
242
9.8
ANOVA as regression
243
9.9
Summary
245
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
Intermediate graphs
263
11.1
Scatter plots
264
Scatter plot matrices
267*
High-density scatter plots
271 ■ 3D
scatter plots
274
Bubble plots
278
CONTENTS
11.2 Line
charts
280
11.3 Correlograms 283
11.4
Mosaic plots
288
11.5
Summary
290
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-sampk and k-sample tests
297 *
Going further
298
12.3
Permutation tests with the
ImPerm
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
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
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 x
Extracting
principal
components
336 ·
Rotating principal components
339
Obtaining principal components scores
341
14.3
Exploratory factor analysis
342
Deciding how many common
f
actors to extract
343 ■
Extracting common
factors
344 ■
Rotating
f
actors
345 ■
Factor scores
349 ■
Other EFA-related
packages
349
14.4
Other latent variable models
349
14.5
Summary
350
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
Advanced graphics
373
16.1
The four graphic systems in
R
374
16.2
The lattice package
375
Conditioning variables
379 ■
P
anel
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
xii CONTENTS
appendix A Graphic user interfaces
403
appendix
В
Customizing the startup environment
406
appendix
С
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
references
434
index
438 |
| any_adam_object | 1 |
| author | Kabacoff, Robert |
| author_GND | (DE-588)14294372X |
| author_facet | Kabacoff, Robert |
| author_role | aut |
| author_sort | Kabacoff, Robert |
| author_variant | r k rk |
| building | Verbundindex |
| bvnumber | BV040574440 |
| classification_rvk | SK 850 WC 7000 ST 601 ST 250 CM 3000 |
| classification_tum | SOZ 720f DAT 307f |
| ctrlnum | (OCoLC)820419494 (DE-599)BVBBV040574440 |
| dewey-full | 519.502855133 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.502855133 |
| dewey-search | 519.502855133 |
| dewey-sort | 3519.502855133 |
| dewey-tens | 510 - Mathematics |
| discipline | Biologie Informatik Soziologie Psychologie Mathematik |
| edition | 2., corr. print. |
| format | Book |
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| id | DE-604.BV040574440 |
| illustrated | Illustrated |
| indexdate | 2024-10-18T04:01:20Z |
| institution | BVB |
| isbn | 9781935182399 1935182390 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025401715 |
| oclc_num | 820419494 |
| open_access_boolean | |
| owner | DE-945 DE-11 DE-739 DE-634 DE-473 DE-BY-UBG DE-384 DE-20 DE-863 DE-BY-FWS DE-29T |
| owner_facet | DE-945 DE-11 DE-739 DE-634 DE-473 DE-BY-UBG DE-384 DE-20 DE-863 DE-BY-FWS DE-29T |
| physical | XXII, 450 S. Ill., graph. Darst. 24 cm |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Manning |
| record_format | marc |
| spellingShingle | Kabacoff, Robert R in action data analysis and graphics with R Datenanalyse (DE-588)4123037-1 gnd R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4705956-4 (DE-588)4056995-0 |
| 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 R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Datenanalyse R Programm Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025401715&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kabacoffrobert rinactiondataanalysisandgraphicswithr |
Inhaltsverzeichnis
THWS Würzburg Zentralbibliothek Lesesaal
| Signatur: |
1000 SK 850 K11 |
|---|---|
| Exemplar 1 | nicht ausleihbar Verfügbar Bestellen |