Modern data science with R:
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| Main Authors: | , , |
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| Format: | Book |
| Language: | English |
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Boca Raton ; London ; New York
CRC Press
[2017]
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| Series: | Texts in statistical science
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxvi, 551 Seiten Illustrationen, Diagramme, Karten (farbig) |
| ISBN: | 9781498724487 |
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| 245 | 1 | 0 | |a Modern data science with R |c Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton |
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| adam_text | Contents
List of Tables xv
List of Figures xvii
Preface xxiii
I Introduction to Data Science 1
1 Prologue: Why data science? 3
1.1 What is data science?..................................................... 4
1.2 Case study: The evolution of sabermetrics ................................ 6
1.3 Datasets.................................................................. 7
1.4 Further resources........................................................ 8
2 Data visualization 9
2.1 The 2012 federal election cycle.......................................... 9
2.1.1 Are these two groups different?.................................... 10
2.1.2 Graphing variation................................................. 11
2.1.3 Examining relationships among variables ........................... 12
2.1.4 Networks......................................................... 13
2.2 Composing data graphics.................................................. 14
2.2.1 A taxonomy for data graphics....................................... 14
2.2.2 Color ......................................................... 19
2.2.3 Dissecting data graphics........................................... 20
2.3 Importance of data graphics: Challenger ................................. 23
2.4 . Creating effective presentations....................................... 27
2.5 The wider world of data visualization.................................... 28
2.6 Further resources...................................................... 30
2.7 Exercises........ ..................................................... 30
3 A grammar for graphics 33
3.1 A grammar for data graphics.............................................. 33
3.1.1 Aesthetics......................................................... 34
3.1.2 Scale.............................................................. 37
3.1.3 Guides............................................................. 38
3.1.4 Facets............................................................ 38
3.1.5 Layers............................................................. 38
3.2 Canonical data graphics in R............................................. 39
3.2.1 Univariate displays................................................ 39
vii
viii CONTENTS
3.2.2 Multivariate displays........................................... 43
3.2.3 Maps ............................................................. 48
3.2.4 Networks.......................................................... 48
3.3 Extended example: Historical baby names.................................. 48
3.3.1 Percentage of people alive today.................................. 50
3.3.2 Most common women’s names ........................................ 56
3.4 Further resources........................................................ 58
3.5 Exercises................................................................ 58
4 Data wrangling 63
4.1 A grammar for data wrangling............................................. 63
4.1.1 select() and f liter()............................................ 63
4.1.2 mutateQ and rename().............................................. 66
4.1.3 arrange()......................................................... 69
4.1.4 summarize() with group_by()....................................... 70
4.2 Extended example: Ben’s time with the Mets............................... 72
4.3 Combining multiple tables ............................................... 79
4.3.1 inner_join()...................................................... 79
4.3.2 leit_join() ...................................................... 81
4.4 Extended example: Manny Ramirez.......................................... 82
4.5 Further resources........................................................ 88
4.6 Exercises................................................................ 88
5 Tidy data and iteration 91
5.1 Tidy data................................................................ 91
5.1.1 Motivation........................................................ 91
5.1.2 What are tidy data?............................................... 93
5.2 Reshaping data........................................................... 98
5.2.1 Data verbs for converting wide to narrow and vice versa.......... 100
5.2.2 Spreading........................................................ 100
5.2.3 Gathering........................................................ 101
5.2.4 Example: Gender-neutral names.................................... 101
5.3 Naming conventions...................................................... 103
5.4 Automation and iteration................................................ 104
5.4.1 Vectorized operations ........................................... 104
5.4.2 The apply() family of functions ................................. 106
5.4.3 Iteration over subgroups with dplyr: : do()...................... 110
5.4.4 Iteration with mosaic: :do ...................................... 113
5.5 Data intake............................................................. 116
5.5.1 Data-table friendly formats...................................... 116
5.5.2 APIs............................................................. 120
5.5.3 Cleaning data.................................................... 120
5.5.4 Example: Japanese nuclear reactors............................... 126
5.6 Further resources....................................................... 127
5.7 Exercises............................................................... 128
6 Professional Ethics 131
6.1 Introduction............................................................ 131
6.2 Truthful falsehoods .................................................... 131
6.3 Some settings for professional ethics................................... 134
6.3.1 The chief executive officer...................................... 134
CONTENTS ix
6.3.2 Employment discrimination........................................... 134
6.3.3 Data scraping....................................................... 135
6.3.4 Reproducible spreadsheet analysis................................... 135
6.3.5 Drug dangers........................................................ 135
6.3.6 Legal negotiations.................................................. 136
6.4 Some principles to guide ethical action.................................... 136
6.4.1 Applying the precepts............................................... 137
6.5 Data and disclosure........................................................ 140
6.5.1 Reidentification and disclosure avoidance .......................... 140
6.5.2 Safe data storage................................................... 141
6.5.3 Data scraping and terms of use...................................... 141
6.6 Reproducibility........................................................... 142
6.6.1 Example: Erroneous data merging .................................... 142
6.7 Professional guidelines for ethical conduct................................ 143
6.8 Ethics, collectively....................................................... 143
6.9 Further resources.......................................................... 144
6.10 Exercises................................................................. 144
II Statistics and Modeling 147
7 Statistical foundations 149
7.1 Samples and populations.................................................... 149
7.2 Sample statistics.......................................................... 152
7.3 The bootstrap.............................................................. 155
7.4 Outliers................................................................... 157
7.5 Statistical models: Explaining variation................................... 159
7.6 Confounding and accounting for other factors............................... 162
7.7 The perils of p-values..................................................... 165
7.8 Further resources.......................................................... 167
7.9 Exercises.................................................................. 168
8 Statistical learning and predictive analytics 171
8.1 Supervised learning........................................................ 172
8.2 Classifiers................................................................ 173
8.2.1 Decision trees...................................................... 173
8.2.2 Example: High-earners in the 1994 United States Census.............. 174
8.2.3 Tuning parameters................................................... 180
8.2.4 Random forests...................................................... 181
8.2.5 Nearest neighbor.................................................... 182
8.2.6 Naive Bayes......................................................... 183
8.2.7 Artificial neural networks.......................................... 185
8.3 Ensemble methods........................................................... 186
8.4 Evaluating models.......................................................... 188
8.4.1 Cross-validation ................................................... 188
8.4.2 Measuring prediction error ......................................... 189
8.4.3 Confusion matrix.................................................... 189
8.4.4 ROC curves.......................................................... 189
8.4.5 Bias-variance trade-off............................................. 192
8.4.6 Example: Evaluation of income models ............................... 192
8.5 Extended example: Who has diabetes? ....................................... 196
X CONTENTS
8.6 Regularization......................................................... 201
8.7 Further resources..................................................... 201
8.8 Exercises............................................................. 201
9 Unsupervised learning 205
9.1 Clustering............................................................. 205
9.1.1 Hierarchical clustering.......................................... 206
9.1.2 fc-means......................................................... 210
9.2 Dimension reduction.................................................... 211
9.2.1 Intuitive approaches............................................. 212
9.2.2 Singular value decomposition..................................... 213
9.3 Further resources...................................................... 218
9.4 Exercises.............................................................. 218
10 Simulation 221
10.1 Reasoning in reverse.................................................. 221
10.2 Extended example: Grouping cancers................................... 222
10.3 Randomizing functions ................................................ 223
10.4 Simulating variability................................................ 225
10.4.1 The partially planned rendezvous................................ 225
10.4.2 The jobs report................................................. 227
10.4.3 Restaurant health and sanitation grades......................... 228
10.5 Simulating a complex system........................................... 231
10.6 Random networks....................................................... 233
10.7 Key principles of simulation.......................................... 233
10.8 Further resources..................................................... 235
10.9 Exercises............................................................. 236
III Topics in Data Science 241
11 Interactive data graphics 243
11.1 Rich Web content using D3.js and htmlwidgets.......................... 243
11.1.1 Leaflet......................................................... 244
11.1.2 Plot.ly......................................................... 244
11.1.3 DataTables...................................................... 244
11.1.4 dygraphs ....................................................... 246
11.1.5 streamgraphs.................................................... 246
11.2 Dynamic visualization using ggvis..................................... 246
11.3 Interactive Web apps with Shiny....................................... 247
11.4 Further customization................................................. 250
11.5 Extended example: Hot dog eating...................................... 254
11.6 Further resources..................................................... 258
11.7 Exercises............................................................. 258
12 Database querying using SQL 261
12.1 From dplyr to SQL..................................................... 261
12.2 Flat-file databases................................................... 265
12.3 The SQL universe...................................................... 266
12.4 The SQL data manipulation language.................................... 267
12.4.1 SELECT. . .FROM................................................. 270
CONTENTS
xi
12.4.2 WHERE.............................................................. 272
12.4.3 GROUP BY........................................................... 275
12.4.4 ORDER BY........................................................... 277
12.4.5 HAVING ............................................................ 278
12.4.6 LIMIT.............................................................. 280
12.4.7 JOIN............................................................... 281
12.4.8 UNION.............................................................. 286
12.4.9 Subqueries ........................................................ 287
12.5 Extended example: FiveThirtyEight flights................................. 289
12.6 SQL vs. R ................................................................ 298
12.7 Further resources......................................................... 298
12.8 Exercises................................................................. 298
13 Database administration 301
13.1 Constructing efficient SQL databases...................................... 301
13.1.1 Creating new databases............................................. 301
13.1.2 CREATE TABLE....................................................... 302
13.1.3 Keys............................................................... 303
13.1.4 Indices............................................................ 304
13.1.5 EXPLAIN............................................................ 306
13.1.6 Partitioning....................................................... 308
13.2 Changing SQL data......................................................... 308
13.2.1 UPDATE ............................................................ 308
13.2.2 INSERT ............................................................ 309
13.2.3 LOAD DATA ......................................................... 309
13.3 Extended example: Building a database..................................... 309
13.3.1 Extract............................................................ 310
13.3.2 Transform.......................................................... 310
13.3.3 Load into MySQL database........................................ 310
13.4 Scalability .............................................................. 314
13.5 Further resources......................................................... 314
13.6 Exercises................................................................. 314
14 Working with spatial data 317
14.1 Motivation: What’s so great about spatial data?........................... 317
14.2 Spatial data structures .................................................. 319
14.3 Making maps............................................................... 322
14.3.1 Static maps with ggmap............................................. 322
14.3.2 Projections........................................................ 324
14.3.3 Geocoding, routes, and distances................................... 330
14.3.4 Dynamic maps with leaf let......................................... 332
14.4 Extended example: Congressional districts ................................ 333
14.4.1 Election results................................................... 334
14.4.2 Congressional districts............................................ 336
14.4.3 Putting it all together............................................ 338
14.4.4 Using ggmap....................................................... 340
14.4.5 Using leaflet..................................................... 343
14.5 Effective maps: How (not) to lie.......................................... 343
14.6 Extended example: Historical airline route maps........................... 345
14.6.1 Using ggmap....................................................... 346
14.6.2 Using leaflet..................................................... 347
xii CONTENTS
14.7 Projecting polygons................................................. 349
14.8 Playing well with others............................................ 351
14.9 Further resources................................................... 352
14.10 Exercises.......................................................... 352
15 Text as data 355
15.1 Tools for working with text......................................... 355
15.1.1 Regular՝ expressions using Macbeth............................ 355
15.1.2 Example: Life and death in Macbeth............................ 359
15.2 Analyzing textual data.............................................. 360
15.2.1 Corpora......................................................... 364
15.2.2 Word clouds .................................................. 365
15.2.3 Document term matrices........................................ 365
15.3 Ingesting text...................................................... 367
15.3.1 Example: Scraping the songs of the Beatles.................... 367
15.3.2 Scraping data from Twitter...................................... 369
15.4 Further resources................................................... 374
15.5 Exercises........................................................... 374
16 Network science 377
16.1 Introduction to network science....................................... 377
16.1.1 Definitions .................................................... 377
16.1.2 A brief history of network science.............................. 378
16.2 Extended example: Six degrees of Kristen Stewart...................... 382
16.2.1 Collecting Hollywood data....................................... 382
16.2.2 Building the Hollywood network.................................. 384
16.2.3 Building a Kristen Stewart oracle............................... 387
16.3 PageRank.............................................................. 390
16.4 Extended example: 1996 men’s college basketball..................... 391
16.5 Further resources..................................................... 398
16.6 Exercises............................................................. 398
17 Epilogue: Towards “big data” 401
17.1 Notions of big data .................................................. 401
17.2 Tools for bigger data................................................. 403
17.2.1 Data and memory structures for big data......................... 403
17.2.2 Compilation..................................................... 404
17.2.3 Parallel and distributed computing.............................. 404
17.2.4 Alternatives to SQL............................................. 411
17.3 Alternatives to R..................................................... 413
17.4 Closing thoughts...................................................... 413
17.5 Further resources..................................................... 413
IV Appendices 415
A Packages used in this book 417
A.l The rnclsr package..................................................... 417
A.2 The etl package suite.................................................. 417
A.3 Other packages......................................................... 418
A.4 Further resources...................................................... 420
CONTENTS
xiii
B Introduction to R and RStudio 421
B.l Installation............................................................ 421
B. 1.1 Installation under Windows....................................... 422
B. 1.2 Installation under Mac OS X...................................... 422
B. 1.3 Installation under Linux......................................... 422
B.1.4 RStudio............................................................ 422
B.2 Running RStudio and sample session...................................... 422
B.3 Learning R.............................................................. 424
B.3.1 Getting help ...................................................... 424
B.3.2 swirl.............................................................. 426
B.4 Fundamental structures and objects...................................... 427
B.4.1 Objects and vectors................................................ 427
B.4.2 Operators.......................................................... 428
B.4.3 Lists.............................................................. 429
B. 4.4 Matrices.......................................................... 429
B.4.5 Dataframes......................................................... 430
B.4.6 Attributes and classes............................................. 431
B.4.7 Options............................................................ 434
B. 4.8 Functions......................................................... 434
B.5 Add-ons: Packages....................................................... 435
B.5.1 Introduction to packages........................................... 435
B.5.2 CRAN task views ................................................... 436
B.5.3 Session information................................................ 436
B.5.4 Packages and name conflicts....................................... 438
B.5.5 Maintaining packages .............................................. 438
B.5.6 Installed libraries and packages................................... 438
B.6 Further resources....................................................... 439
B. 7 Exercises............................................................... 439
C Algorithmic thinking 443
C. l Introduction............................................................ 443
C.2 Simple example.......................................................... 443
C.3 Extended example: Law of large numbers.................................. 446
C.4 Non-standard evaluation ................................................ 448
C.5 Debugging and defensive coding.......................................... 452
C.6 Further resources....................................................... 453
C. 7 Exercises............................................................... 454
D Reproducible analysis and workflow 455
D. l Scriptable statistical computing ....................................... 456
D.2 Reproducible analysis with R Markdown................................... 456
D.3 Projects and version control............................................ 459
D.4 Further resources....................................................... 459
D. 5 Exercises............................................................... 461
E Regression modeling 465
E. l Simple linear regression................................................ 465
E.1.1 Motivating example: Modeling usage of a rail trail.............. 466
E.l.2 Model visualization................................................ 467
E.1.3 Measuring the strength of fit..................................... 467
E.1.4 Categorical explanatory variables.................................. 469
xiv CONTENTS
E.2 Multiple regression................................................... 470
E.2.1 Parallel slopes: Multiple regression with a categorical
variable........................................................ 470
E.2.2 Parallel planes: Multiple regression with a second
quantitative variable........................................... 471
E.2.3 Non-parallel slopes: Multiple regression with interaction........ 472
E. 2.4 Modelling non-linear relationships............................. 472
E.3 Inference for regression.............................................. 474
E.4 Assumptions underlying regression..................................... 475
E.5 Logistic regression................................................. 477
E.6 Further resources . .................................................. 481
E. 7 Exercises........................................................... 482
F Setting up a database server 487
F. l SQLite ............................................................... 487
F.2 MySQL................................................................. 488
F. 2.1 Installation................................................... 488
F.2.2 Access........................................................... 488
F.2.3 Running scripts from the command line............................ 491
F.3 PostgreSQL............................................................ 491
F.4 Connecting to SQL .................................................. 492
F.4.1 The command line client.......................................... 492
F.4.2 GUIs............................................................. 492
F.4.3 R and RStudio ................................................... 492
F.4.4 Load into SQLite database........................................ 497
Bibliography 499
Indices 513
Subject index.............................................................. 514
R index.................................................................... 543
|
| any_adam_object | 1 |
| author | Baumer, Benjamin S. Kaplan, Daniel T. 1959- Horton, Nicholas J. |
| author_GND | (DE-588)17165496X (DE-588)1034622552 |
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| dewey-full | 006.3/12 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik Psychologie Mathematik |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV044199822 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:46:25Z |
| institution | BVB |
| isbn | 9781498724487 |
| language | English |
| lccn | 016042514 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029606424 |
| oclc_num | 974526621 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-945 DE-M49 DE-BY-TUM DE-739 DE-11 DE-1049 DE-898 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR DE-945 DE-M49 DE-BY-TUM DE-739 DE-11 DE-1049 DE-898 DE-BY-UBR |
| physical | xxvi, 551 Seiten Illustrationen, Diagramme, Karten (farbig) |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Texts in statistical science |
| spelling | Baumer, Benjamin S. Verfasser aut Modern data science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton Boca Raton ; London ; New York CRC Press [2017] © 2017 xxvi, 551 Seiten Illustrationen, Diagramme, Karten (farbig) txt rdacontent n rdamedia nc rdacarrier Texts in statistical science Datenanalyse (DE-588)4123037-1 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Datenanalyse (DE-588)4123037-1 s Statistik (DE-588)4056995-0 s Data Mining (DE-588)4428654-5 s Big Data (DE-588)4802620-7 s R Programm (DE-588)4705956-4 s DE-604 Kaplan, Daniel T. 1959- Verfasser (DE-588)17165496X aut Horton, Nicholas J. Verfasser (DE-588)1034622552 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029606424&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Baumer, Benjamin S. Kaplan, Daniel T. 1959- Horton, Nicholas J. Modern data science with R Datenanalyse (DE-588)4123037-1 gnd R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd Big Data (DE-588)4802620-7 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4705956-4 (DE-588)4056995-0 (DE-588)4428654-5 (DE-588)4802620-7 (DE-588)4123623-3 |
| title | Modern data science with R |
| title_auth | Modern data science with R |
| title_exact_search | Modern data science with R |
| title_full | Modern data science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton |
| title_fullStr | Modern data science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton |
| title_full_unstemmed | Modern data science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton |
| title_short | Modern data science with R |
| title_sort | modern data science with r |
| topic | Datenanalyse (DE-588)4123037-1 gnd R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | Datenanalyse R Programm Statistik Data Mining Big Data Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029606424&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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