Data science in R: a case studies approach to computational reasoning and problem solving
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla.
CRC Press
2015
|
| Schriftenreihe: | The R series
|
| Schlagworte: | |
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Beschreibung: | xxiii, 515 S. |
| ISBN: | 9781482234817 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Statistics
Data Science in R: A Case Studies Approach to Computational Reasoning
and Problem Solving illustrates the details involved in solving real computational
problems encountered in data analysis. It reveals the dynamic and iterative pro-
cess by which data analysts approach a problem and reason about different ways
of implementing solutions.
The book’s collection of projects, sample solutions, and follow-up exercises en-
compass practical topics pertaining to data processing, including non-standard
data formats, text processing and regular expressions, exploratory data analy-
sis, visualization, relational databases, simulation, algorithm implementation, big
data, and efficiency idioms. The text also covers statistical methods, such as
classification trees, k-nearest neighbors, and naive Bayes, as well as newer tech-
nologies, such as Web scraping and Google Earth.
Features
• Explores how computing is done for a broad range of data science problems
• Includes authentic real-world data analysis projects that tie concepts into a
data science workflow and illustrate the everyday activities of data scientists
across a spectrum of fields
• Shows how to read and transform raw data, manipulate and visualize the re-
sulting data, and use statistical techniques to solve a problem or understand
relationships between variables
• Describes the use of simulation to understand stochastic processes and
model interesting situations
• Covers various data technologies, including databases, visualization with
Keyhole Markup Language, and scraping data from Web pages with HTTP
requests and text processing
Blending computational details with statistical and data analysis concepts, this
book provides you with an understanding of how professional data scientists
think about daily computational tasks. It will improve your computational reason-
ing of real-world data analyses.
Contents
Preface xv
Acknowledgments xix
Authors xxi
Co-Authors xxiii
I Data Manipulation and Modeling 1
1 Predicting Location via Indoor Positioning Systems 3
Deborah Nolan and Duncan Temple Lang
1.1 Introduction.......................................................... 3
1.1.1 Computational Topics ........................................... 4
1.2 The Raw Data ......................................................... 4
1.2.1 Processing the Raw Data......................................... 8
1.3 Cleaning the Data and Building a Representation for Analysis ........ 12
1.3.1 Exploring Orientation ......................................... 14
1.3.2 Exploring MAC Addresses........................................ 16
1.3.3 Exploring the Position of the Hand-Held Device................. 18
1.3.4 Creating a Function to Prepare the Data........................ 19
1.4 Signal Strength Analysis............................................. 21
1.4.1 Distribution of Signal Strength ............................... 21
1.4.2 The Relationship between Signal and Distance................... 26
1.5 Nearest Neighbor Methods to Predict Location ........................ 31
1.5.1 Preparing the Test Data........................................ 31
1.5.2 Choice of Orientation.......................................... 32
1.5.3 Finding the Nearest Neighbors.................................. 34
1.5.4 Cross-Validation and Choice of k............................... 36
1.6 Exercises ........................................................... 41
2 Modeling Runners’ Times in the Cherry Blossom Race 45
Daniel Kaplan and Deborah Nolan
2.1 Introduction......................................................... 45
2.1.1 Computational Topics .......................................... 47
2.2 Reading Tables of Race Results into R................................ 47
2.3 Data Cleaning and Reformatting Variables ............................ 55
2.4 Exploring the Run Time for All Male Runners ......................... 63
2.4.1 Making Plots with Many Observations............................ 63
2.4.2 Fitting Models to Average Performance.......................... 67
2.4.3 Cross-Sectional Data and Covariates............................ 74
2.5 Constructing a Record for an Individual Runner across Years ......... 79
2.6 Modeling the Change in Running Time for Individuals.................. 88
ix
x Contents
2.7 Scraping Race Results from the Web...................................... 93
2.8 Exercises ............................................................. 100
3 Using Statistics to Identify Spam 105
Deborah Nolan and Duncan Temple Lang
3.1 Introduction........................................................... 105
3.1.1 Computational Topics ............................................ 106
3.2 Anatomy of an email Message............................................ 107
3.3 Reading the email Messages............................................. 110
3.4 Text Mining and Naïve Bayes Classification ............................ 113
3.5 Finding the Words in a Message ........................................ 116
3.5.1 Splitting the Message into Its Header and Body................... 116
3.5.2 Removing Attachments from the Message Body....................... 117
3.5.3 Extracting Words from the Message Body.......................... 124
3.5.4 Completing the Data Preparation Process.......................... 126
3.6 Implementing the Naïve Bayes Classifier................................ 127
3.6.1 Test and Training Data........................................... 128
3.6.2 Probability Estimates from Training Data......................... 129
3.6.3 Classifying New Messages......................................... 131
3.6.4 Computational Considerations..................................... 135
3.7 Recursive Partitioning and Classification Trees ....................... 138
3.8 Organizing an email Message into an R Data Structure .................. 140
3.8.1 Processing the Header............................................ 141
3.8.2 Processing Attachments........................................... 144
3.8.3 Testing Our Code on More email Data.............................. 146
3.8.4 Completing the Process .......................................... 148
3.9 Deriving Variables from the email Message ............................. 150
3.9.1 Checking Our Code for Errors..................................... 155
3.10 Exploring the email Feature Set........................................ 158
3.11 Fitting the rpartQ Model to the email Data ............................ 160
3.12 Exercises ............................................................. 164
4 Processing Robot and Sensor Log Files: Seeking a Circular Target 171
Samuel E. Buttrey, Timothy H. Chung, James N. Eagle, and Duncan Temple Lang
4.1 Description ........................................................... 171
4.1.1 Computational Topics ............................................ 172
4.2 The Data .............................................................. 173
4.2.1 Reading an Entire Log File ...................................... 175
4.2.2 Exploring Log Files.............................................. 179
4.2.3 Visualizing the Path............................................. 184
4.2.4 Exploring a “Look”............................................... 187
4.2.5 The Error Distribution for Range Values.......................... 190
4.3 Detecting a Circular Target ........................................... 194
4.3.1 Connecting Segments Behind the Robot............................. 198
4.3.2 Determining If a Segment Corresponds to a Circle................. 200
4.4 Detecting the Target with Streaming Data in Real Time................. 213
Contents ХІ
5 Strategies for Analyzing a 12-Gigabyte Data Set: Airline Flight Delays 217
Michael Kane
5.1 Introduction......................................................... 217
5.1.1 Computational Tòpics ......................................... 218
5.2 Acquiring the Airline Data Set ..................................... 219
5.3 Computing with Massive Data: Getting Flight Delay Counts ........... 219
5.3.1 The R Programming Environment .......................... 219
5.3.2 The UNIX Shell................................................ 221
5.3.3 An SQL Database with R,....................................... 223
5.3.4 The bigmemory Package with R................................. 227
5.4 Explorations Using Parallel Computing: The Distribution of Flight Delays 229
5.4.1 Writing a Parallelizable Loop with foreach................... 230
5.4.2 Using the Split-Apply-Combine Approach for Better Performance . 231
5.4.3 Using Split-Apply-Combine to Find the Best Time to Fly....... 232
5.5 From Exploration to Model: Do Older Planes Suffer Greater Delays? . . . 236
II Simulation Studies 239
6 Pairs Trading 241
Cari Kaufman and Duncan Temple Lång
6.1 The Problem ........................................................ 241
6.1.1 Computational Topics ........................................ 245
6.2 The Data Format..................................................... 246
6.3 Reading the Financial Data.......................................... 247
6.4 Visualizing the Time Series ........................................ 250
6.5 Finding Opening and Closing Positions .............................. 251
6.5.1 Identifying a Position....................................... 251
6.5.2 Displaying Positions ........................................ 254
6.5.3 Finding All Positions........................................ 256
6.5.4 Computing the Profit for a Position.......................... 257
6.5.5 Finding the Optimal Value for k.............................. 260
6.6 Simulation Study.................................................... 263
6.6.1 Simulating the Stock Price Series............................ 265
6.6.2 Making stockSimQ Faster...................................... 273
7 Simulation Study of a Branching Process 277
Deborah Nolan and Duncan Temple Lång
7.1 Introduction........................................................ 277
7.1.1 The Monte Carlo Method....................................... 279
7.1.2 Computational Topics ........................................ 281
7.2 Exploring the Random Process ....................................... 281
7.3 Generating Offspring................................................ 284
7.3.1 Checking the Results......................................... 286
7.3.2 Considering Alternative Implementations...................... 287
7.4 Profiling and Improving Our Code .................................. 289
7.5 From One Job s Offspring to an Entire Generation ................... 290
7.6 Unit Testing ....................................................... 292
7.7 A Structure for the Function s Return Value ........... 293
7.8 The Family Tree: Simulating the Branching Process .................. 294
7.9 Replicating the Simulation ......................................... 299
7.9.1 Analyzing the Simulation Results............................. 301
xii Contents
7.10 Exercises ............................................................ 306
8 A Self-Organizing Dynamic System with a Phase Transition 309
Deborah Nolan and Duncan Temple Lang
8.1 Introduction and Motivation .......................................... 309
8.1.1 Computational Topics ......................................... 310
8.2 The Model ............................................................ 310
8.2.1 The Order Cars Move ........................................... 312
8.3 Implementing the BML Model .................................... 314
8.3.1 Creating the Initial Grid Configuration........................ 314
8.3.2 Testing the Grid Creation Function............................. 318
8.3.3 Displaying the Grid............................................ 321
8.3.4 Visualizing the Grid .......................................... 322
8.3.5 Simple and Convenient Object-Oriented Programming.............. 325
8.3.6 Moving the Cars ............................................... 327
8.4 Evaluating the Performance of the Code................................ 334
8.5 Implementing the BAIL Alodel in C ................................... 346
8.5.1 The Algorithm in C............................................. 348
8.5.2 Compiling, Loading, and Calling the C Code .................... 355
8.6 Running the Simulations............................................... 359
8.6.1 Exploring Car Velocity......................................... 360
8.7 Experimental Compilation.............................................. 362
9 Simulating Blackjack 367
Hadley Wickham
9.1 Introduction.......................................................... 367
9.1.1 Computational Topics .......................................... 368
9.2 Blackjack Basics ..................................................... 368
9.2.1 Testing Functions.............................................. 370
9.3 Playing a Hand of Blackjack........................................... 372
9.3.1 Creating Functions for the Player’s Actions ................... 373
9.4 Strategies for Playing ............................................... 376
9.4.1 Developing the Optimal Strategy................................ 379
9.5 Playing Alany Games .................................................. 382
9.6 A More Accurate Card Dealer Shoe ..................................... 384
9.7 Counting Cards........................................................ 390
9.8 Putting It All Together .............................................. 393
9.9 Exercises ............................................................ 394
III Data and Web Technologies 397
10 Baseball: Exploring Data in a Relational Database 399
Deborah Nolan and Duncan Temple Lang
10.1 Introduction.......................................................... 399
10.1.1 Computational Topics .......................................... 400
10.2 Sean Lahman s Database ............................................... 401
10.2.1 Connecting to the Baseball Database from within R ............. 401
10.3 Aggregating Salaries into Payroll .................................... 403
10.4 Merging Payroll Data with Information in Other Tables ................ 408
10.4.1 Adding Team Names to the Payroll Data.......................... 409
10.4.2 Adding World Series Records to the Payroll Data................ 411
Contents
Xlll
10.5 Exploring the Extreme Salades......................................... 412
10.6 Exercises ............................................................ 415
11 CIA Factbook Mashup 419
Deborah Nolan and Duncan Temple Lang
11.1 Introduction.......................................................... 419
11.1.1 Computational Topics .......................................... 421
11.2 Acquiring the Data.................................................... 421
11.2.1 Extracting Latitude and Longitude from a CSV File.............. 421
11.3 Integrating Data from Different Sources .............................. 423
11.4 Preparing the Data for Plotting....................................... 424
11.4.1 Redoing the Merge of the Factbook and Location Data............ 428
11.5 Plotting with Google Earth™........................................... 430
11.6 Extracting Demographic Information from the CIA XML File.............. 435
11.7 Generating KML Directly .............................................. 442
11.8 Additional Computational Tasks ........................... 448
11.8.1 Creating Plotting Symbols...................................... 448
11.8.2 Efficiency in Generating KML from Strings ..................... 448
11.8.3 Extracting Latitude and Longitude from an HTML File ........... 450
11.9 Exercises ............................................................ 451
12 Exploring Data Science Jobs with Web Scraping and Text Mining 457
Deborah Nolan and Duncan Temple Lang
12.1 Introduction and Motivation .......................................... 457
12.1.1 Computational Topics .......................................... 459
12.2 Exploring Different Web Sites......................................... 459
12.3 Preliminary/Exploratorv Scraping: The Kaggle Job List................. 465
12.3.1 Processing the Text............................................ 469
12.3.2 Generalizing to Other Posts.................................... 470
12.3.3 Scraping the Kaggle Post List.................................. 473
12.4 Scraping CyberCoders.com ............................................. 475
12.4.1 Getting the Skill List from a Job Post ........................ 478
12.4.2 Finding the Links to Job Postings in the Search Results........ 482
12.4.3 Finding the Next Page of Job Post Search Results............... 487
12.4.4 Putting It All Together........................................ 488
12.5 A Reusable Generic Framework for Arbitrary Sites...................... 489
12.6 Scraping Career Builder .............................................. 492
12.7 Scraping Monster.com ................................................. 494
12.8 Analyzing the Results: The Important Skills........................... 495
12.9 Note on Web Scraping................................................. 503
12.10 Exercises ......................................................... 503
Index 507
Colophon
515
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| spelling | Nolan, Deborah Ann 1955- Verfasser (DE-588)1024951448 aut Data science in R a case studies approach to computational reasoning and problem solving Deborah Nolan ; Duncan Temple Lang Boca Raton, Fla. CRC Press 2015 xxiii, 515 S. txt rdacontent n rdamedia nc rdacarrier The R series R Programm (DE-588)4705956-4 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Data Mining (DE-588)4428654-5 s Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s b DE-604 Lang, Duncan Temple Verfasser 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=028009850&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext 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=028009850&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nolan, Deborah Ann 1955- Lang, Duncan Temple Data science in R a case studies approach to computational reasoning and problem solving R Programm (DE-588)4705956-4 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
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| title | Data science in R a case studies approach to computational reasoning and problem solving |
| title_auth | Data science in R a case studies approach to computational reasoning and problem solving |
| title_exact_search | Data science in R a case studies approach to computational reasoning and problem solving |
| title_full | Data science in R a case studies approach to computational reasoning and problem solving Deborah Nolan ; Duncan Temple Lang |
| title_fullStr | Data science in R a case studies approach to computational reasoning and problem solving Deborah Nolan ; Duncan Temple Lang |
| title_full_unstemmed | Data science in R a case studies approach to computational reasoning and problem solving Deborah Nolan ; Duncan Temple Lang |
| title_short | Data science in R |
| title_sort | data science in r a case studies approach to computational reasoning and problem solving |
| title_sub | a case studies approach to computational reasoning and problem solving |
| topic | R Programm (DE-588)4705956-4 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | R Programm Data Mining Datenanalyse Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028009850&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028009850&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT nolandeborahann datascienceinracasestudiesapproachtocomputationalreasoningandproblemsolving AT langduncantemple datascienceinracasestudiesapproachtocomputationalreasoningandproblemsolving |