Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining -

Название:Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining
Автор: Simon Munzert, Christian Rubba, Peter Meissner, Dominic Nyhuis
Издательство: WILEY
Год: 2015
Страниц: 480
Язык: English
Формат: epub
Размер: 44,5 Mb
A hands on guide to web scraping and text mining for both beginners and experienced users of R
Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL.
Provides basic techniques to query web documents and data sets (XPath and regular expressions).
An extensive set of exercises are presented to guide the reader through each technique.
Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management.
Case studies are featured throughout along with examples for each technique presented.
R code and solutions to exercises featured in the book are provided on a supporting website.
 Показать / Скрыть текстPreface xv
1 Introduction 1
1.1 Case study: World Heritage Sites in Danger 1
1.2 Some remarks on web data quality 7
1.3 Technologies for disseminating, extracting, and storing web data 9
1.3.1 Technologies for disseminating content on the Web 9
1.3.2 Technologies for information extraction from web documents 11
1.3.3 Technologies for data storage 12
1.4 Structure of the book 13
Part One A Primer onWeb and Data Technologies 15
2 HTML 17
2.1 Browser presentation and source code 18
2.2 Syntax rules 19
2.2.1 Tags, elements, and attributes 20
2.2.2 Tree structure 21
2.2.3 Comments 22
2.2.4 Reserved and special characters 22
2.2.5 Document type definition 23
2.2.6 Spaces and line breaks 23
2.3 Tags and attributes 24
2.3.1 The anchor tag 24
2.3.2 The metadata tag 25
2.3.3 The external reference tag 26
2.3.4 Emphasizing tags , , 26
2.3.5 The paragraphs tag 27
2.3.6 Heading tags 27
2.3.7 Listing content with 27
2.3.8 The organizational tags 27
2.3.9 The tag and its companions 29
2.3.10 The foreign script tag 30
2.3.11 Table tags 32
2.4 Parsing 32
2.4.1 What is parsing? 33
2.4.2 Discarding nodes 35
2.4.3 Extracting information in the building process 37
Summary 38
Further reading 38
Problems 39
3 XML and JSON 41
3.1 A short example XML document 42
3.2 XML syntax rules 43
3.2.1 Elements and attributes 44
3.2.2 XML structure 46
3.2.3 Naming and special characters 48
3.2.4 Comments and character data 49
3.2.5 XML syntax summary 50
3.3 When is an XML document well formed or valid? 51
3.4 XML extensions and technologies 53
3.4.1 Namespaces 53
3.4.2 Extensions of XML 54
3.4.3 Example: Really Simple Syndication 55
3.4.4 Example: scalable vector graphics 58
3.5 XML and R in practice 60
3.5.1 Parsing XML 60
3.5.2 Basic operations on XML documents 63
3.5.3 From XML to data frames or lists 65
3.5.4 Event-driven parsing 66
3.6 A short example JSON document 68
3.7 JSON syntax rules 69
3.8 JSON and R in practice 71
Summary 76
Further reading 76
Problems 76
4 XPath 79
4.1 XPath—a query language for web documents 80
4.2 Identifying node sets with XPath 81
4.2.1 Basic structure of an XPath query 81
4.2.2 Node relations 84
4.2.3 XPath predicates 86
4.3 Extracting node elements 93
4.3.1 Extending the fun argument 94
4.3.2 XML namespaces 96
4.3.3 Little XPath helper tools 97
Summary 98
Further reading 99
Problems 99
5 HTTP 101
5.1 HTTP fundamentals 102
5.1.1 A short conversation with a web server 102
5.1.2 URL syntax 104
5.1.3 HTTP messages 106
5.1.4 Request methods 108
5.1.5 Status codes 108
5.1.6 Header fields 109
5.2 Advanced features of HTTP 116
5.2.1 Identification 116
5.2.2 Authentication 121
5.2.3 Proxies 123
5.3 Protocols beyond HTTP 124
5.3.1 HTTP Secure 124
5.3.2 FTP 126
5.4 HTTP in action 126
5.4.1 The libcurl library 127
5.4.2 Basic request methods 128
5.4.3 A low-level function of RCurl 131
5.4.4 Maintaining connections across multiple requests 132
5.4.5 Options 133
5.4.6 Debugging 139
5.4.7 Error handling 143
5.4.8 RCurl or httr—what to use? 144
Summary 144
Further reading 144
Problems 146
6 AJAX 149
6.1 javascript 150
6.1.1 How javascript is used 150
6.1.2 DOM manipulation 151
6.2 XHR 154
6.2.1 Loading external HTML/XML documents 155
6.2.2 Loading JSON 157
6.3 Exploring AJAX with Web Developer Tools 158
6.3.1 Getting started with Chrome’s Web Developer Tools 159
6.3.2 The Elements panel 159
6.3.3 The Network panel 160
Summary 161
Further reading 162
Problems 162
7 SQL and relational databases 164
7.1 Overview and terminology 165
7.2 Relational Databases 167
7.2.1 Storing data in tables 167
7.2.2 Normalization 170
7.2.3 Advanced features of relational databases and DBMS 174
7.3 SQL: a language to communicate with Databases 175
7.3.1 General remarks on SQL, syntax, and our running example 175
7.3.2 Data control language—DCL 177
7.3.3 Data definition language—DDL 178
7.3.4 Data manipulation language—DML 180
7.3.5 Clauses 184
7.3.6 Transaction control language—TCL 187
7.4 Databases in action 188
7.4.1 R packages to manage databases 188
7.4.2 Speaking R-SQL via DBI-based packages 189
7.4.3 Speaking R-SQL via RODBC 191
Summary 192
Further reading 193
Problems 193
8 Regular expressions and essential string functions 196
8.1 Regular expressions 198
8.1.1 Exact character matching 198
8.1.2 Generalizing regular expressions 200
8.1.3 The introductory example reconsidered 206
8.2 String processing 207
8.2.1 The stringr package 207
8.2.2 A couple more handy functions 211
8.3 A word on character encodings 214
Summary 216
Further reading 217
Problems 217
Part Two A Practical Toolbox forWeb Scraping and Text Mining 219
9 Scraping the Web 221
9.1 Retrieval scenarios 222
9.1.1 Downloading ready-made files 223
9.1.2 Downloading multiple files from an FTP index 226
9.1.3 Manipulating URLs to access multiple pages 228
9.1.4 Convenient functions to gather links, lists, and tables from HTML documents 232
9.1.5 Dealing with HTML forms 235
9.1.6 HTTP authentication 245
9.1.7 Connections via HTTPS 246
9.1.8 Using cookies 247
9.1.9 Scraping data from AJAX-enriched webpages with Selenium/Rwebdriver 251
9.1.10 Retrieving data from APIs 259
9.1.11 Authentication with OAuth 266
9.2 Extraction strategies 270
9.2.1 Regular expressions 270
9.2.2 XPath 273
9.2.3 Application Programming Interfaces 276
9.3 Web scraping: Good practice 278
9.3.1 Is web scraping legal? 278
9.3.2 What is robots.txt? 280
9.3.3 Be friendly! 284
9.4 Valuable sources of inspiration 290
Summary 291
Further reading 292
Problems 293
10 Statistical text processing 295
10.1 The running example: Classifying press releases of the British government 296
10.2 Processing textual data 298
10.2.1 Large-scale text operations—The tm package 298
10.2.2 Building a term-document matrix 303
10.2.3 Data cleansing 304
10.2.4 Sparsity and n-grams 305
10.3 Supervised learning techniques 307
10.3.1 Support vector machines 309
10.3.2 Random Forest 309
10.3.3 Maximum entropy 309
10.3.4 The RTextTools package 309
10.3.5 Application: Government press releases 310
10.4 Unsupervised learning techniques 313
10.4.1 Latent Dirichlet Allocation and correlated topic models 314
10.4.2 Application: Government press releases 314
Summary 320
Further reading 320
11 Managing data projects 322
11.1 Interacting with the file system 322
11.2 Processing multiple documents/links 323
11.2.1 Using for-loops 324
11.2.2 Using while-loops and control structures 326
11.2.3 Using the plyr package 327
11.3 Organizing scraping procedures 328
11.3.1 Implementation of progress feedback: Messages and progress bars 331
11.3.2 Error and exception handling 333
11.4 Executing R scripts on a regular basis 334
11.4.1 Scheduling tasks on Mac OS and Linux 335
11.4.2 Scheduling tasks on Windows platforms 337
Part Three A Bag of Case Studies 341
12 Collaboration networks in the US Senate 343
12.1 Information on the bills 344
12.2 Information on the senators 350
12.3 Analyzing the network structure 353
12.3.1 Descriptive statistics 354
12.3.2 Network analysis 356
12.4 Conclusion 358
13 Parsing information from semistructured documents 359
13.1 Downloading data from the FTP server 360
13.2 Parsing semistructured text data 361
13.3 Visualizing station and temperature data 368
14 Predicting the 2014 Academy Awards using Twitter 371
14.1 Twitter APIs: Overview 372
14.1.1 The REST API 372
14.1.2 The Streaming APIs 373
14.1.3 Collecting and preparing the data 373
14.2 Twitter-based forecast of the 2014 Academy Awards 374
14.2.1 Visualizing the data 374
14.2.2 Mining tweets for predictions 375
14.3 Conclusion 379
15 Mapping the geographic distribution of names 380
15.1 Developing a data collection strategy 381
15.2 Website inspection 382
15.3 Data retrieval and information extraction 384
15.4 Mapping names 387
15.5 Automating the process 389
Summary 395
16 Gathering data on mobile phones 396
16.1 Page exploration 396
16.1.1 Searching mobile phones of a specific brand 396
16.1.2 Extracting product information 400
16.2 Scraping procedure 404
16.2.1 Retrieving data on several producers 404
16.2.2 Data cleansing 405
16.3 Graphical analysis 406
16.4 Data storage 408
16.4.1 General considerations 408
16.4.2 Table definitions for storage 409
16.4.3 Table definitions for future storage 410
16.4.4 View definitions for convenient data access 411
16.4.5 Functions for storing data 413
16.4.6 Data storage and inspection 415
17 Analyzing sentiments of product reviews 416
17.1 Introduction 416
17.2 Collecting the data 417
17.2.1 Downloading the files 417
17.2.2 Information extraction 421
17.2.3 Database storage 424
17.3 Analyzing the data 426
17.3.1 Data preparation 426
17.3.2 Dictionary-based sentiment analysis 427
17.3.3 Mining the content of reviews 432
17.4 Conclusion 434
References 435
General index 442
Package index 448
Function index 449
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