Automated Data Collection with R (eBook)

Simon Munzert is the author of Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining, published by Wiley. Christian Rubba is the author of Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining, published by Wiley. Peter Meißner is the author of Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining, published by Wiley. Dominic Nyhuis is the author of Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining, published by Wiley.

Preface 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

1.4 Structure of the book 13

Part One A Primer on Web and Data Technologies 15

2 HTML 17

2.1 Browser presentation and source code 18

2.2 Syntax rules 19

2.3 Tags and attributes 24

2.4 Parsing 32

3 XML and JSON 41

3.1 A short example XML document 42

3.2 XML syntax rules 43

3.3 When is an XML document well formed or valid? 51

3.4 XML extensions and technologies 53

3.5 XML and R in practice 60

3.6 A short example JSON document 68

3.7 JSON syntax rules 69

3.8 JSON and R in practice 71

4 XPath 79

4.1 XPath--a query language for web documents 80

4.2 Identifying node sets with XPath 81

4.3 Extracting node elements 93

5 HTTP 101

5.1 HTTP fundamentals 102

5.2 Advanced features of HTTP 116

5.3 Protocols beyond HTTP 124

5.4 HTTP in action 126

6 AJAX 149

6.1 JavaScript 150

6.2 XHR 154

6.3 Exploring AJAX with Web Developer Tools 158

7 SQL and relational databases 164

7.1 Overview and terminology 165

7.2 Relational Databases 167

7.3 SQL: a language to communicate with Databases 175

7.4 Databases in action 188

8 Regular expressions and essential string functions 196

8.1 Regular expressions 198

8.2 String processing 207

8.3 A word on character encodings 214

Part Two A Practical Toolbox forWeb Scraping and Text Mining 219

9 Scraping the Web 221

9.1 Retrieval scenarios 222

9.2 Extraction strategies 270

9.3 Web scraping: Good practice 278

9.4 Valuable sources of inspiration 290

10 Statistical text processing 295

10.1 The running example: Classifying press releases of the British government 296

10.2 Processing textual data 298

10.3 Supervised learning techniques 307

10.4 Unsupervised learning techniques 313

11 Managing data projects 322

11.1 Interacting with the file system 322

11.2 Processing multiple documents/links 323

11.3 Organizing scraping procedures 328

11.4 Executing R scripts on a regular basis 334

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

12.4 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

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

16 Gathering data on mobile phones 396

16.1 Page exploration 396

16.2 Scraping procedure 404

16.3 Graphical analysis 406

16.4 Data storage 408

17 Analyzing sentiments of product reviews 416

17.1 Introduction 416

17.2 Collecting the data 417

17.3 Analyzing the data 426

17.4 Conclusion 434

References 435

General index 442

Package index 448

Function index 449