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

1.3.1 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 <a> 24

2.3.2 The metadata tag <meta> 25

2.3.3 The external reference tag <link> 26

2.3.4 Emphasizing tags <b>, <i>, <strong> 26

2.3.5 The paragraphs tag <p> 27

2.3.6 Heading tags <h1>, <h2>, <h3>,… 27

2.3.7 Listing content with <ul>, <ol>, and <dl> 27

2.3.8 The organizational tags <div> and <span> 27

2.3.9 The <form> tag and its companions 29

2.3.10 The foreign script tag <script> 30

2.3.11 Table tags <table>, <tr>, <td>, and <th> 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

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.