Automated Data Collection with R

"This book provides a unified framework of web scraping and information extraction from text data with R for the social sciences"--

Autorentext
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.

Klappentext
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.

Inhalt
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 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 , , and 27 2.3.8 The organizational tags and 27 2.3.9 The tag and its companions 29 2.3.10 The foreign script tag 30 2.3.11 Table tags , , , and 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 U…

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