Quantitative Corpus Linguistics with R: A Practical Introduction
The first textbook of its kind, Quantitative Corpus Linguistics with R demonstrates how to use the open source programming language R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to achieve – searching and processing corpora, arranging and outputting the results of corpus searches, statistical evaluation, and graphing.
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The first textbook of its kind, Quantitative Corpus Linguistics with R demonstrates how to use the open source programming language R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to achieve — searching and processing corpora, arranging and outputting the results of corpus searches, statistical evaluation, and graphing.
Acknowledgments viii1 Introduction 11.1 Why Another Introduction to Corpus Linguistics? 11.2 Outline of the Book 41.3 Recommendation for Instructors 52 The Three Central Corpus-linguistic Methods 72.1 Corpora 72.1.1 What is a Corpus? 72.1.2 What Kinds of Corpora are There? 92.2 Frequency Lists 122.3 Lexical Co-occurrence: Collocations 142.4 (Lexico-) Grammatical Co-occurrence: Concordances 163 An Introduction to R 193.1 A Few Central Notions: Data Structures, Functions, and Arguments 233.2 Vectors 283.2.1 Basics 283.2.2 Loading Vectors 323.2.3 Accessing and Processing (Parts of) Vectors 353.2.4 Saving Vectors 423.3 Factors 433.4 Data Frames 443.4.1 Generating Data Frames 443.4.2 Loading and Saving Data Frames 463.4.3 Accessing and Processing (Parts of) Data Frames 483.5 Lists 533.6 Elementary Programming Functions 593.6.1 Conditional Expressions 593.6.2 Loops 603.6.3 Rules of Programming 643.7 Character/String Processing 683.7.1 Getting Information from and Accessing (Vectors of) Character Strings 693.7.2 Elementary Ways to Change (Vectors of) Character Strings 703.7.3 Merging and Splitting (Vectors of) Character Strings without Regular Expressions 703.7.4 Searching and Replacing without Regular Expressions 723.7.5 Searching and Replacing with Regular Expressions 793.7.6 Merging and Splitting (Vectors of) Character Strings with Regular Expressions 963.8 File and Directory Operations 994 Using R in Corpus Linguistics 1054.1 Frequency Lists 1064.1.1 A Frequency List of an Unannotated Corpus 1064.1.2 A Reverse Frequency List of an Unannotated Corpus 1104.1.3A Frequency List of an Annotated Corpus 1124.1.4 A Frequency List of Tag-word Sequences from an Annotated Corpus 1144.1.5 A Frequency List of Word Pairs from an Annotated Corpus 1184.1.6 A Frequency List of an Annotated Corpus (with One Word Per Line) 1244.1.7 A Frequency List of Word Pairs of an Annotated Corpus (with One Word Per Line) 1264.2 Concordances 1274.2.1 A Concordance of an Unannotated Text File 1274.2.2 A Simple Concordance from Files of a POS-tagged (SGML) Corpus 1354.2.3 More Complex Concordances from Files of a POS-tagged (SGML) Corpus 1414.2.4 A Lemma-based Concordance from Files of a POS-tagged and Lemmatized (XML) Corpus 1464.3 Collocations 1494.4 Excursus 1: Processing Multi-tiered Corpora 1564.5 Excursus 2: Unicode 1664.5.1 Frequency Lists 1674.5.2 Concordancing 1695 Some Statistics for Corpus Linguistics 1735.1 Introduction to Statistical Thinking 1745.1.1 Variables and their Roles in an Analysis 1745.1.2 Variables and their Information Value 1745.1.3 Hypotheses: Formulation and Operationalization 1765.1.4 Data Analysis 1825.1.5 Hypothesis (and Significance) Testing 1835.2 Categorical Dependent Variables 1895.2.1 One Categorical Dependent Variable, No Independent Variable 1895.2.2 One Categorical Dependent Variable, One Categorical Independent Variable 1925.2.3 One Categorical Dependent Variable, 2+ Independent Variables 2005.3 Interval/Ratio-scaled Dependent Variables 2015.3.1 Descriptive Statistics for Interval/Ratio-scaled Dependent Variables 2015.3.2 One Interval/Ratio-scaled Dependent Variable, One Categorical Independent Variable 2055.3.3 One Interval/Ratio-scaled Dependent Variable, One Interval/Ratio-scaled Independent Variable 2115.3.4 One Interval/Ratio-scaled Dependent Variable, 2+ Independent Variables 2145.4 Customizing Statistical Plots 2155.5 Reporting Results 2156 Case Studies and Pointers to Other Applications 2196.1 Introduction to the Case Studies 2196.2 Some Pointers to Further Applications 220Appendix 225References 229Endnotes 237Index 243
