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Text analysis with R : for students of literature /

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Bibliographic Details
Main Author: Jockers, Matthew Lee, 1966-
Corporate Author: EBSCOhost
Other Authors: Thalken, Rosamond
Format: eBook
Language:English
Published: Cham : Springer, 2020.
Edition:2nd ed.
Series:Quantitative methods in the humanities and social sciences.
Subjects:
Computational linguistics.
R (Computer program language)
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Preface to the Second Edition
  • Preface from the First Edition (Still Relevant)
  • Contents
  • About the Authors
  • List of Figures
  • List of Tables
  • Part I Microanalysis
  • 1 R Basics
  • 1.1 Introduction
  • 1.2 Download and Install R
  • 1.3 Download and Install RStudio
  • 1.4 Download the Supporting Materials
  • 1.5 RStudio
  • 1.6 Let's Get Started
  • 1.7 Saving Commands and R Scripts
  • 1.8 Assignment Operators
  • 1.9 Practice
  • References
  • 2 First Foray into Text Analysis with R
  • 2.1 Loading the First Text File
  • 2.2 A Word About Warnings, Errors, Typos, and Crashes
  • 2.3 Separate Content from Metadata
  • 2.4 Reprocessing the Content
  • 2.5 Beginning Some Analysis
  • 2.6 Practice
  • 3 Accessing and Comparing Word Frequency Data
  • 3.1 Introduction
  • 3.2 Start Up Code
  • 3.3 Accessing Word Data
  • 3.4 Recycling
  • 3.5 Practice
  • 4 Token Distribution and Regular Expressions
  • 4.1 Introduction
  • 4.2 Start Up Code
  • 4.3 A Word About Coding Style
  • 4.4 Dispersion Plots
  • 4.5 Searching with grep
  • 4.6 Practice
  • Reference
  • 5 Token Distribution Analysis
  • 5.1 Cleaning the Workspace
  • 5.2 Start Up Code
  • 5.3 Identifying Chapter Breaks with grep
  • 5.4 The for Loop and if Conditional
  • 5.5 The for Loop in Eight Parts
  • 5.5.1
  • 5.5.2
  • 5.5.3
  • 5.5.4
  • 5.5.5
  • 5.5.6
  • 5.5.7
  • 5.5.8
  • 5.6 Accessing and Processing List Items
  • 5.6.1 rbind
  • 5.6.2 More Recycling
  • 5.6.3 apply
  • 5.6.4 do.call (do dot call)
  • 5.6.5 cbind
  • 5.7 Practice
  • 6 Correlation
  • 6.1 Introduction
  • 6.2 Start Up Code
  • 6.3 Correlation Analysis
  • 6.4 A Word About Data Frames
  • 6.5 Testing Correlation with Randomization
  • 6.6 Practice
  • 7 Measures of Lexical Variety
  • 7.1 Lexical Variety and the Type-Token Ratio
  • 7.2 Start Up Code
  • 7.3 Mean Word Frequency
  • 7.4 Extracting Word Usage Means
  • 7.5 Ranking the Values
  • 7.6 Calculating the TTR inside lapply
  • 7.7 A Further Use of Correlation
  • 7.8 Practice
  • Reference
  • 8 Hapax Richness
  • 8.1 Introduction
  • 8.2 Start Up Code
  • 8.3 sapply
  • 8.4 An Inline Conditional Function
  • 8.5 Practice
  • 9 Do It KWIC
  • 9.1 Introduction
  • 9.2 Custom Functions
  • 9.3 A Tokenization Function
  • 9.4 Finding Keywords and Their Contextual Neighbors
  • 9.5 Practice
  • Reference
  • 10 Do It KWIC(er) (and Better)
  • 10.1 Getting Organized
  • 10.2 Separating Functions for Reuse
  • 10.3 User Interaction
  • 10.4 readline
  • 10.5 Building a Better KWIC Function
  • 10.6 Fixing Some Problems
  • 10.7 Practice
  • Part II Metadata
  • 11 Introduction to dplyr
  • 11.1 Start Up Code
  • 11.2 Using stack to Create a Data Frame
  • 11.3 Installing and Loading dplyr
  • 11.4 Using mutate, filter, arrange, and select
  • 11.4.1 Mutate
  • 11.4.2 filter
  • 11.4.3 select
  • 11.4.4 arrange
  • 11.5 Practice
  • 12 Parsing TEI XML
  • 12.1 Introduction
  • 12.2 The Text Encoding Initiative (TEI)
  • 12.3 Parsing XML with R Using the Xml2 Package
  • 12.4 Accessing the Textual Content