Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text

Book Preface

There has been a rapid increase in the application of computational methods to humanities data in recent years. Numerousworkshops, lectures, bootcamps, blogs, and texts have arisen to provide an introduction to these techniques. Many of these are very well presented and have enabled humanistswith minimal technical background to quickly produce an impressive array of novel applications and scholarship.

The goal of this text is to complement rather than duplicate this extant body of work. We aim to address two distinct groups of readers: students in a one- or twosemester introductory course on digital methods in the humanities and intermediate users looking for a self-study text to solidify and extend their basic working knowledge of both computational methods and R. While entirely self-contained, the text moves at a pace that may be difficult for complete beginners without supplementary materials such as additional R tutorials or the support and structure of a formal classroom.

A particular challenge of applying computational methods in the humanities is that data is often unstructured and complex. Typical examples include large text corpora, archives of digital images, and geospatially enriched databases. Each of these require customized techniques for visualization and analysis, none of which are commonly taught in introductory texts in statistics. As such, this text is structured around the four basic data types commonly encountered in digital humanities: networks, geospatial data, images, and text. Dedicated chapters present techniques specific to each of these data types, preceded by an introduction to the general principles of exploratory data analysis. The result is a single text that brings together several disparate methodologies, preparing students and scholars to integrate computational methods into their own work.

Contents

Part I Basics

• 1 Set-Up
• 2 A Short Introduction to R
• 3 EDA I: Continuous and Categorical Data
• 4 EDA II: Multivariate Analysis
• 5 EDA III: Advanced Graphics

Part II Humanities Data Types

• 6 Networks
• 7 Geospatial Data
• 8 Image Data
• 9 Natural Language Processing
• 10 Text Analysis

Part III Appendix

• 11 R Packages
• 12 100 Basic Programming Exercises
• 13 100 Basic Programming Solutions