Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences) by Taylor Arnold

by Taylor Arnold

This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, andimages. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. "Humanities Data with R" does not presuppose backgroundprogramming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries. "

Hardcover

First published January 1, 2015

Reviews

[Grace · Rating 3 out of 5]

Not super accessible for a beginner; some mistakes in the sample code make it even more difficult. I found the focus to be strange sometimes as well. Overall, not a perfect introduction, but a good broad overview. Specifically for text analysis, Jockers' book provides a clearer intro.

[Kristin · Rating 2 out of 5]

Provides examples and sample datasets, but doesn't do a very good job of providing a template of how to create or work with your own data. The examples don't make clear which commands are specific to their examples, which are variables, and which are actual commands.