This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. 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 background programming 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.

Provides a user-friendly beginners guide to the key concepts in the digital humanities by focusing on four major types of humanities data structures: networks, text corpora, geospatial data, and images Assumes no prior programming experience Suitable for a one-semester course at the university level for undergraduate, graduate and professional students, or as half or full-day tutorial or seminar

Autorentext

Taylor Arnold is Professor of Data Science & Statistics at the University of Richmond and affiliated faculty in the interdisciplinary programs in linguistics and cognitive science. His research applies and develops corpus-based techniques and software to study how messages are communicated through visual and multimodal forms. Arnold is the co-author of four books: Humanities Data in R: Exploring Networks, Geospatial Data, Images and Texts (Springer, 2015), A Computational Approach to Statistical Learning (CRC Press, 2019), Layered Lives (Stanford University Press, 2022), and Distant Viewing: Analyzing Visual Culture at Scale (MIT Press, 2023). Lauren Tilton is the E. Claiborne Robins Professor of Liberal Arts and Digital Humanities in the Department of Rhetoric and Communication Studies at the University of Richmond. Her research focuses on 20th and 21st century U.S. visual culture. She is director of Photogrammar, a digital public humanities project mapping New Deal and World War II documentary expression funded by the ACLS and NEH, and author of Humanities Data in R: Exploring Networks, Geospatial Data, Images and Texts (Springer, 2015), Layered Lives (Stanford University Press, 2022), and Distant Viewing: Analyzing Visual Culture at Scale (MIT Press, 2023). Her scholarship has appeared in journals such as American Quarterly, Archive Journal, Digital Humanities Quarterly, and Digital Scholarship in the Humanities. She is the co-editor of Computational Humanities (Debates in the Digital Humanities), currently in production with the University of Minnesota Press.

Inhalt
Set-up.- A Short Introduction to R.- EDA I Continuous and Categorical Data.- EDA II Multivariate Analysis.- EDA III Advanced Graphics.- Networks.- Geospatial Data.- Image Data.- Natural Language Processing.- Text Analysis.- Appendix.