Humanities Data in R

Taylor Arnold is Senior Scientist at AT&T Labs Research and Lecturer of Statistics at Yale University. His research focuses on statistical computing, numerical linear algebra, and machine learning. He is the technical director of Photogrammar (photogrammar.yale.edu).Lauren Tilton is a doctoral candidate in American Studies at Yale University. Her interests include documentary media, 20th century history, and visual culture. She is an active member of the digital humanities community, serving as the humanities director of Photogrammar and co-Principal Investigator of the Participatory Media project.

Arnold and Tilton are a brilliant team, and this highly accessible book will appeal to a wide range of digital humanists. The text analysis chapters are very good, and the authors' work to develop an R package for interacting with the Stanford CoreNLP java Library fills a huge hole in the R text processing landscape.Matthew L. Jockers, University of Nebraska-Lincoln; author of Text Analysis with R for Students of Literature (Springer, 2014)This is the first book that covers analysis of all main parts of humanities data: texts, images, geospatial data, and networks. Now digital humanities finally has its perfect textbook. This is the book many of us were awaiting for years. It teaches you R (the most widely used open source data analysis platform today worldwide) using many examples. The writing is very clear, and information is organized in a logical and easy to follow manner. Whether you are just considering working with humanities data or already have experience, this is the must read book.Lev Manovich, The Graduate Center, City University of New York; author of The Language of New Media (MIT, 2001)This book gives a concise yet broadly accessible introduction to R, through the lens of exploratory data analysis, coupled with well-planned forays into key humanities data types and their analysis -- including a nice primer on network analysis.Eric D. Kolaczyk, Boston University; author of Statistical Analysis of Network Data with R (Springer, 2014)

• ​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.