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Humanities data in R = exploring networks, geospatial data, images, and text /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Humanities data in R/ by Taylor Arnold, Lauren Tilton.
Reminder of title:	exploring networks, geospatial data, images, and text /
Author:	Arnold, Taylor.
other author:	Tilton, Lauren.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xiv, 284 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Mathematical statistics. -
Online resource:	https://doi.org/10.1007/978-3-031-62566-4
ISBN:	9783031625664

Humanities data in R = exploring networks, geospatial data, images, and text /
Arnold, Taylor.

Humanities data in Rexploring networks, geospatial data, images, and text /[electronic resource] :by Taylor Arnold, Lauren Tilton. - Second edition. - Cham :Springer International Publishing :2024. - xiv, 284 p. :ill. (some col.), digital ;24 cm. - Quantitative methods in the humanities and social sciences,2199-0964. - Quantitative methods in the humanities and social sciences..

- Part I Core -- Working with Data in R -- EDA I: Grammar of Graphics -- EDA II: Organizing Data -- EDA III: Restructuring Data -- Collecting Data -- Part II Data Types -- Textual Data -- Network Data -- Temporal Data -- Spatial Data -- Image Data -- Part III Additional Methods -- Programming in R -- Data Formats.

This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts. This 2nd edition of Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins) Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets. Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. 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. 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.

ISBN: 9783031625664

Standard No.: 10.1007/978-3-031-62566-4doiSubjects--Topical Terms:

527941
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Humanities data in R = exploring networks, geospatial data, images, and text /
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520 $a This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts. This 2nd edition of Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins) Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets. Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. 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. 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.
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