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This book offers an introduction to computational analysis for humanities scholars and researchers, systematically bridging traditional inquiry with the data-intensive capabilities of the R programming language. Employing diverse cultural datasets about gaming, film, literature, and music, it emphasizes the need to structure data, particularly through the tidy data paradigm, as a foundational practice for efficient analysis. Beginning with the fundamentals of R, the book progresses through sophisticated analytical techniques, including text mining, sentiment analysis, network analysis, and machine learning, all contextualized with rich cultural examples. In doing so, it demystifies complex computational procedures, empowering readers to apply these tools to critical cultural research and fostering a new generation of data-literate humanists equipped to navigate and interpret the increasingly digital cultural record.
Sommario
- 1. Introduction.- 2. Understanding Base R.- 3. Dplyr and TidyR.- 4. Understanding the Grammar of Graphics.- 5. Analyzing Text the Tidy Way.- 6. Understanding Regression.- 7. Tidy Networks.- 8. Machine Learning with Tidy Model.- 9. Conclusions.
Info autore
Nabeel Siddiqui is an Assistant Professor of Digital Media at Susquehanna University, where he also serves as the Director of the Center for Teaching and Learning. His research focuses on the digital humanities, computer vision, the history of technology, online culture, and data science. His work has appeared in numerous venues, including Digital Humanities Quarterly, the Programming Historian, and the International Journal of Digital Humanities.
Riassunto
This book offers an introduction to computational analysis for humanities scholars and researchers, systematically bridging traditional inquiry with the data-intensive capabilities of the R programming language. Employing diverse cultural datasets about gaming, film, literature, and music, it emphasizes the need to structure data, particularly through the ‘tidy data’ paradigm, as a foundational practice for efficient analysis. Beginning with the fundamentals of R, the book progresses through sophisticated analytical techniques, including text mining, sentiment analysis, network analysis, and machine learning, all contextualized with rich cultural examples. In doing so, it demystifies complex computational procedures, empowering readers to apply these tools to critical cultural research and fostering a new generation of data-literate humanists equipped to navigate and interpret the increasingly digital cultural record.
