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Cultural Analytics in R: A Tidy Approach / by Nabeel Siddiqui.
Springer Nature - Springer Mathematics and Statistics (R0) eBooks 2025 English International Available online
View online- Format:
- Book
- Author/Creator:
- Siddiqui, Nabeel.
- Series:
- Use R!, 2197-5744
- Language:
- English
- Subjects (All):
- Quantitative research.
- Social sciences--Statistical methods.
- Social sciences.
- Digital humanities.
- Data Analysis and Big Data.
- Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
- Digital Humanities.
- Local Subjects:
- Data Analysis and Big Data.
- Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
- Digital Humanities.
- Physical Description:
- 1 online resource (292 pages)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- 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.
- Contents:
- - 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-96618-X
- OCLC:
- 1535978888
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