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25.4 MB
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EC0FE5F1D229B2E7DBB0E9DDCF44B9081C5A1F63
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Sept. 16, 2025, 6:33 a.m.
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(Last updated: Sept. 16, 2025, 6:34 a.m.)
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| Siddiqui N. Cultural Analytics in R. A Tidy Approach 2025.pdf | 25.4 MB |
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24.8 MB
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| Uploaded by indexFroggy | Size 24.8 MB | Health [ 17 /0 ] | Added 2023-07-02 |
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28.8 MB
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SOURCE: Siddiqui N. Cultural Analytics in R. A Tidy Approach 2025
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MEDIAINFO
Textbook in PDF format 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. As cultural analysts deal with more data, we will need tools that are powerful enough to process and analyze vast and complex datasets efficiently. The choice of R for cultural analysis, however, may initially seem unconventional given its strong emphasis on statistics. As Norman Matloff notes, “R is written by statisticians, for statisticians,” and this is reflected in its history. Developed in the early 1990s at the University of Auckland by Ross Ihaka and Robert Gentleman, R emerged as a dialect of the programming language S. Ihaka and Gentleman were seeking out a piece of software that they could use in their Macintosh teaching lab oriented for statistics but found no suitable options. Consequently, they set out to create a language that would meet their needs and, in doing so, laid the foundation for one of the most versatile and widely used statistical programming environments for academic statisticians. From the beginning, R developed a large culture that encouraged the sharing of code among its users. In March 1997, Hornik and Leisch, of the Vienna University of Economics and Business, built the Comprehensive R Archive Network (CRAN), which serves as a repository of thousands of packages that extend the base R system. This system resulted in a paradigm shift where statisticians and data analysts could easily access a wealth of tools tailored to their specific needs. In the field of statistics and other quantitatively heavy fields, for example, it is still common to publish new methods in journal articles that are accompanied by R packages. These packages serve as a mechanism not just for dissemination but also for verifying computational claims, thereby fostering replicability and rigor in scholarly communication. Notably, the wide range of libraries developed for the language enables new users, including those in the humanities, to tailor its capabilities to their specific research questions. The Chapter 2 provides a comprehensive introduction to R programming. Readers gain a solid understanding of the fundamental concepts of R, ranging from basic mathematical operations and variable assignments to more complex data structures like vectors, data frames, and lists. Through hands-on exploration of a real-world dataset covering 120 years of Olympic athletes, the chapter demonstrates how base R functions can be applied to clean, subset, and analyze data to uncover insights into trends such as health, popularity, nationalism, and gender dynamics. Control structures such as conditional statements and loops are explained, with an emphasis on their practical application in R scripts. Finally, to visually represent data, the chapter covers basic plotting techniques using base R graphics, setting the stage for more advanced visualization tools available in the Tidyverse. Preface Introduction Understanding Base R Dplyr and TidyR Understanding the Grammar of Graphics Analyzing Text the Tidy Way Understanding Regression Tidy Networks Machine Learning with Tidy Models Conclusion
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