Название: Cultural Analytics in R: A Tidy Approach
Автор: Nabeel Siddiqui
Издательство: Springer
Серия: Use R!
Год: 2025
Страниц: 309
Язык: английский
Формат: pdf (true), epub
Размер: 50.1 MB

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.

Who Is This Book for?
Despite the growing amount of cultural data available, training in the humanities remains tied to “traditional” approaches that sometimes overlook the analytic possibilities offered by digital methods. Few humanities scholars today have training in quantitative methods or possess the coding skills that are essential to their peers in computer science or data science. In fact, for many humanities scholars, the very notion of “data” can seem alien and overly empirical, an imposition of scientific methodology on the study of human culture and history that may not always seem appropriate. As Nick Redfern argues, humanities scholars see their disciplines as bound by an epistemological foundation that relies on qualitative analysis, which creates a resistance to approaches that may be perceived as reductionist. Thus:
“Because the methodologies used by digital humanists come from the sciences…the knowledge they produce is the product of the scientific method and is, therefore, scientific knowledge. By definition, the knowledge produced by the digital humanities cannot be a part of humanist knowledge about culture because it is produced by the wrong way of knowing.”

This book assumes little familiarity with Data Science or programming and none in R itself. In fact, for those coming with a background in other programming languages, some of the concepts in R may seem counterintuitive at first due to their unique syntax. I have purposefully avoided as much math as possible and explained concepts through examples from the humanities whenever they are introduced. In this way, readers can see how these data science principles apply directly to their own fields and research questions. By grounding the discussion in familiar contexts, I hope to demystify the process and encourage a more intuitive understanding of the material. My goal is that those interested in understanding the background of the methodologies employed will find in this book both a guide and an encouragement to explore further.

That said, I will assume that you can download R and install the necessary packages on your computer. In the case that you encounter difficulties, resources are widely available online. For art historians, literary theorists, musicologists, and other cultural researchers, the thought of installing programming software and packages may seem daunting. However, the process has been significantly simplified over recent years, making it achievable even for those who may not consider themselves technically adept.