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74.0 MB
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200D65C5445C8AE3BDE46F6D881E94ACCD9CBA11
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Aug. 30, 2025, 6:39 a.m.
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(Last updated: Sept. 20, 2025, 1:22 a.m.)
| File | Size |
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| Beecham R. Visualization for Social Data Science 2026.pdf | 74.0 MB |
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| Uploaded by mainlyclassical | Size 501.3 MB | Health [ 2 /0 ] | Added 2024-08-30 |
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SOURCE: Beecham R. Visualization for Social Data Science 2026
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COVER
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MEDIAINFO
Textbook in PDF format Visualization for Social Data Science provides end-to-end skills in visual data analysis. The book demonstrates how data graphics and modern statistics can be used in tandem to process, explore, model and communicate data-driven social science. It is packed with detailed data analysis examples, pushing you to do visual data analysis. As well as introducing, and demonstrating with code, a wide range of data visualizations for exploring patterns in data, Visualization for Social Data Science shows how models can be integrated with graphics to emphasise important structure and de-emphasise spurious structure and the role of data graphics in scientific communication -- in building trust and integrity. Many of the book’s influences are from data journalism, as well as information visualization and cartography. Visualization toolkits such as ggplot2, vega-lite and Tableau have been designed to ease the process of generating data graphics for analysis. There is a comprehensive set of texts and resources on visualization design theory, and several notable how-to primers on visualization practice. However, comparatively few existing resources demonstrate with real data and real social science scenarios how and why data graphics should be incorporated in a data analysis, and ultimately how they can be used to generate and claim knowledge. This book aims to fill this space. It presents principled workflows, with code, for using data graphics and statistics in tandem. In doing so it equips readers with critical design and technical skills needed to analyse and communicate with a range of datasets in the social sciences. All data collection, analysis and reporting activity will be completed using the open source statistical programming environment R. There are several benefits that come from being fully open-source, with a critical mass of users. Firstly, there is an array of online fora, tutorials and code examples from which to learn. Second, with such a large community, there are numerous expert R users who themselves contribute by developing packages that extend its use. Of particular importance is the Tidyverse. This is a set of packages for doing data science authored by the software development team at Posit. Tidyverse packages share a principled underlying philosophy, syntax and documentation. Contained within the tidyverse is its data visualization package, ggplot2. This package predates the tidyverse and is one of the most widely-used toolkits for generating data graphics. Readers of this book might already have some familiarity with R and the RStudio IDE. If not, then this section is designed to quickly acclimatise you to R and RStudio and to briefly introduce Quarto, R scripts and RStudio Projects
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