Visualization for Social Data Science

<p><i><b>This is an important book on an important topic. I particularly like the examples showing different visualizations of the same data and the parallel presentation of graphics and code. And I absolutely love the chapter on visual storytelling. I can't wait to use this book in my classes.</b></i>- Andrew Gelman Department of Statistics and Department of Political Science Columbia University New York</p><p><b><i>A book that gives learners the inspiration knowledge and worked examples to create cutting edge visualisations of their own.</i></b><i>- </i>James Cheshire Professor of Geographic Information and Cartography University College London</p><p><b>Visualization for Social Data Science</b> 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.</p><p> Each chapter introduces statistical and graphical ideas for analysis underpinned by real social science datasets. Those ideas are then implemented via principled step-by-step workflows in the programming environment R. Key features include:</p><p>• Extensive real-world data sets and data analysis scenarios in Geography Public Health Transportation Political Science;</p><p>• Code examples fully-integrated into main text with code that builds in complexity and sophistication;</p><p>• Quarto template files for each chapter to support literate programming practices;</p><p>• Functional programming examples using tidyverse for generating empirical statistics (bootstrap resamples permutation tests) and working programmatically over model outputs;</p><p>• Unusual but important programming tricks for generating sophisticated data graphics such as network visualizations dot-density maps OD maps glyphmaps icon arrays hypothetical outcome plots and graphical line-ups plots. Every data graphic in the book is implemented via ggplot2.</p><p>• Chapters on uncertainty visualization and data storytelling that are uniquely accompanied with detailed worked examples.</p>