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Data Visualization is clear, beautifully formatted, and full of careful insights.” - Brandon Stewart, Princeton University “Kieran Healy has written a wonderful book that fills an important niche in an increasingly crowded landscape of materials about software in R. There is no other book quite like this.” - Thomas J. “Healy provides a unique introduction to the process of visualizing quantitative data, offering a remarkably coherent treatment that will appeal to novices and advanced analysts alike.
It is packed full of clear-headed and sage insights.” Becky Pettit, University of Texas at Austin It is easily accessible for students at any level and will be an incredible teaching resource for courses on research methods, statistics, and data visualization. The book is broadly relevant, beautifully rendered, and engagingly written.
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“Data Visualization is a brilliant book that not only teaches the reader how to visualize data but also carefully considers why data visualization is essential for good social science. “Healy’s fun and readable book is unusual in covering the ‘why do’ as well as the ‘how to’ of data visualization, demonstrating how dataviz is a key step in all stages of social science-from theory construction to measurement to modeling and interpretation of analyses-and giving readers the tools to integrate visualization into their own work.” - Andrew Gelman, Columbia University A must-read for anyone who works with data.” - Elizabeth Bruch, University of Michigan Data Visualization is brimming with insights into how quantitative analysts can use visualization as a tool for understanding and communication. Healy combines the beauty and insight of Tufte with the concrete helpfulness of Stack Exchange. “Finally! A data visualization guide that is simultaneously practical and elegant. 8.4 Use theme elements in a substantive way.8.3 Change the appearance of plots with themes.6.1 Show several fits at once, with a legend.5.6 Understanding scales, guides, and themes.5.2 Continuous variables by group or category.4.7 Avoid transformations when necessary.4.5 Frequency plots the slightly awkward way.4.2 Grouped data and the “group” aesthetic.4.1 Colorless green data sleeps furiously.3.3 Mappings link data to things you see.2.4 Be patient with R, and with yourself.2.1 Work in plain text, using RMarkdown.1.6 Problems of honesty and good judgment.
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