Data Visualization: A Practical Introduction

by Kieran Healy

Overview

An accessible primer on how to create effective graphics from data

This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.

Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.

Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.

• Provides hands-on instruction using R and ggplot2
  
• Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent
  
• Includes a library of data sets, code, and functions
  

Product Details

ISBN-13:	9780691181622
Publisher:	Princeton University Press
Publication date:	12/18/2018
Pages:	296
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

Kieran Healy is associate professor of sociology at Duke University. He is the author of Last Best Gifts: Altruism and the Market for Human Blood and Organs.

Table of Contents

Preface xi

What You Will Learn xii

The Right Frame of Mind xiv

How to Use This Book xv

Conventions xvi

Before You Begin xvii

1 Look at Data 1

1.1 Why Look at Data? 2

1.2 What Makes Bad Figures Bad? 5

1.3 Perception and Data Visualization 14

1.4 Visual Tasks and Decoding Graphs 23

1.5 Channels for Representing Data 26

1.6 Problems of Honesty and Good Judgment 27

1.7 Think Clearly about Graphs 29

1.8 Where to Go Next 31

2 Get Started 32

2.1 Work in Plain Text, Using RMarkdown 32

2.2 Use R with RStudio 35

2.3 Things to Know about R 38

2.4 Be Patient with R, and with Yourself 48

2.5 Get Data into R 49

2.6 Make Your First Figure 51

2.7 Where to Go Next 52

3 Make a Plot 54

3.1 How Ggplot Works 54

3.2 Tidy Data 56

3.3 Mappings Link Data to Things You See 56

3.4 Build Your Plots Layer by Layer 59

3.5 Mapping Aesthetics vs Setting Them 63

3.6 Aesthetics Can Be Mapped per Geom 66

3.7 Save Your Work 68

3.8 Where to Go Next 71

4 Show the Right Numbers 73

4.1 Colorless Green Data Sleeps Furiously 74

4.2 Grouped Data and the "Group" Aesthetic 74

4.3 Facet to Make Small Multiples 76

4.4 Geoms Can Transform Data 80

4.5 Frequency Plots the Slightly Awkward Way 82

4.6 Histograms and Density Plots 85

4.7 Avoid Transformations When Necessary 88

4.8 Where to Go Next 91

5 Graph Tables, Add Labels, Make Notes 93

5.1 Use Pipes to Summarize Data 94

5.2 Continuous Variables by Group or Category 102

5.3 Plot Text Directly 115

5.4 Label Outliers 121

5.5 Write and Draw in the Plot Area 124

5.6 Understanding Scales, Guides, and Themes 125

5.7 Where to Go Next 131

6 Work with Models 134

6.1 Show Several Fits at Once, with a Legend 135

6.2 Look Inside Model Objects 137

6.3 Get Model-Based Graphics Right 141

6.4 Generate Predictions to Graph 143

6.5 Tidy Model Objects with Broom 146

6.6 Grouped Analysis and List Columns 151

6.7 Plot Marginal Effects 157

6.8 Plots from Complex Surveys 161

6.9 Where to Go Next 168

7 Draw Maps 173

7.1 Map U.S. State-Level Data 175

7.2 America's Ur-choropleths 182

7.3 Statebins 189

7.4 Small-Multiple Maps 191

7.5 Is Your Data Really Spatial? 194

7.6 Where to Go Next 198

8 Refine Your Plots 199

8.1 Use Color to Your Advantage 201

8.2 Layer Color and Text Together 205

8.3 Change the Appearance of Plots with Themes 208

8.4 Use Theme Elements in a Substantive Way 211

8.5 Case Studies 215

8.6 Where to Go Next 230

Acknowledgments 233

Appendix 235

1 A Little More about R 235

2 Common Problems Reading in Data 245

3 Managing Projects and Files 253

4 Some Features of This Book 257

References 261

Index 267

What People are Saying About This

From the Publisher

“Finally! A data visualization guide that is simultaneously practical and elegant. Healy combines the beauty and insight of Tufte with the concrete helpfulness of Stack Exchange. Data Visualization is brimming with insights into how quantitative analysts can use visualization as a tool for understanding and communication. A must-read for anyone who works with data.”—Elizabeth Bruch, University of Michigan

“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, author of Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do

“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. The book is broadly relevant, beautifully rendered, and engagingly written. 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. It is packed full of clear-headed and sage insights.”—Becky Pettit, University of Texas at Austin

“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. There is no other book quite like this.”—Thomas J. Leeper, London School of Economics

“Kieran Healy has written a wonderful book that fills an important niche in an increasingly crowded landscape of materials about software in R. Data Visualization is clear, beautifully formatted, and full of careful insights.”—Brandon Stewart, Princeton University

“Healy’s prose is clear and direct. I came away from this book with a much better understanding of both visualizations and R.”—Neal Caren, University of North Carolina, Chapel Hill

“Innovative and extraordinarily well-written.”—Jeremy Freese, Stanford University

Editorial Reviews

"Undoubtedly, this book is an excellent introduction to an essential tool for anyone who needs to collect and present data."

Conservation Biology

"[Healy’s] prose is engaging and chatty, and the style of instruction is unpretentious and practical . . . This single volume represents an excellent entry point for those wishing to upskill their abilities in data visualization."—-Paul Cuffe, IEEE Transactions

From the Publisher