Princeton.University.Press.Data.Visualization.A.Practical.Introd....pdf

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Cover

Title

Dedication

Contents

Preface

What You Will Learn

The Right Frame of Mind

How to Use This Book

Conventions

Before You Begin

1 Look at Data

1.1 Why Look at Data?

1.2 What Makes Bad Figures Bad?

1.3 Perception and Data Visualization

1.4 Visual Tasks and Decoding Graphs

1.5 Channels for Representing Data

1.6 Problems of Honesty and Good Judgment

1.7 Think Clearly about Graphs

1.8 Where to Go Next

2 Get Started

2.1 Work in Plain Text, Using RMarkdown

2.2 Use R with RStudio

2.3 Things to Know about R

2.4 Be Patient with R, and with Yourself

2.5 Get Data into R

2.6 Make Your First Figure

2.7 Where to Go Next

3 Make a Plot

3.1 How Ggplot Works

3.2 Tidy Data

3.3 Mappings Link Data to Things You See

3.4 Build Your Plots Layer by Layer

3.5 Mapping Aesthetics vs Setting Them

3.6 Aesthetics Can Be Mapped per Geom

3.7 Save Your Work

3.8 Where to Go Next

4 Show the Right Numbers

4.1 Colorless Green Data Sleeps Furiously

4.2 Grouped Data and the “Group” Aesthetic

4.3 Facet to Make Small Multiples

4.4 Geoms Can Transform Data

4.5 Frequency Plots the Slightly Awkward Way

4.6 Histograms and Density Plots

4.7 Avoid Transformations When Necessary

4.8 Where to Go Next

5 Graph Tables, Add Labels, Make Notes

5.1 Use Pipes to Summarize Data

5.2 Continuous Variables by Group or Category

5.3 Plot Text Directly

5.4 Label Outliers

5.5 Write and Draw in the Plot Area

5.6 Understanding Scales, Guides, and Themes

5.7 Where to Go Next

6 Work with Models

6.1 Show Several Fits at Once, with a Legend

6.2 Look Inside Model Objects

6.3 Get Model-Based Graphics Right

6.4 Generate Predictions to Graph

6.5 Tidy Model Objects with Broom

6.6 Grouped Analysis and List Columns

6.7 Plot Marginal Effects

6.8 Plots from Complex Surveys

6.9 Where to Go Next

7 Draw Maps

7.1 Map U.S. State-Level Data

7.2 America’s Ur-choropleths

7.3 Statebins

7.4 Small-Multiple Maps

7.5 Is Your Data Really Spatial?

7.6 Where to Go Next

8 Refine Your Plots

8.1 Use Color to Your Advantage

8.2 Layer Color and Text Together

8.3 Change the Appearance of Plots with Themes

8.4 Use Theme Elements in a Substantive Way

8.5 Case Studies

8.6 Where to Go Next

Acknowledgments

Appendix

1 A Little More about R

2 Common Problems Reading in Data

3 Managing Projects and Files

4 Some Features of This Book

References

Index

Data Visualization

Data Visualization A PRACTICAL INTRODUCTION Kieran Healy p r i n c e t o n u n i v e r s i t y p r e s s p r i n c e t o n a n d o x f o r d

© 2019 Princeton University Press Published by Princeton University Press 41 William Street, Princeton, New Jersey 08540 6 Oxford Street, Woodstock, Oxfordshire OX20 1TR press.princeton.edu All Rights Reserved Library of Congress Control Number: 2018935810 ISBN 978-0-691-18161-5 ISBN (pbk.) 978-0-691-18162-2 British Library Cataloging-in-Publication Data is available This book has been composed with open-source tools in Minion Pro, Myriad Pro, and Iosevka Type. Printed on acid-free paper. ∞ Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

For the Llamanteriat, who saw it first.

Contents xi Preface What You Will Learn xii The Right Frame of Mind xiv How to Use This Book Conventions Before You Begin xvii xvi xv 1 2 2 Look at Data 1 1.1 Why Look at Data? 1.2 What Makes Bad Figures Bad? 1.3 Perception and Data Visualization 14 1.4 Visual Tasks and Decoding Graphs 23 1.5 Channels for Representing Data 1.6 Problems of Honesty and Good Judgment 1.7 Think Clearly about Graphs 1.8 Where to Go Next 29 31 5 26 27 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 2.5 Get Data into R 49 48

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