Data visualization : a practical introduction

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

• Preface p. xi
• What You Will Learn p. xii
• The Right Frame of Mind p. xiv
• How to Use This Book p. xv
• Conventions p. xvi
• Before You Begin p. xvii
• 1 Look at Data p. 1
• 1.1 Why Look at Data? p. 2
• 1.2 What Makes Bad Figures Bad? p. 5
• 1.3 Perception and Data Visualization p. 14
• 1.4 Visual Tasks and Decoding Graphs p. 23
• 1.5 Channels for Representing Data p. 26
• 1.6 Problems of Honesty and Good Judgment p. 27
• 1.7 Think Clearly about Graphs p. 29
• 1.8 Where to Go Next p. 31
• 2 Get Started p. 32
• 2.1 Work in Plain Text, Using RMarkdown p. 32
• 2.2 Use R with RStudio p. 35
• 2.3 Things to Know about R p. 38
• 2.4 Be Patient with R, and with Yourself p. 48
• 2.5 Get Data into R p. 49
• 2.6 Make Your First Figure p. 51
• 2.7 Where to Go Next p. 52
• 3 Make a Plot p. 54
• 3.1 How Ggplot Works p. 54
• 3.2 Tidy Data p. 56
• 3.3 Mappings Link Data to Things You See p. 56
• 3.4 Build Your Plots Layer by Layer p. 59
• 3.5 Mapping Aesthetics vs Setting Them p. 63
• 3.6 Aesthetics Can Be Mapped per Geom p. 66
• 3.7 Save Your Work p. 68
• 3.8 Where to Go Next p. 71
• 4 Show the Right Numbers p. 73
• 4.1 Colorless Green Data Sleeps Furiously p. 74
• 4.2 Grouped Data and the "Group" Aesthetic p. 74
• 4.3 Facet to Make Small Multiples p. 76
• 4.4 Geoms Can Transform Data p. 80
• 4.5 Frequency Plots the Slightly Awkward Way p. 82
• 4.6 Histograms and Density Plots p. 85
• 4.7 Avoid Transformations When Necessary p. 88
• 4.8 Where to Go Next p. 91
• 5 Graph Tables, Add Labels, Make Notes p. 93
• 5.1 Use Pipes to Summarize Data p. 94
• 5.2 Continuous Variables by Group or Category p. 102
• 5.3 Plot Text Directly p. 115
• 5.4 Label Outliers p. 121
• 5.5 Write and Draw in the Plot Area p. 124
• 5.6 Understanding Scales, Guides, and Themes p. 125
• 5.7 Where to Go Next p. 131
• 6 Work with Models p. 134
• 6.1 Show Several Fits at Once, with a Legend p. 135
• 6.2 Look Inside Model Objects p. 137
• 6.3 Get Model-Based Graphics Right p. 141
• 6.4 Generate Predictions to Graph p. 143
• 6.5 Tidy Model Objects with Broom p. 146
• 6.6 Grouped Analysis and List Columns p. 151
• 6.7 Plot Marginal Effects p. 157
• 6.8 Plots from Complex Surveys p. 161
• 6.9 Where to Go Next p. 168
• 7 Draw Maps p. 173
• 7.1 Map U.S. State-Level Data p. 175
• 7.2 America's Ur-choropleths p. 182
• 7.3 Statebins p. 189
• 7.4 Small-Multiple Maps p. 191
• 7.5 Is Your Data Really Spatial? p. 194
• 7.6 Where to Go Next p. 198
• 8 Refine Your Plots p. 199
• 8.1 Use Color to Your Advantage p. 201
• 8.2 Layer Color and Text Together p. 205
• 8.3 Change the Appearance of Plots with Themes p. 208
• 8.4 Use Theme Elements in a Substantive Way p. 211
• 8.5 Case Studies p. 215
• 8.6 Where to Go Next p. 230
• Acknowledgments p. 233
• Appendix p. 235
• 1 A Little More about R p. 235
• 2 Common Problems Reading in Data p. 245
• 3 Managing Projects and Files p. 253
• 4 Some Features of This Book p. 257
• References p. 261
• Index p. 267