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.
Author(s): Kieran Healy
Publisher: Princeton University Press
Year: 2019
Language: English
Pages: 293
Tags: Data Mining, Databases, Social Sciences Methodology
Cover......Page 1
Title......Page 4
Copyright......Page 5
Dedication......Page 6
Contents......Page 8
Preface......Page 12
What You Will Learn......Page 13
The Right Frame of Mind......Page 15
How to Use This Book......Page 16
Conventions......Page 17
Before You Begin......Page 18
1 Look at Data......Page 22
1.1 Why Look at Data?......Page 23
1.2 What Makes Bad Figures Bad?......Page 26
1.3 Perception and Data Visualization......Page 35
1.4 Visual Tasks and Decoding Graphs......Page 44
1.5 Channels for Representing Data......Page 47
1.6 Problems of Honesty and Good Judgment......Page 48
1.7 Think Clearly about Graphs......Page 50
1.8 Where to Go Next......Page 52
2.1 Work in Plain Text, Using RMarkdown......Page 53
2.2 Use R with RStudio......Page 56
2.3 Things to Know about R......Page 59
2.4 Be Patient with R, and with Yourself......Page 69
2.5 Get Data into R......Page 70
2.6 Make Your First Figure......Page 72
2.7 Where to Go Next......Page 73
3.1 How Ggplot Works......Page 75
3.3 Mappings Link Data to Things You See......Page 77
3.4 Build Your Plots Layer by Layer......Page 80
3.5 Mapping Aesthetics vs Setting Them......Page 84
3.6 Aesthetics Can Be Mapped per Geom......Page 87
3.7 Save Your Work......Page 89
3.8 Where to Go Next......Page 92
4 Show the Right Numbers......Page 94
4.2 Grouped Data and the “Group” Aesthetic......Page 95
4.3 Facet to Make Small Multiples......Page 97
4.4 Geoms Can Transform Data......Page 101
4.5 Frequency Plots the Slightly Awkward Way......Page 103
4.6 Histograms and Density Plots......Page 106
4.7 Avoid Transformations When Necessary......Page 109
4.8 Where to Go Next......Page 112
5 Graph Tables, Add Labels, Make Notes......Page 114
5.1 Use Pipes to Summarize Data......Page 115
5.2 Continuous Variables by Group or Category......Page 123
5.3 Plot Text Directly......Page 136
5.4 Label Outliers......Page 142
5.5 Write and Draw in the Plot Area......Page 145
5.6 Understanding Scales, Guides, and Themes......Page 146
5.7 Where to Go Next......Page 152
6 Work with Models......Page 155
6.1 Show Several Fits at Once, with a Legend......Page 156
6.2 Look Inside Model Objects......Page 158
6.3 Get Model-Based Graphics Right......Page 162
6.4 Generate Predictions to Graph......Page 164
6.5 Tidy Model Objects with Broom......Page 167
6.6 Grouped Analysis and List Columns......Page 172
6.7 Plot Marginal Effects......Page 178
6.8 Plots from Complex Surveys......Page 182
6.9 Where to Go Next......Page 189
7 Draw Maps......Page 194
7.1 Map U.S. State-Level Data......Page 196
7.2 America’s Ur-choropleths......Page 203
7.3 Statebins......Page 210
7.4 Small-Multiple Maps......Page 212
7.5 Is Your Data Really Spatial?......Page 215
7.6 Where to Go Next......Page 219
8 Refine Your Plots......Page 220
8.1 Use Color to Your Advantage......Page 222
8.2 Layer Color and Text Together......Page 226
8.3 Change the Appearance of Plots with Themes......Page 229
8.4 Use Theme Elements in a Substantive Way......Page 232
8.5 Case Studies......Page 236
8.6 Where to Go Next......Page 251
Acknowledgments......Page 254
1 A Little More about R......Page 256
2 Common Problems Reading in Data......Page 266
3 Managing Projects and Files......Page 274
4 Some Features of This Book......Page 278
References......Page 282
Index......Page 288
