Visualization for Data Science with R
About This Book
Proposal
Why read this book
Structure of the book
Software information and conventions
About the Author
Introduction
Outline
1
Overview of common visualizations and how to read them
1.1
Visualization components
1.2
Bar Chart
1.2.1
Variations
1.2.2
Add a variable: Bar charts with color
1.3
Scatter Plot
1.3.1
Variations
1.3.2
Add a variable: Scatter plot with color
1.3.3
Add a variable: Bubble chart
1.4
Line Chart
1.4.1
Using circles to highlight the position of the data points
1.4.2
Using colors to distinguish individual lines
1.4.3
Variations
1.4.4
Add a variable: Line chart with color
1.5
Pie Chart
1.5.1
Variations
1.5.2
Add a variable: Small multiples
1.6
Heat Map
1.6.1
Variations
1.6.2
Add a variable: Bubble matrix
1.7
Histogram
1.7.1
Variations
1.7.2
Add a variable: Separate groups on x axis
1.7.3
Add a variable: Separate groups with color
1.8
Maps
1.8.1
Choropleth
1.8.2
Proportional Symbol Map
2
Building basic visualizations with ggplot2
2.1
Basic ggplot2 syntax
2.1.1
Simple Plot Template
2.2
Example: Vineyards in the Finger Lakes Region of New York State
2.2.1
Step 1: Add Main Plot Function
2.2.2
Step 2: Set the Data
2.2.3
Step 3: Choose a shape layer
2.2.4
Step 4: Map Variables to Aesthetics
2.2.5
Step 5: Adjust the Defaults
2.3
Inheritance
2.4
How to Debug Code
3
Working with textual data in ggplot2
3.1
What is a text variable?
3.2
Visualizing text variables
3.2.1
Common mappings for text variables
3.2.2
Ordering
3.2.3
Long Text Labels
3.2.4
Picking Colors
3.2.5
Small Multiples
4
Customizing the design of ggplot2 visualizations
4.1
Common Data Modifications
4.2
Labels
4.3
Scales
4.4
Themes
5
Avoiding unethical design practices
5.1
Due Diligence for Analysis
5.2
Promote Dignity and Agency
5.3
Reduce Distortion
5.3.1
Color
5.3.2
Shapes
5.3.3
Truncated Axes
5.4
Compare Like Things
5.4.1
Dual Axis
5.4.2
Normalizing Raw Data
5.5
Be True to the Data
5.5.1
Don’t Cherry-Pick Data
5.5.2
Matching Data With Chart Type
5.5.3
Beware of Conflicts of Interest
5.5.4
Check if subsets exhibit different patterns (Simpson’s Paradox)
5.5.5
Careful Binning
5.6
Proper Citation and Documentation
5.6.1
Transparent Practices
6
Building ggplot2 visualizations into print publications
6.1
Exporting ggplot2 Visualizations
6.2
Using RMarkdown to create PDF reports
6.3
Using R Packages to Build Word and PowerPoint Files
7
Basic accessibility for static visualizations
7.1
Accessible Text Accompanying the Visualization
7.2
Neurodivergence
7.3
Low Vision
7.4
Color Vision Deficiency
7.4.1
Dual encoding (never just color)
7.4.2
Color palettes
7.5
Screen Reader Users
7.5.1
Alternative Text
7.5.2
Longer Descriptions
7.5.3
Converting graphics to sound, touch, text, table
7.6
Accessibility Resources
8
Exploring interactivity in visualizations with plotly and crosstalk
8.1
Interactivity for Exploration
8.2
Interactive Components
8.2.1
About Websites and HTML
8.2.2
Simple Markdown Websites
8.3
HTML Widgets
8.3.1
Plotly
8.3.2
DataTables
8.4
CrossTalk
9
Using RMarkdown to build websites for projects
9.1
Static Websites
9.2
Hosting
9.3
Associating a Website with an R Project
10
Using RMarkdown to build dashboards for projects
10.1
What is a dashboard?
10.2
Using flexdashboards to arrange components
10.3
Blending Crosstalk with Flexdashboards
10.4
Basic Dashboard Design
11
Basic usability for interactive visualizations
11.1
What do users want to do with a dashboard
11.1.1
Conducting an Initial User Study
11.2
How to Test a Dashboard
11.2.1
Preliminary Questions
11.2.2
5-second test for first impressions
11.2.3
Think-aloud Scenarios
11.2.4
Closing Questions
12
Teacher’s guide
12.1
Live Coding
12.2
Sample Data
Appendix
A
Datasets
Duke Enrollment
Bar Chart
Coral Resilience Data
Git Experience
Inclusiveness Index
Candidate Demographics
Affinity Spending
Vineyards
References
Published with bookdown
Visualization for Data Science with R
Chapter 4
Customizing the design of ggplot2 visualizations
4.1
Common Data Modifications
4.2
Labels
4.3
Scales
4.4
Themes