Fundamentals of Data Visualization

Table of contents

1. Preface
   1. Thoughts on Graphing Software and Figure-Preparation Pipelines
   2. Conventions Used in This Book
   3. Using Code Examples
   4. O’Reilly Online Learning
   5. How to Contact Us
   6. Acknowledgments
2. 1. Introduction
   1. Ugly, Bad, and Wrong Figures
3. I. From Data to Visualization
4. 2. Visualizing Data: Mapping Data onto Aesthetics
   1. Aesthetics and Types of Data
   2. Scales Map Data Values onto Aesthetics
5. 3. Coordinate Systems and Axes
   1. Cartesian Coordinates
   2. Nonlinear Axes
   3. Coordinate Systems with Curved Axes
6. 4. Color Scales
   1. Color as a Tool to Distinguish
   2. Color to Represent Data Values
   3. Color as a Tool to Highlight
7. 5. Directory of Visualizations
   1. Amounts
   2. Distributions
   3. Proportions
   4. x–y relationships
   5. Geospatial Data
   6. Uncertainty
8. 6. Visualizing Amounts
   1. Bar Plots
   2. Grouped and Stacked Bars
   3. Dot Plots and Heatmaps
9. 7. Visualizing Distributions: Histograms and Density Plots
   1. Visualizing a Single Distribution
   2. Visualizing Multiple Distributions at the Same Time
10. 8. Visualizing Distributions: Empirical Cumulative Distribution Functions and Q-Q Plots
    1. Empirical Cumulative Distribution Functions
    2. Highly Skewed Distributions
    3. Quantile-Quantile Plots
11. 9. Visualizing Many Distributions at Once
    1. Visualizing Distributions Along the Vertical Axis
    2. Visualizing Distributions Along the Horizontal Axis
12. 10. Visualizing Proportions
    1. A Case for Pie Charts
    2. A Case for Side-by-Side Bars
    3. A Case for Stacked Bars and Stacked Densities
    4. Visualizing Proportions Separately as Parts of the Total
13. 11. Visualizing Nested Proportions
    1. Nested Proportions Gone Wrong
    2. Mosaic Plots and Treemaps
    3. Nested Pies
    4. Parallel Sets
14. 12. Visualizing Associations Among Two or More Quantitative Variables
    1. Scatterplots
    2. Correlograms
    3. Dimension Reduction
    4. Paired Data
15. 13. Visualizing Time Series and Other Functions of an Independent Variable
    1. Individual Time Series
    2. Multiple Time Series and Dose–Response Curves
    3. Time Series of Two or More Response Variables
16. 14. Visualizing Trends
    1. Smoothing
    2. Showing Trends with a Defined Functional Form
    3. Detrending and Time-Series Decomposition
17. 15. Visualizing Geospatial Data
    1. Projections
    2. Layers
    3. Choropleth Mapping
    4. Cartograms
18. 16. Visualizing Uncertainty
    1. Framing Probabilities as Frequencies
    2. Visualizing the Uncertainty of Point Estimates
    3. Visualizing the Uncertainty of Curve Fits
    4. Hypothetical Outcome Plots
19. II. Principles of Figure Design
20. 17. The Principle of Proportional Ink
    1. Visualizations Along Linear Axes
    2. Visualizations Along Logarithmic Axes
    3. Direct Area Visualizations
21. 18. Handling Overlapping Points
    1. Partial Transparency and Jittering
    2. 2D Histograms
    3. Contour Lines
22. 19. Common Pitfalls of Color Use
    1. Encoding Too Much or Irrelevant Information
    2. Using Nonmonotonic Color Scales to Encode Data Values
    3. Not Designing for Color-Vision Deficiency
23. 20. Redundant Coding
    1. Designing Legends with Redundant Coding
    2. Designing Figures Without Legends
24. 21. Multipanel Figures
    1. Small Multiples
    2. Compound Figures
25. 22. Titles, Captions, and Tables
    1. Figure Titles and Captions
    2. Axis and Legend Titles
    3. Tables
26. 23. Balance the Data and the Context
    1. Providing the Appropriate Amount of Context
    2. Background Grids
    3. Paired Data
    4. Summary
27. 24. Use Larger Axis Labels
28. 25. Avoid Line Drawings
29. 26. Don’t Go 3D
    1. Avoid Gratuitous 3D
    2. Avoid 3D Position Scales
    3. Appropriate Use of 3D Visualizations
30. III. Miscellaneous Topics
31. 27. Understanding the Most Commonly Used Image File Formats
    1. Bitmap and Vector Graphics
    2. Lossless and Lossy Compression of Bitmap Graphics
    3. Converting Between Image Formats
32. 28. Choosing the Right Visualization Software
    1. Reproducibility and Repeatability
    2. Data Exploration Versus Data Presentation
    3. Separation of Content and Design
33. 29. Telling a Story and Making a Point
    1. What Is a Story?
    2. Make a Figure for the Generals
    3. Build Up Toward Complex Figures
    4. Make Your Figures Memorable
    5. Be Consistent but Don’t Be Repetitive
34. Annotated Bibliography
    1. Thinking About Data and Visualization
    2. Programming Books
    3. Statistics Texts
    4. Historical Texts
    5. Books on Broadly Related Topics
35. Technical Notes
36. References
37. Index