Wilke C.O. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures

O’Reilly, 2019. — 337 p. — ISBN 1492031089.Effective visualization is the best way to communicate information from the increasingly large and complex datasets in natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.This practical book takes you through many commonly encountered visualization problems and pitfalls and provides simple and clear guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization.Explore the basic concepts of color use as a tool to highlight, distinguish, or represent a value
Understand the importance of redundant coding to ensure that you provide key information in multiple ways
Use our directory of visualizations: a graphical guide to the most commonly used types of data visualizations
Get extensive examples of good and bad figures; learn how to use figures in a document or report
Learn methods for visualizing amounts and proportions, paired data, trends, and time series
Visualize distributions with histograms and density plots, boxplots and violin plots, and ridgeline plotsUgly, Bad, and Wrong Figures
From Data to Visualization
Visualizing Data: Mapping Data onto Aesthetics
Aesthetics and Types of Data
Scales Map Data Values onto Aesthetics
Coordinate Systems and Axes
Cartesian Coordinates
Nonlinear Axes
Coordinate Systems with Curved Axes
Color Scales
Color as a Tool to Distinguish
Color to Represent Data Values
Color as a Tool to Highlight
Directory of Visualizations
Amounts
Distributions
Proportions
x–y relationships
Geospatial Data
Uncertainty
Visualizing Amounts
Bar Plots
Grouped and Stacked Bars
Dot Plots and Heatmaps
Visualizing Distributions: Histograms and Density Plots
Visualizing a Single Distribution
Visualizing Multiple Distributions at the Same Time
Visualizing Distributions: Empirical Cumulative Distribution Functions and Q-Q Plots
Empirical Cumulative Distribution Functions
Highly Skewed Distributions
Quantile-Quantile Plots
Visualizing Many Distributions at Once
Visualizing Distributions Along the Vertical Axis
Visualizing Distributions Along the Horizontal Axis
Visualizing Proportions
A Case for Pie Charts
A Case for Side-by-Side Bars
A Case for Stacked Bars and Stacked Densities
Visualizing Proportions Separately as Parts of the Total
Visualizing Nested Proportions
Nested Proportions Gone Wrong
Mosaic Plots and Treemaps
Nested Pies
Parallel Sets
Visualizing Associations Among Two or More Quantitative Variables
Scatterplots
Correlograms
Dimension Reduction
Paired Data
Visualizing Time Series and Other Functions of an Independent Variable
Individual Time Series
Multiple Time Series and Dose–Response Curves
Time Series of Two or More Response Variables
Visualizing Trends
Smoothing
Showing Trends with a Defined Functional Form
Detrending and Time-Series Decomposition
Visualizing Geospatial Data
Projections
Layers
Choropleth Mapping
Cartograms
Visualizing Uncertainty
Framing Probabilities as Frequencies
Visualizing the Uncertainty of Point Estimates
Visualizing the Uncertainty of Curve Fits
Hypothetical Outcome Plots
Principles of Figure Design
The Principle of Proportional Ink
Visualizations Along Linear Axes
Visualizations Along Logarithmic Axes
Direct Area Visualizations
Handling Overlapping Points
Partial Transparency and Jittering
2D Histograms
Contour Lines
Common Pitfalls of Color Use
Encoding Too Much or Irrelevant Information
Using Nonmonotonic Color Scales to Encode Data Values
Not Designing for Color-Vision Deficiency
Redundant Coding
Designing Legends with Redundant Coding
Designing Figures Without Legends
Multipanel Figure
Small Multiples
Compound Figures
Titles, Captions, and Tables
Figure Titles and Captions
Axis and Legend Titles
Tables
Balance the Data and the Context
Providing the Appropriate Amount of Context
Background Grids
Paired DataUse Larger Axis Labels
Avoid Line Drawings
Don’t Go 3D
Avoid Gratuitous 3D
Avoid 3D Position Scales
Appropriate Use of 3D Visualizations
Miscellaneous Topics
Understanding the Most Commonly Used Image File Formats
Bitmap and Vector Graphics
Lossless and Lossy Compression of Bitmap Graphics
Converting Between Image Formats
Choosing the Right Visualization Software
Reproducibility and Repeatability
Data Exploration Versus Data Presentation
Separation of Content and Design
Telling a Story and Making a Point
What Is a Story?
Make a Figure for the Generals
Build Up Toward Complex Figures
Make Your Figures Memorable
Be Consistent but Don’t Be Repetitive
Annotated Bibliography
Technical Notes