Fundamentals of Data Visualization

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Book description

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the 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 it provides 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 as a tool to highlight, distinguish, or represent a value
• Understand the importance of redundant coding to ensure you provide key information in multiple ways
• Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations
• Get extensive examples of good and bad figures
• Learn how to use figures in a document or report and how employ them effectively to tell a compelling story

Publisher resources

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
   1. Ugly, Bad, and Wrong Figures
   1. Aesthetics and Types of Data
   2. Scales Map Data Values onto Aesthetics
   1. Cartesian Coordinates
   2. Nonlinear Axes
   3. Coordinate Systems with Curved Axes
   1. Color as a Tool to Distinguish
   2. Color to Represent Data Values
   3. Color as a Tool to Highlight
   1. Amounts
   2. Distributions
   3. Proportions
   4. x–y relationships
   5. Geospatial Data
   6. Uncertainty
   1. Bar Plots
   2. Grouped and Stacked Bars
   3. Dot Plots and Heatmaps
   1. Visualizing a Single Distribution
   2. Visualizing Multiple Distributions at the Same Time
   1. Empirical Cumulative Distribution Functions
   2. Highly Skewed Distributions
   3. Quantile-Quantile Plots
   1. Visualizing Distributions Along the Vertical Axis
   2. Visualizing Distributions Along the Horizontal Axis
   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
   1. Nested Proportions Gone Wrong
   2. Mosaic Plots and Treemaps
   3. Nested Pies
   4. Parallel Sets
   1. Scatterplots
   2. Correlograms
   3. Dimension Reduction
   4. Paired Data
   1. Individual Time Series
   2. Multiple Time Series and Dose–Response Curves
   3. Time Series of Two or More Response Variables
   1. Smoothing
   2. Showing Trends with a Defined Functional Form
   3. Detrending and Time-Series Decomposition
   1. Projections
   2. Layers
   3. Choropleth Mapping
   4. Cartograms
   1. Framing Probabilities as Frequencies
   2. Visualizing the Uncertainty of Point Estimates
   3. Visualizing the Uncertainty of Curve Fits
   4. Hypothetical Outcome Plots
   1. Visualizations Along Linear Axes
   2. Visualizations Along Logarithmic Axes
   3. Direct Area Visualizations
   1. Partial Transparency and Jittering
   2. 2D Histograms
   3. Contour Lines
   1. Encoding Too Much or Irrelevant Information
   2. Using Nonmonotonic Color Scales to Encode Data Values
   3. Not Designing for Color-Vision Deficiency
   1. Designing Legends with Redundant Coding
   2. Designing Figures Without Legends
   1. Small Multiples
   2. Compound Figures
   1. Figure Titles and Captions
   2. Axis and Legend Titles
   3. Tables
   1. Providing the Appropriate Amount of Context
   2. Background Grids
   3. Paired Data
   4. Summary
   1. Avoid Gratuitous 3D
   2. Avoid 3D Position Scales
   3. Appropriate Use of 3D Visualizations
   1. Bitmap and Vector Graphics
   2. Lossless and Lossy Compression of Bitmap Graphics
   3. Converting Between Image Formats
   1. Reproducibility and Repeatability
   2. Data Exploration Versus Data Presentation
   3. Separation of Content and Design
   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
   1. Thinking About Data and Visualization
   2. Programming Books
   3. Statistics Texts
   4. Historical Texts
   5. Books on Broadly Related Topics
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   Product information

   • Title: Fundamentals of Data Visualization
   • Author(s): Claus O. Wilke
   • Release date: April 2019
   • Publisher(s): O'Reilly Media, Inc.
   • ISBN: 9781492031086

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