Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures

Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures

by Claus Wilke
Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures

Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures

by Claus Wilke


    Qualifies for Free Shipping
    Choose Expedited Shipping at checkout for delivery by Thursday, December 7
    Check Availability at Nearby Stores

Related collections and offers


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

Product Details

ISBN-13: 9781492031086
Publisher: O'Reilly Media, Incorporated
Publication date: 04/15/2019
Pages: 387
Sales rank: 687,745
Product dimensions: 6.90(w) x 9.10(h) x 0.80(d)

About the Author

Claus O. Wilke is a professor of Integrative Biology at The University of Texas at Austin. He holds a PhD in theoretical physics from the Ruhr-UniversityBochum, Germany. Claus is the author or coauthor of over 170 scientific publications, covering topics in computational biology, mathematical modeling, bioinformatics, evolutionary biology, protein biochemistry, virology, and statistics. He has also authored several popular R packages used for data visualization, such as cowplot and ggridges, and he is a contributor to the package ggplot2.

Table of Contents

Preface xi

1 Introduction 1

Ugly, Bad, and Wrong Figures 2

Part I From Data to Visualization

2 Visualizing Data: Mapping Data onto Aesthetics 7

Aesthetics and Types of Data 7

Scales Map Data Values onto Aesthetics 10

3 Coordinate Systems and Axes 13

Cartesian Coordinates 13

Nonlinear Axes 16

Coordinate Systems with Curved Axes 22

4 Color Scales 27

Color as a Tool to Distinguish 27

Color to Represent Data Values 29

Color as a Tool to Highlight 33

5 Directory of Visualizations 37

Amounts 37

Distributions 38

Proportions 39

x-y relationships 41

Geospatial Data 42

Uncertainty 43

6 Visualizing Amounts 45

Bar Plots 45

Grouped and Stacked Bars 50

Dot Plots and Heatmaps 53

7 Visualizing Distributions: Histograms and Density Plots 59

Visualizing a Single Distribution 59

Visualizing Multiple Distributions at the Same Time 64

8 Visualizing Distributions: Empirical Cumulative Distribution Functions and Q-Q Plots 71

Empirical Cumulative Distribution Functions 71

Highly Skewed Distributions 74

Quantile-Quantile Plots 78

9 Visualizing Many Distributions at Once 81

Visualizing Distributions Along the Vertical Axis 81

Visualizing Distributions Along the Horizontal Axis 88

10 Visualizing Proportions 93

A Case for Pie Charts 93

A Case for Side-by-Side Bars 97

A Case for Stacked Bars and Stacked Densities 99

Visualizing Proportions Separately as Parts of the Total 101

11 Visualizing Nested Proportions 105

Nested Proportions Gone Wrong 105

Mosaic Plots and Treemaps 107

Nested Pies 111

Parallel Sets 113

12 Visualizing Associations Among Two or More Quantitative Variables 117

Scatterplots 117

Correlograms 121

Dimension Reduction 124

Paired Data 127

13 Visualizing Time Series and Other Functions of an Independent Variable 131

Individual Time Series 131

Multiple Time Series and Dose-Response Curves 135

Time Series of Two or More Response Variables 138

14 Visualizing Trends 145

Smoothing 145

Showing Trends with a Defined Functional Form 151

Detrending and Time-Series Decomposition 155

15 Visualizing Geospatial Data 161

Projections 161

Layers 169

Choropleth Mapping 172

Cartograms 176

16 Visualizing Uncertainty 181

Framing Probabilities as Frequencies 181

Visualizing the Uncertainty of Point Estimates 186

Visualizing the Uncertainty of Curve Fits 197

Hypothetical Outcome Plots 201

Part II Principles of Figure Design

17 The Principle of Proportional Ink 207

Visualizations Along Linear Axes 208

Visualizations Along Logarithmic Axes 212

Direct Area Visualizations 215

18 Handling Overlapping Points 219

Partial Transparency and Jittering 219

2D Histograms 222

Contour Lines 225

19 Common Pitfalls of Color Use 233

Encoding Too Much or Irrelevant Information 233

Using Nonmonotonic Color Scales to Encode Data Values 237

Not Designing for Color-Vision Deficiency 238

20 Redundant Coding 243

Designing Legends with Redundant Coding 243

Designing Figures Without Legends 250

21 Multipanel Figures 255

Small Multiples 255

Compound Figures 260

22 Titles, Captions, and Tables 267

Figure Titles and Captions 267

Axis and Legend Titles 270

Tables 273

23 Balance the Data and the Context 277

Providing the Appropriate Amount of Context 277

Background Grids 282

Paired Data 287

Summary 290

24 Use Larger Axis Labels 291

25 Avoid Line Drawings 297

26 Don't Go 3D 305

Avoid Gratuitous 3D 305

Avoid 3D Position Scales 307

Appropriate Use of 3D Visualizations 313

Part III Miscellaneous Topics

27 Understanding the Most Commonly Used Image File Formats 319

Bitmap and Vector Graphics 319

Lossless and Lossy Compression of Bitmap Graphics 321

Converting Between Image Formats 324

28 Choosing the Right Visualization Software 325

Reproducibility and Repeatability 326

Data Exploration Versus Data Presentation 327

Separation of Content and Design 330

29 Telling a Story and Making a Point 333

What Is a Story? 334

Make a Figure for the Generals 337

Build Up Toward Complex Figures 341

Make Your Figures Memorable 343

Be Consistent but Don't Be Repetitive 345

Annotated Bibliography 351

Technical Notes 355

References 357

Index 361

From the B&N Reads Blog

Customer Reviews