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Fundamentals of Data Visualization

A Primer on Making Informative and Compelling Figures

Paperback Engels 2019 9781492031086
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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


Trefwoorden:Data visualisatie
Aantal pagina's:388
Hoofdrubriek:IT-management / ICT


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Over Claus Wilke

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-University Bochum, 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.

Andere boeken door Claus Wilke


Thoughts on Graphing Software and Figure-Preparation Pipelines
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us

1. Introduction
Ugly, Bad, and Wrong Figures

I. From Data to Visualization
2. Visualizing Data: Mapping Data onto Aesthetics
Aesthetics and Types of Data
Scales Map Data Values onto Aesthetics

3. Coordinate Systems and Axes
Cartesian Coordinates
Nonlinear Axes
Coordinate Systems with Curved Axes

4. Color Scales
Color as a Tool to Distinguish
Color to Represent Data Values
Color as a Tool to Highlight

5. Directory of Visualizations
x–y relationships
Geospatial Data

6. Visualizing Amounts
Bar Plots
Grouped and Stacked Bars
Dot Plots and Heatmaps

7. Visualizing Distributions: Histograms and Density Plots
Visualizing a Single Distribution
Visualizing Multiple Distributions at the Same Time

8. Visualizing Distributions: Empirical Cumulative Distribution Functions and Q-Q Plots
Empirical Cumulative Distribution Functions
Highly Skewed Distributions
Quantile-Quantile Plots

9. Visualizing Many Distributions at Once
Visualizing Distributions Along the Vertical Axis
Visualizing Distributions Along the Horizontal Axis

10. 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

11. Visualizing Nested Proportions
Nested Proportions Gone Wrong
Mosaic Plots and Treemaps
Nested Pies
Parallel Sets

12. Visualizing Associations Among Two or More Quantitative Variables
Dimension Reduction
Paired Data

13. 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

14. Visualizing Trends
Showing Trends with a Defined Functional Form
Detrending and Time-Series Decomposition

15. Visualizing Geospatial Data
Choropleth Mapping

16. Visualizing Uncertainty
Framing Probabilities as Frequencies
Visualizing the Uncertainty of Point Estimates
Visualizing the Uncertainty of Curve Fits
Hypothetical Outcome Plots

II. Principles of Figure Design
17. The Principle of Proportional Ink
Visualizations Along Linear Axes
Visualizations Along Logarithmic Axes
Direct Area Visualizations

18. Handling Overlapping Points
Partial Transparency and Jittering
2D Histograms
Contour Lines

19. 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

20. Redundant Coding
Designing Legends with Redundant Coding
Designing Figures Without Legends

21. Multipanel Figures
Small Multiples
Compound Figures

22. Titles, Captions, and Tables
Figure Titles and Captions
Axis and Legend Titles

23. Balance the Data and the Context
Providing the Appropriate Amount of Context
Background Grids
Paired Data

24. Use Larger Axis Labels
25. Avoid Line Drawings

26. Don’t Go 3D
Avoid Gratuitous 3D
Avoid 3D Position Scales
Appropriate Use of 3D Visualizations

III. Miscellaneous Topics
27. Understanding the Most Commonly Used Image File Formats
Bitmap and Vector Graphics
Lossless and Lossy Compression of Bitmap Graphics
Converting Between Image Formats

28. Choosing the Right Visualization Software
Reproducibility and Repeatability
Data Exploration Versus Data Presentation
Separation of Content and Design

29. 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
Thinking About Data and Visualization
Programming Books
Statistics Texts
Historical Texts
Books on Broadly Related Topics
Technical Notes


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