Fundamentals of Data Visualization

Recommendation numbered, Nº: 06042020p1

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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 plots. (Source:


1 Introduction
Part I: From data to visualization
2 Visualizing data: Mapping data onto aesthetics
3 Coordinate systems and axes
4 Color scales
5 Directory of visualizations
6 Visualizing amounts
7 Visualizing distributions: Histograms and density plots
8 Visualizing distributions: Empirical cumulative distribution functions and q-q plots
9 Visualizing many distributions at once
10 Visualizing proportions
11 Visualizing nested proportions
12 Visualizing associations among two or more quantitative variables
13 Visualizing time series and other functions of an independent variable
14 Visualizing trends
15 Visualizing geospatial data
16 Visualizing uncertainty
Part II: Principles of figure design
17 The principle of proportional ink
18 Handling overlapping points
19 Common pitfalls of color use
20 Redundant coding
21 Multi-panel figures
22 Titles, captions, and tables
23 Balance the data and the context
24 Use larger axis labels
25 Avoid line drawings
26 Don’t go 3D
Part III: Miscellaneous topics
27 Understanding the most commonly used image file formats
28 Choosing the right visualization software
29 Telling a story and making a point
30 Annotated bibliography
Technical notes


[Unofficial biography. For informational purposes only]

Claus O Wilke

Claus O. Wilke holds a PhD in theoretical physics from the Ruhr-University Bochum, Germany, and he is currently the Jane and Roland Blumberg Centennial Professor in Molecular Evolution at The University of Texas at Austin. Wilke has published nearly 200 scientific publications covering topics in computational biology, molecular evolution, protein biochemistry, and virology. 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. In 2019, Wilke published the book Fundamentals of Data Visualization, which provides a concise introduction to effectively visualizing many different types of data sets. (Source:

Please, thank the author

Thank you very much for this work to @ClausWilke, via @States_AI_IA #datascience #rstudio #openscience #openaccess #ai #artificialintelligence #ia #thebibleai #ebook #free

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