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Claus O. Wilke

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Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures 1st Edition

by Claus Wilke (Author)

4.6 224 ratings

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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

ISBN-10

1492031089
ISBN-13

978-1492031086
Edition

1st
Publisher

O'Reilly Media
Publication date

May 14, 2019
Language

English
Dimensions

7 x 1 x 9 inches
Print length

387 pages
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Editorial Reviews

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-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.

Product details

Publisher ‏ : ‎ O'Reilly Media; 1st edition (May 14, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 387 pages
ISBN-10 ‏ : ‎ 1492031089
ISBN-13 ‏ : ‎ 978-1492031086
Item Weight ‏ : ‎ 1.8 pounds
Dimensions ‏ : ‎ 7 x 1 x 9 inches

Best Sellers Rank: #102,437 in Books (See Top 100 in Books)
- #19 in Business Mathematics
- #30 in Data Modeling & Design (Books)
- #49 in Data Processing

Customer Reviews:
4.6 224 ratings

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4.6 out of 5

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Chris Adami

This book makes you understand data visualization

Reviewed in the United States on May 2, 2019

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Wilke’s “Fundamentals of Data Visualization” fills an important niche: a manual for the professional data scientist who wants to convey the essence of data succinctly, accurately, and in an aesthetically pleasing way. Wilke shows that this is (perhaps surprisingly) difficult, and he does this by showing both good and bad examples of data visualization in order to make this point. The book really attempts to build up the reader’s intuition about what makes a good visualization chapter by chapter. By the end, the lessons learned have become so obvious that when going back to earlier chapters, a single glance at the figure is enough to remind you about what is good (or bad) in the example given. This is not a book that gives programming examples that tell the reader how to achieve a particular figure, and I think this is intentional. The emphasis is on understanding the principles of data visualization, not on supplying a hack for the next figure. (But for those who want to recreate a figure, Wilke provides the entire source code for the book on a web site). All in all, this is probably the best book on data visualization for the practicing scientist out there. The prose is clear and concise, the principles behind the choices he makes are clearly laid out, and the figures are clean and free of clutter. My only gripe is that the colors in the book appear to be somewhat flat and less intense than in the electronic version. Hopefully this can be remedied in the future.

15 people found this helpful

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L. Reding

Reviewed in the United States on August 7, 2019

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As a working data scientist and recovering academic, I've been making data visualizations for a decade. I've read lots of books about data visualization, but there were few I found difficult to put down; this book has been an exception.

The principles in the book are solid and agree with my experience: following the guidelines in the book will help you craft more meaningful figures that resonant more deeply with your audience and allow you the freedom to move away from the production of criminally overwrought visualizations that are so common in academia and elsewhere.

In the process, Wilke gives very approachable, high-level forays into elementary statistics, PCA, and map projections that felt like welcome and complementary diversions. I would have liked to have read a bit more about how humans perceive different visualization types, plotting characters and the like and how empirical studies of human perception can inform our design of visualizations; this was sprinkled throughout the text but a chapter on it would have been welcome. But that's a minor point; the text is clear and the entire book is incredibly well thought-out.

The code for all the figures, should you want it, is online; if you are an R user (note that the book is language-agnostic), studying it will give you power to craft and tweak your figures to a degree to make even the Wickham's of the world jealous. Highly recommended.

7 people found this helpful

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Dan Leahy

Super book

Reviewed in the United States on May 16, 2019

Verified Purchase

Full disclosure: 3 years ago I became a colleague of Prof. Wilke at the University of Texas, and we have interacted occasionally since then although we are in different departments. I recently purchased this book because of interest in the topic and my respect for Prof. Wilke and have not been disappointed. I have taken the author's advice and have not read the book cover to cover but have been dipping into chapters of interest--but then I find myself hooked and reading further on. The author's straightforward presentations and clear delineation between good, bad, and matters of opinion are helpful and spot on. I am working on two scientific papers at the moment and found advice and discussions in the book that have improved figures in both papers--and I had previously prided myself on what I thought was a thoughtful approach to making figures. I left the book with my students, and now a regular feature of our weekly journal club is a Wilke-inspired critique of how the figures could have been better made to illustrate the main point. I strongly recommend this book to anyone whose work entails creating graphical illustrations to present complex data. It is full of thoughtful analysis, good advice, and great examples and will make your presentations and publications better.

3 people found this helpful

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OP

FANTASTIC !

Reviewed in the United States on May 2, 2019

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I saw this book on a friend’s desk and had to get my own copy! I’ve read straight through a quarter of it already and it is so clear and straightforward that it makes all the ideas seem obvious. I had previously tried to absorb some works by Edward Tufte but found them overwhelming compared to this book. I’ve been an academic biologist for years and wish I’d had a book like this earlier in my career. The principles the author explains help me codify what I already intuitively sensed about various visual representations. It will be much easier now to design effective figures for my own work and to interpret at a glance the data of others! In fact I have already used a principle from these early chapters to improve a figure I’m working on for a manuscript. While extremely readable and appropriate for any layman, this book is a must for professionals representing data in any form, and in teaching graduate students how to communicate data. This book is so straight forward I would hope it could be used even in high schools where the basic important concepts would serve everybody entering this data-rich world!

5 people found this helpful

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Terri Burghart

Valuable Reference

Reviewed in the United States on November 19, 2019

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This handy, easy to read, and well-organized book is a valuable reference for anybody wanting to display their data in an attractive and meaningful way. The layout is simple, easy to follow, and the author gets right to the point, providing numerous useful examples of what figures to use in a given situation, and how the figures should be constucted. The book should be required reading for anyone beginning any career that involves data visualization. After reading it, you won't just be aware of the fact that people are constantly presenting crappy figures, you'll know exactly why they are crappy and you'll be able to avoid making similar mistakes.

One person found this helpful

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Swaroop

Excellent book but poor handling and Grayscale prin

Reviewed in India on June 10, 2022

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The media could not be loaded.

The book is awesome in terms of learnings. But there are two problems due to which I have reduced a star.
1. Amazon delivery was fast but not sure how come they damage the corners for most of the books they deliver. I think they need to work on their packaging n handling
2. The book is a grayscale print of the original edition for Indian subcontinent. Which I think should be blamed to the O’Reilly. I mean what’s the sense of selling a book on data visualisation with full chapters on Color usage without using any Color in book and mind you this is not like a very cheaply priced for India.

5 people found this helpful

Joe Bathelt

Every scientist should read this

Reviewed in the United Kingdom on July 3, 2022

Verified Purchase

There are almost as many books teaching data visualization as there are software packages for this purpose. However, these books mostly focus on the technicalities of creating visualizations. In contrast, this book discusses what makes a good figure, irrespective of how it is created. For instance, the author discusses the right choice of colour to highlight certain features of the data, how many dimensions you should include in a figure without overloading it, and which type of figure suits which purpose the best. These practical principles are very clearly explained and richly illustrated with many example visualizations. The book also comes with a companion website so the interested reader can look up the implementation of a particular figure if they wished to do so. I found the book very readable and extremely useful as a reference manual. I think that every scientist, from master student to senior researcher, should have a copy of this book.

One person found this helpful

Dr. Franco Arda

One of my top 10 Data Science books

Reviewed in Germany on March 8, 2021

Verified Purchase

According to the author, “data visualization is part art and part science.” And, in my view, the author excels at both.

What I particularly love about the book is the author's classification of visualizations: ugly, bad, and wrong figures. Pure genius!

The book is probably most useful for Data Scientists using R, Python, Power BI, and Tableau.

A few personal highlights:

- Page 29: Besides from Tableau, I’ve rarely seen so much attention to color-coding continuous and categorical data. I love it.
- Page 53: If you’re in Data Science or Machine Learning, you will appreciate the Titanic samples.
- Page 61: The importance of not relying on default bins for visualizing a single distribution.
- Page 96: Many don’t like pie charts. I love the author's example where a pie chart might have been superior to other visualizations.
- Page 112: How visualizing nested proportions (I really don’t like the “sunburst”) can be improved.
- Page 233: Common pitfalls in color use.
- Page 267: EVERY FIGURE NEEDS A TITLE. Something I still do wrong.
- Page 297: Why line drawings have been popular. Here, the author shows a strong historical understanding of visualizations. What more can you ask for?
- Page 338: Something I have to remind myself constantly: “never assume your audience can rapidly process visual displays.”
- Page 343: how to strike the balance between making a visualization memorable and clear.

The book is clearly among my top 10 Data Science books.

Franco