This document provides guidance on data visualization best practices. It discusses two main reasons to visualize data: for efficient communication and to detect patterns in data. It emphasizes exploiting the human visual system through techniques like Gestalt theory and preattentive attributes. The document provides tips on choosing effective visuals, focusing on the important information, removing clutter, and making visualizations accessible to broader audiences. Throughout, it stresses simplicity, truthful representation of data, and letting data drive visual design choices over aesthetics.

1. Anne-Marie Tousch
Senior Research Scientist
@amy8492
please don't ruin it!
Data is beautiful
Mach 12th, 2019

4. 2 reasons
to visualize
data
Photo by Yuri Loginov from Pexels

5. 5 •
I keep seeing plain tables.

6. 6 •
I keep seeing plain tables.
 Do they want me to read all this?
 Did they copy-paste their slides from their paper?
 Do they care about their audience?
 Do they care about giving this talk?
 Are they hiding something?
 Do they realize a dataviz would be much more
powerful?
Most respectful interpretation?

7. 7 •
Efficient communication
A picture tells a
1000 words.
Source: Business Insider, August 2016

8. 8 •
Summary statistics

9. 9 •
never trust summary
statistics alone;
always visualize your
data
Detecting patterns
http://www.thefunctionalart.com/2016/08/download-datasaurus-never-trust-summary.html

10. What is dataviz about?

11. 11 •
Maxwell’s model of Saturn’s rings, 1858
Visualizing is always great, not only for data

12. 12 •
Visualize algorithms http://playground.tensorflow.org

13. 13 • Pictures credits : Wiki Commons / the_jetboy CC2.0 / Sandor Vamos / Wiki Commons / Acute3D / Walkerssk / Pixabay / Wiki Commons / Wiki Commons
Many ways to apprehend the world
7,000,000 visitors a year​2,500,000 rivets​
10,100 tons​

14. 14 •
Exploiting the human visual system
Ever heard of Gestalt
theory?

15. 15 •
Count the 3s below
Ready?

16. 16 •
Count the 3s below
756395068473
658663037576
860372658602
846589107830
Source: http://www.storytellingwithdata.com/book/downloads

17. 17 •
How many 3s?
Source: http://www.storytellingwithdata.com/book/downloads

18. 18 •
Count the 3s below
756395068473
658663037576
860372658602
846589107830
Source: http://www.storytellingwithdata.com/book/downloads

19. 19 •
Much easier now, uh?
Source: http://www.storytellingwithdata.com/book/downloads

20. How?
Eugene Kim CC2.0

21. 21 •
Define your goal Choose an
effective visual
Find the right
focus
Close the loop
Explore / Explain
Question?
Simple is better
Function first,
form next
Use color, size
Remove clutter
Do you answer your
question?
Do you have a
story?
Follow the process

22. 22 •
Follow best practices
Actively take control
Think accessibility
Use rules of thumbs
Be truthful

23. 23 •
When you want to focus the
attention on just a number or two
When you have a mixed
audience, for information lookup
To show the relationship between
two things
The best for continuous data over
time
Makes it very easy to compare
categories
To compare totals and also
subcomponents
Choose an effective, simple visual
Source: http://www.storytellingwithdata.com/book/downloads

24. 24 •
Pie charts are evil

25. 25 •
756395068473
658663037576
860372658602
846589107830
Remember
Source: http://www.storytellingwithdata.com/book/downloads

26. 26 •
There are many preattentive attributes
Source: http://www.storytellingwithdata.com/book/downloads

27. 27 •
But two are special
Colour is the most powerful tool you have.
Use it sparingly and resist the urge to use colour for the sake of being colourful.
Leverage colour selectively to highlight the important parts of your visual.
Size matters.
If you’re showing multiple things that are of roughly equal importance, size them similarly.
If there is one really important thing, leverage size to indicate that: make it BIG!

28. 28 •
Maximise data-ink ratio, within
reason.
Edward Tufte, The Visual Display of Quantitative Information

29. 29 •
Forgo chartjunk, including
moiré vibration, the grid, and the duck.
Edward Tufte, The Visual Display of Quantitative Information

30. 30 •
The moiré effect

31. 31 •
Don’t let your design choices be happenstance.
They should be the result of explicit decisions.

32. 32 •
Select good defaults

33. 33 •
One Python trick:

34. 34 •
Take the control

35. Take-aways
Wikimedia Commons

36. 36 •
Know your data

37. 37 •
You should care
It’s not only about nice graphics
There’s a wealth of resources
Well-grounded best practices

38. Further tips
Highlight the important stuff
Eliminate distractions
Create a visual hierarchy of
information
Make it accessible
1
2
3
4
Only highlight 10% of the overall visual. Use preattentive attributes to do so, even together
for very important stuff
When detail isn’t needed, summarize. Ask yourself if eliminating this would change
anything. If not, take it out. Push less impacting items to the background with light grey
Organize information to guide the audience. Follow a Z-pattern from top left to bottom right.
You might be an engineer, but it shouldn’t take someone with an engineering degree to
understand your graph.
Use simple language5
Choose simple language over complex, choose fewer words over more words, define any
specialized language with which your audience may not be familiar, and spell out
acronyms.
Be mindful of aestethics6
Be smart with colors. Pay attention to alignment to give a sense of unity and cohesion.
Leverage white space, and don’t add stuff just to fill space
Always prefer simple over complex

39. 39 •
The Visual Display of Quantitative Information. Edward Tufte. Graphics Press, 2d edition,
2001. The classic on beautiful, faithful displays.
Visualization Analysis and Design. Tamara Munzner. AK Peters / CRC Press, Oct 2014. A
comprehensive textbook.
Visualize this: the FlowingData guide to design, visualization, and statistics. Nathan Yau. John
Wiley & Sons, 2011. For practical examples and code.
The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting
Data, Facts, and Figures. Dona M. Wong. W. W. Norton & Company, 2013.
Storytelling with Data: A Data Visualization Guide for Business Professionals. Cole
Nussbaumer Knaflic. Wiley, 2015.
Books

40. 40 •
Tukey, John W. "The future of data analysis." The annals of mathematical statistics 33.1 (1962): 1-67. pdf
Cleveland, William S., and Robert McGill. "Graphical perception: Theory, experimentation, and application to
the development of graphical methods." Journal of the American statistical association 79.387 (1984): 531-554.
pdf
Gelman, Andrew, Cristian Pasarica, and Rahul Dodhia. "Let's practice what we preach: turning tables into
graphs." The American Statistician 56.2 (2002): 121-130. pdf
Gelman, Andrew, and Antony Unwin. "Infovis and statistical graphics: different goals, different looks." Journal of
Computational and Graphical Statistics 22.1 (2013): 2-28. pdf
Gelman, Andrew, and Thomas Basbøll. "When do stories work? Evidence and illustration in the social
sciences." Sociological Methods & Research 43.4 (2014): 547-570. pdf
Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of machine learning
research 9.Nov (2008): 2579-2605. pdf
Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. "Examples are not enough, learn to criticize! criticism for
interpretability." Advances in Neural Information Processing Systems. 2016. pdf
Wongsuphasawat, Kanit, et al. "Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow." IEEE
transactions on visualization and computer graphics 24.1 (2018): 1-12. pdf
Research papers

41. 41 •
• Flowing Data
• Storytelling With Data
• The Functional Art
• Google Brain PAIR group
• colorbrewer2.org helps select colors
Blogs & other resources
Learn from good examples
• junkcharts
• vizwiz
• fivethirtyeight
• theguardian.com/data
But also from bad ones
• viz.wtf
Practice with makeovermonday
Interested? React on paris-wimlds.slack.com

42. Questions?
Thanks @Paolo Terzi
(Criteo) from whom I took a
bunch of slides
Colocho CC BY-SA 2.5

43. 43 •
Rule of thumb: function first, form next