Best FiveThirtyEight Graphs of 2016

FiveThirtyEight recently released their favorite graphs from the past year (2016). While many of the graphs are in standard formats (scatter plots, for instance), the team at FiveThirtyEight is particularly good at using other design elements to make the graphs visually appealing and easily understood. For example, in graph number 10 "What Hoarding MVPs Looks Like", notice the good use of colors and labels.

Enjoy!

Quickly Adding Depth to Flat Figures

Last week I attended a talk by Christopher Love, a faculty member at MIT. I noticed a simple visual effect he used several times in his presentation to give depth and dimension to what would otherwise be a flat figure.

On one slide, he illustrated a crowd of people as a way to talk about the effects of a treatment. The figure could have looked something like this, if he'd left it flat.

Now, if had been me, I'd probably have just left it like this. But not Dr. Love. He went the extra mile and added depth by changing the size and color. He made figures in the foreground bigger and darker, while figures in the background were smaller and lighter.

It takes 30 seconds to add these extra details, and the result is more pleasing and interesting. Now you have this same trick in your toolbox.

Minimizing Cognitive Strain

William Cleveland wrote the classic "The Elements of Graphing Data" which has been informing data visualization efforts for over 20 years now. He is a big proponent of exploratory data visualization. But perhaps the most essential point he makes is that:

Visualizations should maximize information content and minimize cognitive strain.

Remember that your audience is busy. They probably don't have time to laboriously interpret your work. Your audience also is probably not as familiar with your data as you are, nor can they read your mind. To be effective, there needs to be enough information to tell a story and the story has to be obvious. Make it easy. Spoon feed your audience, not because they're dumb, but because they only have a few moments to spare before moving on. This is your chance to educate, inform, perhaps even surprise and captivate. That will only happen if the story from the data is glaringly obvious.

A simple example from the Win-Vector blog (a great resource for data visualization and data science):

 
These two plots contain the same information (number of households per state), but in the first case, the states are sorted alphabetically, and in the second case, by the number of households. The simple act of sorting made it easier for anyone to recognize that Wyoming has the smallest number of households and that California has the most. It's also easier to get a feel for the distribution this way.

Of course, the more data we layer on, the harder it is to interpret. Always remember William Cleveland: Maximize Information but Minimize Cognitive Strain.

Graphical Perception

Not all quantitative visualizations are created equal. Graphical perception refers to the "visual decoding of information encoded on graphs". Human beings are good at decoding some types of "visual encodings", but not others. For example, we are much better at understanding the relative differences between data points on a linear scale, but not so good at gauging angles or areas. Presenting your information in "easily decoded" formats will help your audience digest the material more accurately and more easily.

In 1984, researchers William S. Cleveland and Robert McGill published a paper quantifying the ability of people to accurately decode different types of visual information. The results of this paper are summarized in this blog post at FlowingData.com.

Color me happy!

Choosing the right colors is one of the more subtle (as opposed to font sizes or plot type) yet important skills that sets apart the novices from the experienced data visualizers. Samantha Zhang wrote this great post for Graphiq that breaks down the choice of color palettes into a few simple principles. Her post is a quick read which will be well-worth your time.

Here's a quick summary of Samantha's rules: 

1. Choose a color palette with the maximum possible variation in color and hue
2. Follow natural color patterns
3. Use a gradient instead of a fixed selection of colors

The Thought Process Behind Data-Rich Figures

As a new graduate student, or really anyone who is just getting their feet wet organizing their own data into beautiful figures, it can be helpful to see how other people approach the task (which is the whole premise behind Eats, Graphs, and Leaves). I stumbled across this blog by Will Stahl-Timmins which is a fantastic glimpse into the mind of a professional data visualizer. Will works for the BMJ and as a freelance "data graphic designer". Each post is a small vignette about a particular project he worked on, from how he drafted and brainstormed all the way to the software he used to finish. 

Most Unusual FiveThirtyEight Graphs from 2015

I'm appreciative that FiveThirtyEight (the blogging outlet of the stats celebrity Nate Silver) compiled their 47 most unusual graphs from 2015. It's not clear how they defined "unusual"--it seems to be based on the topic and/or design. Some graphics used standard designs (e.g. line graphs) to visualize an unusual and interesting dataset (see #18 "The U.S. Has Won the Women's World Cup"). Other graphics implement new and creative visualization strategies (see #35 "Where Do Your State's Taxes Come From?"). 

For example, I love the hexagonal heat maps they use to display basketball stats. 

Browse through and get your daily dose of visualization inspiration.

World Statistics Day 2015: Data Visualization Competition Results

If you're in a slump and need some visualization inspiration, look no further. John Wiley & Sons Ltd. organized a data visualization competition and the winning entries are posted online for all to enjoy (and learn from). 

Denise Peaslee won first place with a Tableau-based visualization of worldwide refugee data.

In second place, Deborah Brown and Iain Bryson display recent, optimistic data on the Millennium Development Goals 2015 in video format.

Many other excellent visualizations from runners-up are also worth perusing. For example, Edward Brown's entry on world religion and social indicators is a great example of interactive visualizations.

Now that you've been inspired, get back out there and visualize some data!

How to Design Effective Figures

Marco Rolandi, Karen Cheng, and Sarah Pérez-Kriz—all three professors at the University of Washington, Seattle—wrote a great essay titled "How to Design Effective Figures". Here are the main points:

1. Design figures for your audience (Not for you)
2. Focus on the most important information
3. Design a clear visual structure
4. Use visual contrast, but keep figures simple
5. Create legible and readable typography

The essay contains clear descriptions and exemplary figures for each point they make, and includes references to further resources.

Crash Course: Principles of Analytical Design

It is common among fledgling scientists to learn to design figures "on the job", and perhaps this is the best way to get started. However, some very smart people have put intense thought into what makes a good figure, and have distilled those principles for our learning and benefit. Perhaps the foremost among these "figure design gurus" is Edward Tufte, who you may know better as the author of "The Visual Display of Quantitative Information".

In this post we outline Tufte's six Fundamental Principles of Analytical Design from the fifth chapter of his book "Beautiful Evidence". Following these principles will help you to elegantly convey your information.

The map above was created by E.J. Marey in 1869 to summarize Napoleon's Russian campaign of 1812-1813. The base layer is a map beginning Niemen River and ending in Moscow. The brown lines show the army on the Journey into Russia, and the black show the return journey. The width of these lines indicate the size of the army. A scale bar indicates distances, and the temperature and dates are indicated for the return journey. Tufte uses this figure to illustrate each of his principles.

Principle 1: 

Show comparisons, contrasts, differences

At the Niemen river, where the campaign begins, the start and end sizes of the army present a stark contrast.

Principle 2: 

Show causality, mechanism, explanation, systematic structure

The temperature and dates help the viewer to infer the reasons for the changes in army size on the return journey. The winter was harsh.

Principle 3:

Show multivariate data; that is, show more than 1 or 2 variables

The map displays 6 variables: army size, two-dimensional location, direction, temperature, and dates. In this case, the many variables are displayed cleanly and help convey the rich story of the campaign.

Principle 4:

Completely integrate words, numbers, images, diagrams

In other words, no need to be a purist; feel free to mix information types in the same figure. In this example, words are used to annotate the map, while a temperature graph dangles from the bottom. The information is immediately accessible, all in one place. To quote Tufte "In reasoning about substantive problems, what matters entirely is the evidence, not particular modes of evidence."

Principle 5:

Thoroughly describe the evidence. Provide a detailed title, indicate the authors and sponsors, document the data sources, show complete measurement scales, point out relevant issues.

The writing at the top of Marey's figure is a complete description about the topic of the figure, the author, the data sources, scales and assumptions. While this may seem natural and even requisite for scientific writing and figure design, in practice, there is room for improvement. Be purposeful and clear in documenting your evidence.

Principle 6:

Analytical presentations ultimately stand or fall depending on the quality, relevance, and integrity of their content.

Not only is Marey's figure beautifully designed, but the content is important. It highlights the enormous human cost of the Russian campaign, and is based on solid scholarship. To quote Tufte, "This suggests that the most effective way to improve a presentation is to get better content. It also suggests that design devices and gimmicks cannot salvage failed content."

When to Apply these Principles

Tufte ends this chapter with these words: "The purpose of an evidence presentation is to assist thinking. Thus presentations should be constructed so as to assist with the fundamental intellectual tasks in reasoning about evidence: describing the data, making multivariate comparisons, understanding causality, integrating a diversity of evidence, and documenting the analysis [...] If the intellectual task is to make comparisons, as it is in nearly all data analysis, then 'Show comparisons' is the design principle. If the intellectual task is to understand causality, then the design principle is to use architectures and data elements that show causality."

In other words, be thoughtful about the purpose of your figures. Often you will be creating a narrative, and each figure will play a particular role. A first figure may highlight a big-picture problem, while a central figure may display the results from a key experiment. A final figure may integrate the results into an explanation of causality. In each case, design the figure with its purpose in mind, and apply design principles accordingly.

Avoiding Bias in Visualization

Whether intentional or not, many scientific figures are designed to support an argument. Often, aspects of the data are emphasized or de-emphasized in an attempt to guide the readers through the author's argument. Other times, there is simply too much information to absorb quickly, and it may be difficult for readers to absorb everything without serious effort. Usually, these effects are not serious, but in medicine, where life-and-death decisions are being made on a daily basis, clear, easily-interpretable, unbiased visualization is far more critical. 

A recent blog post through Scientific American discusses the need for clear medical visualizations for use in educating patients on the risk associated with their treatment options. The principles discussed are applicable to any visualization effort, particularly those attempting to reach a broad audience.

PLOS Computational Biology: Ten Rules for Better Figures

A new article in PLOS Computational Biology presents 10 simple rules for better scientific figures:

1. Know your audience
2. Identify your message
3. Adapt the figure to the support medium
4. Captions are not optional
5. Do not trust the defaults
6. Use color effectively
7. Do not mislead the reader
8. Avoid "chartjunk"
9. Message trumps beauty
10. Get the right tool

The whole article is worth reading. The authors highlight a few "right tools", including MatPlotLib, R, Inkscape, GIMP, TikZ and PGF, ImageMagick, D3.js, Cytoscape, and Circos. Furthermore, they give excellent examples of both good and bad figures with accompanying information on how the figures were created.

Note our tutorials on the R graphing package ggplot2, Inkscape and GIMP.

Think Outside the Bargraph

A recent publication in PLOS Biology highlighted the need for scientists (in this case, physiologists) to learn more creative ways to display data. In top physiology journals, most data was displayed as a line graph or a bar graph--the problem being that many data distributions can lead to the same bar or line graphs, making it hard to interpret the results. Come on people, think outside the bargraph!

Learning by example--both good and bad

First, some great data visualizations to drool over.

And second, some of the worst, from which to learn caution.