A Guide to Data Visualization

Source: UX Motel, @FlavienP

Source: UX Motel, @FlavienP

One of the most interesting parts of having an active twitter feed is the immediate feedback you get to posts based on retweets, quotes, and favorites.   I found this chart on twitter and retweeted it, and it has been one of my most popular tweets.  I thought it would be valuable to feature it on the blog.

This chart gives some simple guidance as to how to think about visually displaying your data based on what it is you'd like to highlight.   The chart breaks things into four useful categories of relationships:

Composition:  If you'd like to highlight how a key variable is changing over time, the bar charts and pie charts provide a way to highlight compositional changes.   Our friends at FlowingData.com provide this doozy of a pie chart for your entertainment pleasure.   But all kidding aside, a well-made pie chart can be a tremendously powerful way to illustrate composition and I find them to be intuitive even to non-statistically oriented types.

Distribution:   Illustrating distributions is about trying to show the full spread of the data.  I think most people think of distributions by hearkening back to the results from an exam in high school;   the teacher explains that 3 people in the class got an A, 15 people got a B, and 3 got a C, and 1 got an F--this is a distribution.   I suspect most people (if they think about distributions at all) are familiar with the bell curve.  Here is an interesting Forbes article, however, on research that suggests that in the work place, most value is created by a small percentage of hyper performers at the very top of the distribution.

Comparison:  How do the experiences of one group differ from the experiences of another?  In comparison charts, we simply want to draw out similarities or differences between the outcomes or experiences of different sets of people.   In this line chart from a recent article on income inequality in The Huffington Post, we see a comparison of real average after-tax income for different wage earners over time.

Relationships:  In the real world, we think all the time about how two things relate.  If I spend more time exercising, how much weight will I lose?  If I save an extra dollar today, how much more will I have for retirement in 20 years?  Charts designed to show the relationships between two variables abound--and can be some of the most misleading or informative depending on presentation and content.  More on this later--but for now, enjoy this article on the relationship between margarine and divorce rates.