Variance

6 posts
Unintentional deception of area expansion #bigdata #piechart

Someone sent me this chart via Twitter, as an example of yet another terrible pie chart. (I couldn't find that tweet anymore but thank you to the reader for submitting this.) At first glance, this looks like a pie chart with the radius as a second dimension. But that is the wrong interpretation. In a pie chart, we typically encode the data in the angles of the pie sectors, or...

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A pretty good chart ruined by some naive analysis

The following chart showing wage gaps by gender among U.S. physicians was sent to me via Twitter: The original chart was published by the Stat News website (link). I am most curious about the source of the data. It apparently came from a website called Doximity, which collects data from physicians. Here is a link to the PR release related to this compensation dataset. However, the data is not freely...

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Visualizing citation impact

Michael Bales and his associates at Cornell are working on a new visual tool for citations data. This is an area that is ripe for some innovation. There is a lot of data available but it seems difficult to gain insights from them. The prototypical question is how authoritative is a particular researcher or research group, judging from his or her or their publications. A proxy for "quality" is the...

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Your charts need the gift of purpose

Via Twitter, I received this chart: My readers are nailing it when it comes to finding charts that deserve close study. On Twitter, the conversation revolved around the inversion of the horizontal axis. Favorability is associated with positive numbers, and unfavorability with negative numbers, and so, it seems the natural ordering should be to place Favorable on the right and Unfavorable on the left. Ordinarily, I'd have a problem with...

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Much more to do after selecting a chart form

I sketched out this blog post right before the Superbowl - and was really worked up as I happened to be flying into Atlanta right after they won (well, according to any of our favorite "prediction engines," the Falcons had 95%+ chance of winning it all a minute from the end of the 4th quarter!) What I'd give to be in the SuperBowl-winning city the day after the victory! Maybe...

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February talks, and exploratory data analysis using visuals

News: In February, I am bringing my dataviz lecture to various cities: Atlanta (Feb 7), Austin (Feb 15), and Copenhagen (Feb 28). Click on the links for free registration. I hope to meet some of you there. *** On the sister blog about predictive models and Big Data, I have been discussing aspects of a dataset containing IMDB movie data. Here are previous posts (1, 2, 3). The latest instalment...

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Is this chart rotten?

Some students pointed me to a FiveThirtyEight article about Rotten Tomatoes scores that contain the following chart: (link to original) This is a chart that makes my head spin. Too much is going on, and all the variables in the plot are tangled with each other. Even after looking at it for a while, I still don't understand how the author looked at the above and drew this conclusion: "Movies...

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Political winds and hair styling

Washington Post (link) and New York Times (link) published dueling charts last week, showing the swing-swang of the political winds in the U.S. Of course, you know that the pendulum has shifted riotously rightward towards Republican red in this election. The Post focused its graphic on the urban / not urban division within the country: Over Twitter, Lazaro Gamio told me they are calling these troll-hair charts. You certainly can...

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Depicting imbalance, straying from the standard chart

My friend Tonny M. sent me a tip to two pretty nice charts depicting the state of U.S. healthcare spending (link). The first shows U.S. as an outlier: This chart is a replica of the Lane Kenworthy chart, with some added details, that I have praised here before. This chart remains one of the most impactful charts I have seen. The added time-series details allow us to see a divergence...

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Lining up the dopers and their medals

The Times did a great job making this graphic (this snapshot is just the top half): A lot of information is packed into a small space. It's easy to compose the story in our heads. For example, Lee Chong Wai, the Malaysian badminton silver medalist, was suspended for doping for a short time during 2015, and he was second twice before the doping incident. They sorted the athletes according to...

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