{"id":3162,"date":"2017-10-17T16:50:16","date_gmt":"2017-10-17T20:50:16","guid":{"rendered":"http:\/\/blogs.ams.org\/blogonmathblogs\/?p=3162"},"modified":"2017-10-17T16:50:16","modified_gmt":"2017-10-17T20:50:16","slug":"un-junking-your-charts","status":"publish","type":"post","link":"https:\/\/blogs.ams.org\/blogonmathblogs\/2017\/10\/17\/un-junking-your-charts\/","title":{"rendered":"Un-Junking your Charts"},"content":{"rendered":"

Junk Charts<\/a><\/span> is a blog by Kaiser Fung, who describes himself as \u201cthe Web\u2019s first data visualization critic.\u201d People have been criticizing and prescribing solutions for misleading data visualization for a long time. (How to Lie With Statistics<\/span><\/a> was first published<\/span><\/a> in 1954<\/span><\/a>, when a gallon of gas was 22 cents, a movie ticket was 70 cents, and the average new house was $10,250.00.) I don’t know whether Fung was literally the first to do it on the Web, but his blog has been around for over a decade and has an extensive archive of interesting posts for your perusal.<\/span><\/p>\n

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A found graph displays the density of cats in the vicinity of each part of the park bench. Credit: Evelyn Lamb<\/p><\/div>\n

When I first saw the title Junk Charts, I assumed it would be a blog that pointed out and made fun of bizarre and misleading graphs and charts. That\u2019s all good fun<\/span><\/a>, but this blog generally takes a less adversarial approach. Fung often examines data visualizations that are pretty good and shows how he would make them even more effective. For example, a recent post shows his suggested tweaks<\/span><\/a> for a Washington Post graphic about voter polarization. The original graphic isn\u2019t ugly or misleading, but the new one makes certain statistics jump out more readily.<\/span><\/p>\n

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Kaiser Fung’s redesigned display of information about how the two parties have diverged in the past few decades. Credit: Kaiser Fung. CC BY-NC-SA 3.0<\/p><\/div>\n

Some posts start with less successful original material, such as this post discussing a flawed chart<\/span><\/a> about politician approval ratings. On Pi Day 2014, Fung started the #onelesspie initiative<\/span><\/a> to replace pie charts with better charts. (Except when they are self-descriptive<\/span><\/a>, pie charts<\/span><\/a> are mostly bad<\/span><\/a>. Embrace non-pies<\/span><\/a>!) The #onelesspie posts in later years<\/span><\/a> have been entertaining<\/span><\/a>.<\/span><\/p>\n

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The only good pie chart is a self-descriptive one. Credit: Randall Munroe, xkcd. CC BY-NC 2.5.<\/p><\/div>\n

I don\u2019t have much experience creating data visualizations or working with statistics, so I\u2019ve enjoyed the perspective Fung brings in Junk Charts. Synergistically, while I was writing this post, the Information Is Beautiful website unveiled their Information is Beautiful Awards longlist<\/span><\/a> for this year, which has lots and lots of interesting and visually arresting data displays. I can use my gradually developing chart sense when public voting opens later this month.<\/span><\/p>\n

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Junk Charts is a blog by Kaiser Fung, who describes himself as \u201cthe Web\u2019s first data visualization critic.\u201d People have been criticizing and prescribing solutions for misleading data visualization for a long time. (How to Lie With Statistics was first … Continue reading →<\/span><\/a><\/p>\n

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