Data

99 posts
Does this chart tell the sordid tale of T1’s decline?

The Hustle has an interesting article on the demise of the T1 calculator, which is popular in business circles. The article uses this bar chart: From a Trifecta Checkup perspective, this is a Type DV chart. (See this guide to the Trifecta Checkup.) The chart addresses a nice question: is the T1 graphing calculator a victim of new technologies? The visual design is marred by the use of the calculator...

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Announcement: Advancing your data skills, Fall 2019

Interrupting the flow of dataviz with the following announcement. If you're looking to shore up your data skills, modernize your skill set, or know someone looking for hands-on, high-touch instruction in Machine Learning, R, Cloud Computing, Data Quality, Digital Analytics,  A/B Testing and Financial Analysis, Principal Analytics Prep is offering evening classes this Fall. Click here to learn about our courses.  Our instructors are industry veterans with 10+ years of...

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Women workers taken for a loop or four

I was drawn to the following chart in Business Insider because of the calendar metaphor. (The accompanying article is here.) Sometimes, the calendar helps readers grasp concepts faster but I'm afraid the usage here slows us down. The underlying data consist of just four numbers: the wage gaps between race and gender in the U.S., considered simply from an aggregate median personal income perspective. The analyst adopts the median annual...

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What is a bad chart?

In the recent issue of Madolyn Smith’s Conversations with Data newsletter hosted by DataJournalism.com, she discusses “bad charts,” featuring submissions from several dataviz bloggers, including myself. What is a “bad chart”? Based on this collection of curated "bad charts", it is not easy to nail down “bad-ness”. The common theme is the mismatch between the message intended by the designer and the message received by the reader, a classic error...

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Men and women faced different experiences in the labor market

Last week, I showed how the aggregate statistics, unemployment rate, masked some unusual trends in the labor market in the U.S. Despite the unemployment rate in 2018 being equal, and even a little below, that in 2000, the peak of the last tech boom, there are now significantly more people "not in the labor force," and these people are not counted in the unemployment rate statistic. The analysis focuses on...

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Crazy rich Asians inspire some rich graphics

On the occasion of the hit movie Crazy Rich Asians, the New York Times did a very nice report on Asian immigration in the U.S. The first two graphics will be of great interest to those who have attended my free dataviz seminar (coming to Lyon, France in October, by the way. Register here.), as it deals with a related issue. The first chart shows an income gap widening between...

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Crazy rich Asians inspire some rich graphics

Kaiser Fung (Junkcharts, Principal Analytics Prep) examines several charts made by New York Times on income distribution among Asian Americans, as the hit movie Crazy Rich Asians dominates the U.S. box office in September.

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Some Tufte basics brought to you by your favorite birds

Someone sent me this via Twitter, found on the Data is Beautiful reddit: The chart does not deliver on its promise: It's tough to know which birds like which seeds. The original chart was also provided in the reddit: I can see why someone would want to remake this visualization. Let's just apply some Tufte fixes to it, and see what happens. Our starting point is this: First, consider the...

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Is the chart answering your question? Excavating the excremental growth map

San Franciscans are fed up with excremental growth. Understandably. Here is how the Economist sees it - geographically speaking. *** In the Trifecta Checkup analysis, one of the questions to ask is "What does the visual say?" and with respect to the question being asked. The question is how much has the problem of human waste in SF grew from 2011 to 2017. What does the visual say? The number...

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History of the word ‘data’

Sandra Rendgen describes the history of “data” the word and where it stands in present day. All through the evolution of statistics through the 19th century, data was generated by humans, and the scientific methodology of measuring and recording data had been a constant topic of debate. This is not trivial, as the question of how data is generated also answers the question of whether and how it is capable...

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