Scatter plot

48 posts
How to read this chart about coronavirus risk

In my just-published Long Read article at DataJournalism.com, I touched upon the subject of "How to Read this Chart". Most data graphics do not come with directions of use because dataviz designers follow certain conventions. We do not need to tell you, for example, that time runs left to right on the horizontal axis (substitute right to left for those living in right-to-left countries). It's when we deviate from the...

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The rule governing which variable to put on which axis, served a la mode

When making a scatter plot, the two variables should not be placed arbitrarily. There is a rule governing this: the outcome variable should be shown on the vertical axis (also called y-axis), and the explanatory variable on the horizontal (or x-) axis. This chart from the archives of the Economist has this reversed: The title of the accompanying article is "Ice Cream and IQ"... In a Trifecta Checkup (link), it's...

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Wayward legend takes sides in a chart of two sides, plus data woes

Reader Chris P. submitted the following graph, found on Axios: From a Trifecta Checkup perspective, the chart has a clear question: are consumers getting what they wanted to read in the news they are reading? Nevertheless, the chart is a visual mess, and the underlying data analytics fail to convince. So, it’s a Type DV chart. (See this overview of the Trifecta Checkup for the taxonomy.) *** The designer did...

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Clearing a forest of labels

This chart by the Financial Times has a strong message, and I like a lot about it: The countries are by and large aligned along a diagonal, with the poorer countries growing strongly between 2007-2019 while the richer countries suffered negative growth. A small issue with the chart is the thick forest of text - redundant text. The sub-title, the axis titles, the quadrant labels, and the left-right-half labels all...

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Clearing a forest of labels

This chart by the Financial Times has a strong message, and I like a lot about it: The countries are by and large aligned along a diagonal, with the poorer countries growing strongly between 2007-2019 while the richer countries suffered negative growth. A small issue with the chart is the thick forest of text - redundant text. The sub-title, the axis titles, the quadrant labels, and the left-right-half labels all...

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A data graphic that solves a consumer problem

Saw this great little sign at Ippudo, the ramen shop, the other day: It's a great example of highly effective data visualization. The names on the board are sake brands.  The menu (a version of a data table) is the conventional way of displaying this information. The Question Customers are selecting a sake. They don't have a favorite, or don't recognize many of these brands. They know a bit about...

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Visual Exploration of Unemployment Data

The charts on unemployment data I put up last week are best viewed as a collection.  I have put them up on the (still in beta) JMP Public website. You can find the project here.  I believe that if you make an account, you can grab the underlying dataset.  

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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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What to make of the historically low unemployment rate

One of the amazing economic stories of the moment is the unemployment rate, which at around 4% has returned to the level last reached during the peak of the tech boom in 2000. The story is much more complex than it seems. I devoted a chapter of Numbersense (link) to explain how the government computes unemployment rates. The most important thing to realize is that an unemployment rate of 4...

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Message-first visualization

Sneaky Pete via Twitter sent me the following chart, asking for guidance: This is a pretty standard dataset, frequently used in industry. It shows a breakdown of a company's profit by business unit, here classified by "state". The profit projection for the next year is measured on both absolute dollar terms and year-on-year growth. Since those two metrics have completely different scales, in both magnitude and unit, it is common...

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