Junk Charts

Welcome

Junk Charts is made by Kaiser Fung, the Web’s first data visualization critic. I discuss what makes graphics work, and how to make them better. Think chartjunk + junk art.

Here is my first post from more than ten years ago. These are the posts with the most (clicks): 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. There are some things I say over and over again: the Trifecta Checkup, the self-sufficiency test, bumps charts, small multiples, the power of aggregation. I carefully tag all posts with topics so clicking on map shows all posts about maps. A list of topics is on the right column. Here is a call for more dataviz criticism.

Here is my other blog, about statistics and Big Data. Here is my twitter feed, aggregating both blogs. If you prefer RSS, there's one feed for Junk Charts, and one for Big Data Plainly Spoken.

I also write popular books about statistics applied to daily life. Here is Numbers Rule Your World, and Numbersense. Tom Peters tweeted: On my 13-hour Boston-Dubai flight, I re-read cover-2-cover Kaiser Fung's superb-useful-fun book Number Sense. Trust him, or trust me.

I am a business statistician and speaker/trainer for hire. This is about a few hours in my life. I built the data teams at Vimeo (still part of the family) and Sirius XM Radio. Click here to write me. Some citations in the media. Talks. A free course.

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Oct 2014: I'm teaching my data visualization workshop. This is a great way for you to learn how to make great graphics in a fun, immersive setting. See a fuller description here.

Posted on Aug 14, 2014 at 08:15 AM | Permalink | Comments (2)

What if the RNC assigned seating randomly

The punditry has spoken: the most important data question at the Republican Convention is where different states are located. Here is the FiveThirtyEight take on the matter:

They crunched some numbers and argue that Trump's margin of victory in the state primaries is the best indicator of how close to the front that state's delegation is situated.

Others have put this type of information on a map:

The scatter plot with the added "trendline" is often misleading. Your eyes are drawn to the line, and distracted from the points that are far away from the line. In fact, the R-squared of the regression line is only about 20%. This is quite obvious from the distribution of green shades in the map below.

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So, I wanted to investigate the question of how robust this regression line is. The way statisticians address this question is as follows: imagine that the seating has been assigned completely at random - how likely would the actual seating plan have arisen from random assignment?

Take the seating assignments from the scatter plot. Then randomly shuffle the assignment to create simulated random seating plans. We keep the same slots, for example, four states were given #1 positions in the actual arrangement. In every simulation, four states got #1 positions - it's just that which four states were decided by flipping coins.

I did one hundred simulated seating plans at a time. For each plan, I created the scatter plot of seating position versus Trump margin (mirror image of the FiveThirtyEight chart), and fitted a regression line. The following shows the slopes of the first 200 simulations:

The more negative the slope, the more power Trump margin has in explaining the seating arrangement.

Notice that even though all these plans are created at random, the magnitude of the slopes range widely. In fact, there is one randomly created plan that sits right below the actual RNC plan shown in red. So, it is possible--but very unlikely--that the RNC plan is randomly drawn up.

Another view of this phenomenon is the histogram of the slopes:

This again shows that the actual seating plan is very unlikely to be produced by a random number generator. (I plotted 500 simulations here.)

In statistics, we measure rarity by "standard errors". The actual plan is almost but not quite three standard errors away from the average random plan. A rule of thumb is that 3 standard errors or more is rare. (This corresponds to over 99% confidence.)

PS. Does anyone have the data corresponding to the original scatter plot? There are other things I want to do with the data but I'd need to find (a) the seating position by state and (b) the primary results nicely set in a spreadsheet.

Posted on Jul 21, 2016 at 04:00 PM | Permalink | Comments (0)

Confusion is not limited to complex dataviz

This chart looks simple and harmless but I find it disarming.

I usually love the cheeky titles in the Economist but this title is very destructive to the data visualization. The chart has nothing to do with credit scores. In fact, credit scoring is associated with consumers while countries have credit ratings.

Also, I am not a fan of the Economist way of labeling negative axes. The negative sign situated between 0 and 1 looks like a stray hyphen that the editor missed.

A line chart would have brought out the pattern more sharply:

The pairing of columns in the original chart signals that readers should compare GDP growth to population growth. A good point, since GDP scales with population.

Controlling for population size can be accomplished by the per-capita GDP growth rate.

The last three years are clearly different. By this metric, different in a good way.

This chart creates a problem for the journalist. The article is about the deal to "save" Puerto Rico which some has criticized as colonial. Presumably, the territory has been in dire straits. There are plenty of metrics to illustrate this point but GDP growth is not one of them.

Posted on Jul 07, 2016 at 08:23 AM | Permalink | Comments (3)

A Tufte fix for austerity

Trish, who attended one of my recent data visualization workshops, submitted a link to the Illinois Atlas of Austerity.

Shown on the right is one of the charts included in the presentation.

This is an example of a chart that fails my self-sufficiency test.

There is no chance that readers are getting any information out of the graphical elements (the figurines of 100 people each).

Everyone who tries to learn something from this chart will be reading the data labels directly.

The entire dataset is printed on the chart itself. If you cover up the data, the chart becomes unreadable!

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Here is a simple fix that resettles the figurines onto a bar chart:

Tufte would not be amused by this composition. The figurines are purely decorative.

This version is more likely to delight Tufte:

It is the edges of the bars in the bar chart that make all the difference.

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Aside from the visual problems, there is also a data issue. They should have controlled by the size of different programs.

Posted on Jun 29, 2016 at 08:03 AM | Permalink | Comments (1)

Raining, data art, if it ain't broke

Via Twitter, reader Joe D. asked a few of us to comment on the SparkRadar graphic by WeatherSpark.

At the time of writing, the picture for Baltimore is very pretty:

The picture for New York is not as pretty but still intriguing. We are having a bout of summer and hence the white space (no precipitation):

Interpreting this innovative chart is a tough task - this is a given with any innovative chart. Explaining the chart requires all the text on this page.

The difficulty of interpreting the SparkRadar chart is twofold.

Firstly, the axes are unnatural. Time runs vertically, defying the horizontal convention. Also, "now" - the most recent time depicted - is at the very bottom, which tempts readers to read bottom to top, meaning we are reading time running backwards into the past. In most charts, time run left to right from past to present (at least in the left-right-centric part of the world that I live in.)

Location has been reduced to one dimension. The labels "Distance Inside" and "Distance from Storm" confuse me - perhaps those who follow weather more closely can justify the labels. Conventionally, location is shown in two dimensions.

The second difficulty is created by the inclusion of irrelevant data (aka noise). The square grid prescribes a fixed box inside which all data are depicted. In the New York graphic, something is going on in the top right corner - far away in both time and space - how does it help the reader?

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Now, contrast this chart to the more standard one, a map showing rain "clouds" moving through space.