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Elevator shoes for column charts

Continuing my review of some charts spammed to me, I wasn’t expecting to find any interest in the following:


It’s a column chart showing the number of years of data available for different asset classes. The color has little value other than to subtly draw the reader’s attention to the bar called “Art,” which is the focus of the marketing copy.

Do the column heights encode the data?

The answer is no.


Let’s take a little journey. First I notice there is a grid behind the column chart, hanging above the baseline.

I marked out two columns with values 50 and 25, so the second column should be exactly half the height of the first. Each column consists of two parts, the first overlapping the grid while the second connecting the bottom of the grid to the baseline. The second part is a constant for every column; I label this distance Y.  

Against the grid, the column “50” spans 9 cells while the column “25” spans 4 cells. I label the grid height X. Now, if the first column is twice the height of the second, the equation: 9X + Y = 2*(4X+Y) should hold.

The only solution to this equation is X = Y. In other words, the distance between the bottom of the grid to the baseline must be exactly the height of one grid cell if the column heights were to faithfully represent the data. Well – it’s obvious that the former is larger than the latter.

In the revision, I have chopped off the excess height by moving the baseline upwards.


That’s the mechanics. Now, figuring out the motivation is another matter.

Chartjunk as marketing copy

I got some spam marketing message last week. How exciting. They even use a subject line that has absolutely nothing to do with its content, baiting me to open it. And open I did, to some data graphics horrors.

The marketer promises a whole series of charts to prove that art is a great asset class for investment returns.

The very first chart already caught my full attention. It's this one:


It's a simple bar chart, with four values. Looks innocuous.

I'm unable to appreciate the recent trend to align bars in the middle, rather than at their bases. So I converted it to the canonical form:


Do you see the problem?

The second value ($1.7 trillion) is exactly half the size of the first value ($3.4 trillion) and yet the second bar is two-thirds of the length of the first bar. So, the size of the second bar is exaggerated relative to its label – and that’s the bar displaying the market size for “art,” which is what the spammer is pitching.

The bottom pair of values share the same relationship: $0.8 trillion is exactly half of $1.6 trillion. Again, the relative lengths of those two bars are not 50% but slightly over 60%.


Did the designer think that the bar lengths could be customized to whatever s/he desires? This one is hard to crack.


The sixth chart in the series is a different kind of puzzle:


All three lines have the exact same labels but show different values over time.


And they have pie charts, of course. Take a look:


Something went wrong here too. I'll leave it to my readers who can certainly figure it out :)


These charts were probably spammed to at least thousands.


Two metrics in-fighting

The Wall Street Journal shows the following chart which pits two metrics against each other:


The primary metric is the change in median yearly salary between the two periods of time. We presume it's primary because of its presence in the chart title, and the blue bars being more readable than the green bubbles. The secondary metric is the median yearly salary in the later period.

That, I believe, was the intended design. When I saw this chart, my eyes went to the numbers inside the green bubbles. Perhaps it's because I didn't read the chart title first, and the horizontal axis wasn't labelled so it wasn't obvious what the blue bars coded.

As with most bubble charts, the data labels exist to cover up the inadequacy of circular areas. The self-sufficiency test - removing the data labels - shows this well:


It's simply impossible to know what values should be in each bubble, or to perceive the relative sizes of those bubbles.


Reversing the order of the blue bars also helps:


The original order is one of the more annoying features in most visualization packages. Because internally, the categories are numbered 1, 2, 3, ..., and because the convention is to have values run higher as they run up the vertical axis, these packages would place the top-ranked item at the bottom of the chart.

Most people read top to bottom, which means that they read the least important item first, and the most important item last!

In most visualization packages, it takes only 1 click or 1 action to reverse the order of the items. Please do it!


For change over time, I like using a Bumps chart, otherwise called a slope graph:


An elaborate data vessel

Visualcapitalist_globaloilproductionI recently came across the following dataviz showing global oil production (link).

This is an ambitious graphic that addresses several questions of composition.

The raw data show the amount of production by country adding up to the global total. The countries are then grouped by region. Further, the graph presents an oil-and-gas specific grouping, as indicated by the legend shown just below the chart title. This grouping is indicated by the color of the circumference of the circle containing the flag of the country.

This chart form is popular in modern online graphics programs. It is like an elaborate data vessel. Because the countries are lined up around the barrel, a space has been created on three sides to admit labels and text annotations. This is a strength of this chart form.


The chart conveys little information about the underlying data. Each country is given a unique odd shaped polygon, making it impossible to compare sizes. It’s definitely possible to pick out U.S., Russia, Saudi Arabia as the top producers. But in presenting the ranks of the data, this chart form pales in comparison to a straightforward data table, or a bar chart. The less said about presenting values, the better.

Indeed, our self-sufficiency test exposes the inability of these polygons to convey the data. This is precisely why almost all values of the dataset are present on the chart.


The dataviz subtly presumes some knowledge on the part of the readers.

The regions are not directly labeled. The readers must know that Saudi Arabia is in the Middle East, U.S. is part of North America, etc. Admittedly this is not a big ask, but it is an ask.

It is also assumed that readers know their flags, especially those of smaller countries. Some of the small polygons have no space left for country names and they are labeled with just flags.


In addition, knowing country acronyms is required for smaller countries as well. For example, in Africa, we find AGO, COG and GAB.


For this chart form the designer treats each country according to the space it has on the chart (except those countries that found themselves on the edges of the barrel). Font sizes, icons, labels, acronyms, data labels, etc. vary.

The readers are assumed to know the significance of OPEC and OPEC+. This grouping is given second fiddle, and can be found via the color of the circumference of the flag icons.


I’d have not assigned a color to the non-OPEC countries, and just use the yellow and blue for OPEC and OPEC+. This is a little edit but makes the search for the edges more efficient.



Let’s now return to the perception of composition.

In exactly the same manner as individual countries, the larger regions are represented by polygons that have arbitrary shapes. One can strain to compile the rank order of regions but it’s impossible to compare the relative values of production across regions. Perhaps this explains the presence of another chart at the bottom that addresses this regional comparison.

The situation is worse for the OPEC/OPEC+ grouping. Now, the readers must find all flag icons with edges of a specific color, then mentally piece together these arbitrarily shaped polygons, then realizing that they won’t fit together nicely, and so must now mentally morph the shapes in an area-preserving manner, in order to complete this puzzle.

This is why I said earlier this is an elaborate data vessel. It’s nice to look at but it doesn’t convey information about composition as readers might expect it to.