Aligning the visual and the message to hot things up

The headline of this NBC News chart (link) tells readers that Phoenix (Arizona) has been very, very hot this year. It has over 120 days in which the average temperature exceeded 100F (38 C).

It's not obvious how extreme this situation is. To help readers, it would be useful to add some kind of reference points.

A couple of possibilities come to mind:

First, how many days are depicted in the chart? Since there is one cell for each day of the year, and the day of week is plotted down the vertical axis, we just need to count the number of columns. There are 38 columns, but the first column has one missing cell while the last column has only 3 cells. Thus, the number of days depicted is (36*7)+6+3 = 261. So, the average temperature in Phoenix exceeded 100F on about 46% of the days of the year thus far.

That sounds like a high number. For a better reference point, we'd also like to know the historical average. Is Phoenix just a very hot place? Is 2024 hotter than usual?

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Let's walk through how one reads the Phoenix "heatmap".

We already figured out that each column represents a week of the year, and each row shows a cross-section of a given day of week throughout the year.

The first column starts on a Monday because the first day of 2024 falls on a Monday. The last column ends on a Tuesday, which corresponds to Sept 17, 2024, the last day of data when this chart was created.

The columns are grouped into months, although such division is complicated by the fact that the number of days in a month (except for a leap month) isn't ever divisible by seven. The designer subtly inserted a thicker border between months. This feature allows readers to comment on the average temperature in a given month. It also lets readers learn quickly that we are two weeks and three days into September.

The color legend explains that temperature readings range from yellow (lower) to red (higher). The range of average daily temperatures during 2024 was 54-118F (12-48C). The color scale is progressive.

Given that 100F is used as a threshold to define "hot days," it makes sense to accentuate this in the visual presentation. For example:

Here, all days with maximum temperature at 100F or above have a red hue.

Small tweaks that make big differences

It's one of those days that a web search led me to an unfamiliar corner, and I found myself poring over a pile of column charts that look like this:

This pair of charts appears to be canonical in a type of genetics analysis. I'll focus on the column chart up top.

The chart plots a variety of gene functions along the horizontal axis. These functions are classified into three broad categories, indicated using axis annotation.

What are some small tweaks that readers will enjoy?

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First, use colors. Here is an example in which the designer uses color to indicate the function classes:

The primary design difference between these two column charts is using three colors to indicate the three function classes. This little change makes it much easier to recognize the ending of one class and the start of the other.

Color doesn't have to be limited to column areas. The following example extends the colors to the axis labels:

Again, just a smallest of changes but it makes a big difference.

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It bugs me a lot that the long axis labels are printed in a slanted way, forcing every serious reader to read with slanted heads.

Slanting it the other way doesn't help:

Vertical labels are best read...

These vertical labels are best read while doing side planks.

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I'm surprised the horizontal alignment is rather rare. Here's one:

 

Tidying up the details

This column chart caught my attention because of the color labels.

Well, it also concerns me that the chart takes longer to take in than you'd think.

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The color labels say "FY2123", "FY2022", and "FY1921". It's possible but unlikely that the author is making comparisons across centuries. The year 2123 hasn't yet passed, so such an interpretation would map the three categories to long-ago past, present and far-into-the-future.

Perhaps hyphens were inadvertently left off so "FY2123" means "FY2021 - FY2023". It's odd to report financial metrics in multi-year aggregations. I rule this out because the three categories would then also overlap.

Here's what I think the mistake is: somehow the prefix is rolled forward when it is applied to the years. "FY23", "FY22", "FY21" got turned into "FY[21]23", "FY[20]22", "FY[19]21" instead of putting 20 in all three slots.

The chart appeared in an annual financial report, and the comparisons were mostly about the reporting year versus the year before so I'm pretty confident the last two digits are accurately represented.

Please let me know if you have another key to this puzzle.

In the following, I'm going to assume that the three colors represent the most recent three fiscal years.

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A few details conspire to blow up our perception time.

There was no extra spacing between groups of columns.

The columns are arranged in reverse time order, with the most recent year shown on the left. (This confuses those of us that use the left-to-right convention.)

The colors are not ordered. If asked to sort the three colors, you will probably suggest what is described as "intuitive" below:

The intuitive order aligns with the amount of black added to a base color (hue). But this isn't the order assigned to the three years on the original chart.

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Some of the other details on the chart are well done. For example, I like the handling of the gridlines and the axes.

The following revision tidies up some of the details mentioned above, without changing the key features of the chart:

 

Using disaggregation in dataviz

This chart appears in a journal article on the use of AI (artificial intelligence) in healthcare (link).

It's a stacked bar chart in which each bar is subdivided into four segments. The authors are interested in the relative frequency of research using AI by disease type. The chart only shows the top 10 disease types.

What is unusual is that the subdivisions are years. So these authors revealed four years of journal articles, and while the overall ranking of the disease types is by the aggregated four-year total counts, each total count has been disaggregated by color so readers can also see the annual counts.

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A slight rearrangement yields the following:

Most readers will only care about the left chart showing the total counts. More invested readers may consider the colored charts that show annual totals. These are arranged so that the annual counts are easily read and compared.

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One annoying aspect of this type of presentation is that in almost all cases, the top 10 types in aggregate will not be the top 10 types by individual year. In some of those years, I expect that the 10 types shown do not include all of the top 10 types for a particular year.