A few months ago, I wrote a post about why and how to avoid dual axis charts. In that post, I focused exclusively on dual axis charts in which both series are lines, that is, showing changes over time. But there are other kinds of dual axis charts. In this post, I’ll focus on dual axis charts that show two categories on two separate axes.

Let’s use a simple example of this kind of dual axis chart, one that places two metrics on two axes. The graph below shows two metrics: life expectancy (measured in years) and health expenditures (measured as a percentage of GDP). The life expectancy metric is encoded as vertical bars associated with the left vertical axis and health expenditures are shown as dots associated with the right vertical axis.

As I argued in the previous post, these kind of dual axis charts can create several perceptual challenges:

- it’s not quite clear which series correspond to which axis
- the axis labels and gridlines may not match up (notice the percentage labels on the right vertical axis don’t sit on the gridlines)
- our eyes may move to intersections (or separations) in the two graph objects (i.e., bars and dots) that may not correspond to important differences.
- I’ll add one more here—they are just darn confusing. Too many bars and dots and overlaps and whatnot.

## Alternative Approaches

My previous post suggested several alternatives to the dual axis line chart problem, including arranging separate graphs vertically or horizontally, or alternative graph types like the connected scatterplot. In addition to simply leaving the two series on two separate graphs, there are at least three different alternative approaches.

**Scatterplot**

If the point of this kind of dual axis chart is to look at the relationship between the two series, how about a scatterplot? In this case, I put health expenditures on the horizontal axis and life expectancy on the vertical axis. I’m not sure if the scatterplot is more or less familiar than the dual axis chart to most readers, but I do find it a bit easier to read (admitedly, I’ve had a lot of experience reading this. kindof chart).

It’s also worth pointing out that, as Michael Friendly recently noted, Playfair’s famous dual axis chart might have been the best chart at the time because the scatterplot had not yet been invented.

The other advantage is that the scatterplot can include additional data such as sizing the circles to a third variable or adding additional years to the series. The graph below is as a scatterplot with trails (really just a connected scatterplot) from *Our World in Data* and has the same organization as the graph above but includes additional years of data.

**Marimekko/Mosaic**

Another alternative is to use a Marimekko chart, which scales the *widths* of bars in a vertical bar chart to a second variable. In this case, life expectancy is shown along the vertical axis and health expenditures as a share of total health expenditures among these countries is shown along the horizontal axis. (I learned how to create a Marimekko/Mosaic chart from this short video from The Information Lab.)

### Parallel Coordinates/Slope Chart

Yet one more alternative is to try a parallel coordinates plot. Personally, I’m not a huge fan of the standard parallel coordinates plots you might see in a Google search with tons of lines and multiple vertical axes. But with just two vertical axes, I think they can be okay; these data may not be the *best *example for this kind of chart, but hopefully you get the idea.

I added the words “Slope Chart” after the forward slash because many people would probably call this chart a slope chart. Personally, I reserve that term for a chart of the same format but when used to show change over time. The form is basically the same. (I learned how to create a Marimekko/Mosaic chart from this short video from The Information Lab.)

**Conclusion**

I’ll end this post the way I ended the first one: I hope I’ve demonstrated how dual axis charts can be troublesome. They can be confusing, difficult to read, and even harder to figure out what the point of the chart is supposed to be. Some of the alternatives here—not to mention the tried-and-true strategy of just using separate graphs—are hopefully strategies you can try in your own work.

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