Arguing in favor of dual axes to show correlation

Charts that use two different scales on the same vertical often get the automatic “misleading” label, because if you stretch and shrink two data series enough, you’ll eventually find a way to make them look related. Toph Tucker argues that the automatic dismissal is misguided:

So yes, dual axes transform the series, and that transformation can lie. But it is the same kind of transformation that is already built into the Pearson correlation coefficient. Insofar as dual axes are bad, so is the Pearson correlation coefficient. Their merits and their badness go together. Dual axes are good at showing spurious correlation because they are good at showing correlation.

The challenge is that when you see a line chart with time on the horizontal axis and multiple data lines, it’s hard to separate coordinate systems and we’ve learned to read the lines as patterns over time. On the other hand, a scatterplot (or a connected one for time) highlights the relationship.

So while you don’t need to avoid dual axes completely, you should be careful when you do.

Tags: , ,

Use dual axes with care, if at all

Dual axes, where there are two value scales in a single chart, are almost never a good idea. As a reader, you should always question the source when you see a chart that uses such scales. Zan Armstrong explains with a recent example.

One of the best descriptions I’ve heard for data viz is that: when the data is different, the viz should look different and when the data is similar, the viz should look similar.

If you allow yourself to have two y-axis for the same metric, with both a different scale on each axis and a different base value, then you can make a lot of charts with the exact same data that look very different.

If there’s a direct transformation between the scales, say between metric and Imperial units, then okay, that’s fine. In almost all other cases, people use dual axes to overemphasize a relationship between two variables, and you should wonder why the maker did that.

Tags: ,