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This is a good graphic, no doubt about it, and I think will often be my preferred approach to this type of data.

Interestingly, this is still basically a dual axis plot. This graphic, drawn with ggplot2 which has good defaults and does not allow easy configuration of faceted vertical axes, rightly chooses vertical scales to maximise use of the page - but the difficulty in over-riding that is a ggplot feature, not a feature of faceted scales in general. Because the choice of scale is a choice by the designer, objections 1 and 4 arbitrary differing scales, and vulnerable to malicious misleading apply to this sort of graphic as much as a dual axis plot.

Consider this alternative version:. I had to exit the facet paradigm of ggplot2 to do this because it rightly makes it difficult to warp the y axes of individual facets; but with a little effort I managed to make it look to a naive viewer as though the US exchange rate is more volatile than the trade weighted index.

What the facet version does do is make it harder to do micro comparisons. Finally, splitting the plot into facets does precisely nothing about avoiding problem 3. The fact is, confusing correlation with causation is rife, and making the correlation less obvious is not a sustainable solution.

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I chose the range of the scales shown by my two dual axes in that chart in such a way that the two series would be drawn effectively identically to this index plot. However, problems 2, 3, 4, 5 are not addressed in any way by this. Consider how the choice of reference period for the index impacts on the crossover point of the series, as shown in this alternative:. By constraining ourselves to an index we leave a few less options for arbitrarily changing scales in misleading ways, but we still to need to make design choices that materially impact on the look of the data.

We have still left the designer in charge of key decision about cross over of the series. And in the process, we have taken a decisive hit in interpretability by losing the original scale.

Two Time - Jack Stauber

Many of my stakeholders hate indexes with a passion, because of their abstract nature and the difficulty in explaining how they relate to units they are familiar with. Below, I suggest a method of dual axis plots that has the non-misleading scale properties of an index chart while retaining interpretability.

For analytical purposes I think a connected scatter plot should be the standard choice for examining two continuous variables change over time but see below for caveats on this. The strengths and weakenesses of this are apparent. It gets worse with longer time series and with seasonal data.

Multiple time dimensions

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Multiple time dimensions - Wikipedia

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