How to compare dependent variables with different units using Grammar of Graphics paradigm? My data analysis workflow is through R and ggplot2, in part because ggplot2 discourages me from making 'bad' (misleading, etc.) plots. However, I sometimes would like to make plots that compare multiple unlike dependent variables against a common independent variable, like below:
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This is usually done by adding multiple axes.  ggplot can't add multiple axes.
I realize that I have several ways around this: I could move out of ggplot for this type of plot, or I could probably hack something within ggplot.
My question is how I should deal with with this. The ggplot way to deal with this is by facetting, but often I want to facet by other variables. (For instance, with the pictured example, I might have collected the data at multiple locations and altitudes).
Is Grammar of Graphics just a bad paradigm for this kind of dataset, or is there some presentation option that would work well?
 A: Call me a heretic, but I completely disagree with this flame war on double axes; yes, they can be extremely deceiving, but only when you plot two things of a same unit (i.e. when their sum or difference makes sense) -- then the plot suggest that they have an equal range, which is usually not the case (like income of two companies).
But if you have two different units, this problem cease to exist -- viewer's mind instantly rejects the idea that values are important and focuses on co-occurring patterns.
Also if you want to show a nonlinear and lagged correlation, scatterplots and ratio plots will be completely useless, autocorrelation may be inconclusive and will show only a fraction of information and facets will hide the nature of the lag -- I see no better option here:


Moreover, double axes are great for a completely benign task of showing something in two linearly dependent units, like temperature in °C and K.
Finally, they are simply ubiquitous in physics (meteorology included) and engineering and none of those people feel wrong about it.
A: I think your graph can mislead about the relationship.  The following, which just reverses one of the lines, tells a rather more informative story about which changes first and shows when one changes and the other does not.

and would be better still if the one of the lines resulted from a linear regression of the red data and black data.  
If each lines is a linear function of the original data, I would then have less objection to adding the two scales, even though it could still confuse as one would be upside down.   
A: Given the comments, I suggest graphing the ratio of one variable to the other. This is easier for people to track than looking at and comparing two lines. The idea is similar to the one highlighted by William Cleveland in one of his books where he looks at a graph by Playfair of England's imports vs. exports and converts it to one of the gap between them. However, with different scales, difference makes less sense than ratio.
The ratio may not have any intuitive appeal, itself, but it will aid the visualization. 
