**socialdatablog » R**, and kindly contributed to R-bloggers)

I wrote before about heatmap tables as a better way of producing frequency or other tables, with a solution which works nicely in latex.

It is possible to do them much more easily in ggplot2, like this

library(Hmisc) library(ggplot2) library(reshape) data(HairEyeColor) P=t(HairEyeColor[,,2]) Pm=melt(P) ggfluctuation(Pm,type="heatmap")+geom_text(aes(label=Pm$value),colour="white")+ opts(axis.text.x=theme_text(size = 15),axis.text.y=theme_text(size = 15))

Note that ggfluctuation will also take a table as input, but in this case P isn’t a table, it is a matrix, so we have to melt it using the reshape package.

Here is the output from the code above:

However, doing the marginal totals would be a bit of a faff like this.

Notice that this is statistically quite a different animal – unlike the previous version, the colours just divide the range of values. They are not indications of any kind of significant deviation from expected values. So they are less useful to the careful reader but on the other hand need no explanation.

Note also that ggfluctuation produces by default a different output

which is better in many ways. But it looks like a graphic, not a table, and the point of heatmap tables is you can slip them in where your reader expects a table and you don’t have to do so much explaining.

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