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In the process of munging data for my current project I came across the need to compare (visually) the difference between two modes within the same dataset. I was using a simple scatterplot and setting the alpha in the hopes that the over-plotting would indicate which was the major mode. Unfortunately, the size of the data overwhelmed this approach.

I only wanted to use a single image and it was important that I keep the scatterplot to show other features of the data. I started looking for a way to combine a histogram (rotated 90 degrees) with the scatterplot to help describe the density within the plot. A quick search for how to do this in R turned up empty so I decided to implement my own version of such a plot.

Certainly, there are other ways to describe the features that I am trying to present here but in this particular case the following code worked out nicely. Hopefully it proves useful to others as well.

```plot.vertical.hist <- function(data,breaks=500) {

agg <- aggregate(data\$Y, by=list(xs=data\$X), FUN=mean)
hs <- hist(agg\$x / 10000, breaks=breaks, plot=FALSE)

mar.default <- par('mar')
mar.left <- mar.default
mar.right <- mar.default
mar.left <- 0
mar.right <- 0

# Main plot
par (fig=c(0,0.8,0,1.0), mar=mar.left)
plot (agg\$xs, agg\$x / 10000,
xlab="X", ylab="Y",
main="Vertical Histogram Side Plot",
pch=19, col=rgb(0.5,0.5,0.5,alpha=0.5))
grid ()

# Vertical histogram of the same data
par (fig=c(0.8,1.0,0.0,1.0), mar=mar.right, new=TRUE)
plot (NA, type='n', axes=FALSE, yaxt='n',
xlab='Frequency', ylab=NA, main=NA,
xlim=c(0,max(hs\$counts)),
ylim=c(1,length(hs\$counts)))
axis (1)
arrows(rep(0,length(hs\$counts)),1:length(hs\$counts),
hs\$counts,1:length(hs\$counts),
length=0,angle=0)

par(old.par)
invisible ()
}
```

Results look similar to the following:

Initially, I experimented with rug or barplot(..., horiz=TRUE). Unfortunately, rug isn't available on the left or right side and would suffer from the same problem that the alpha settings did and I was unable to get the alignment worked out when using barplot.