Question

My question has to do with facetting. In my example code below, I look at some facetted scatterplots, then try to overlay information (in this case, mean lines) on a per-facet basis.

The tl;dr version is that my attempts fail. Either my added mean lines compute across all data (disrespecting the facet variable), or I try to write a formula and R throws an error, followed by incisive and particularly disparaging comments about my mother.

library(ggplot2)

# Let's pretend we're exploring the relationship between a car's weight and its
# horsepower, using some sample data
p <- ggplot()
p <- p + geom_point(aes(x = wt, y = hp), data = mtcars)
print(p)

# Hmm. A quick check of the data reveals that car weights can differ wildly, by almost
# a thousand pounds.
head(mtcars)

# Does the difference matter? It might, especially if most 8-cylinder cars are heavy,
# and most 4-cylinder cars are light. ColorBrewer to the rescue!
p <- p + aes(color = factor(cyl))
p <- p + scale_color_brewer(pal = "Set1")
print(p)

# At this point, what would be great is if we could more strongly visually separate
# the cars out by their engine blocks.
p <- p + facet_grid(~ cyl)
print(p)

# Ah! Now we can see (given the fixed scales) that the 4-cylinder cars flock to the
# left on weight measures, while the 8-cylinder cars flock right. But you know what
# would be REALLY awesome? If we could visually compare the means of the car groups.
p.with.means <- p + geom_hline(
                      aes(yintercept = mean(hp)),
                      data = mtcars
         )
print(p.with.means)

# Wait, that's not right. That's not right at all. The green (8-cylinder) cars are all above the
# average for their group. Are they somehow made in an auto plant in Lake Wobegon, MN? Obviously,
# I meant to draw mean lines factored by GROUP. Except also obviously, since the code below will
# print an error, I don't know how.
p.with.non.lake.wobegon.means <- p + geom_hline(
                                       aes(yintercept = mean(hp) ~ cyl),
                                       data = mtcars
                                     )
print(p.with.non.lake.wobegon.means)

There must be some simple solution I’m missing.

Answer 1

You mean something like this:

rs <- ddply(mtcars,.(cyl),summarise,mn = mean(hp))

p + geom_hline(data=rs,aes(yintercept=mn))

It might be possible to do this within the ggplot call using stat_*, but I’d have to go back and tinker a bit. But generally if I’m adding summaries to a faceted plot I calculate the summaries separately and then add them with their own geom.

EDIT

Just a few expanded notes on your original attempt. Generally it’s a good idea to put aes calls in ggplot that will persist throughout the plot, and then specify different data sets or aesthetics in those geom‘s that differ from the ‘base’ plot. Then you don’t need to keep specifying data = ... in each geom.

Finally, I came up with a kind of clever use of geom_smooth to do something similar to what your asking:

p <- ggplot(data = mtcars,aes(x = wt, y = hp, colour = factor(cyl))) + 
    facet_grid(~cyl) + 
    geom_point() + 
    geom_smooth(se=FALSE,method="lm",formula=y~1,colour="black")

The horizontal line (i.e. constant regression eqn) will only extend to the limits of the data in each facet, but it skips the separate data summary step.