Is that what you are looking for?
library(ggplot2)
x <- factor(rep(1:10, 100))
y <- rnorm(1000)
df <- data.frame(x=x, y=y)
ggplot(df, aes(x=x, y=y)) +
geom_boxplot() +
stat_summary(fun.y=mean, geom="line", aes(group=1)) +
stat_summary(fun.y=mean, geom="point")
Update:
Some clarification about setting group=1: I think that I found an explanation in Hadley Wickham's book "ggplot2: Elegant Graphics for Data Analysis". On page 51 he writes:
Different groups on different layers.
Sometimes we want to plot summaries
based on different levels of
aggregation. Different layers might
have different group aesthetics, so
that some display individual level
data while others display summaries of
larger groups.
Building on the previous example,
suppose we want to add a single smooth
line to the plot just created, based
on the ages and heights of all the
boys. If we use the same grouping for
the smooth that we used for the line,
we get the first plot in Figure 4.4.
p + geom_smooth(aes(group = Subject),
method="lm", se = F)
This is not what we wanted; we have
inadvertently added a smoothed line
for each boy. This new layer needs a
different group aesthetic, group = 1,
so that the new line will be based on
all the data, as shown in the second
plot in the figure. The modified layer
looks like this:
p + geom_smooth(aes(group = 1),
method="lm", size = 2, se = F)
[...] Using aes(group = 1) in the
smooth layer fits a single line of
best fit across all boys."