```
# NOT RUN {
d <- ggplot(mtcars, aes(cyl, mpg)) + geom_point()
d + stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
# Orientation follows the discrete axis
ggplot(mtcars, aes(mpg, cyl)) +
geom_point() +
stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
# You can supply individual functions to summarise the value at
# each x:
d + stat_summary(fun = "median", colour = "red", size = 2, geom = "point")
d + stat_summary(fun = "mean", colour = "red", size = 2, geom = "point")
d + aes(colour = factor(vs)) + stat_summary(fun = mean, geom="line")
d + stat_summary(fun = mean, fun.min = min, fun.max = max,
colour = "red")
d <- ggplot(diamonds, aes(cut))
d + geom_bar()
d + stat_summary(aes(y = price), fun = "mean", geom = "bar")
# Orientation of stat_summary_bin is ambiguous and must be specified directly
ggplot(diamonds, aes(carat, price)) +
stat_summary_bin(fun = "mean", geom = "bar", orientation = 'y')
# }
# NOT RUN {
# Don't use ylim to zoom into a summary plot - this throws the
# data away
p <- ggplot(mtcars, aes(cyl, mpg)) +
stat_summary(fun = "mean", geom = "point")
p
p + ylim(15, 30)
# Instead use coord_cartesian
p + coord_cartesian(ylim = c(15, 30))
# A set of useful summary functions is provided from the Hmisc package:
stat_sum_df <- function(fun, geom="crossbar", ...) {
stat_summary(fun.data = fun, colour = "red", geom = geom, width = 0.2, ...)
}
d <- ggplot(mtcars, aes(cyl, mpg)) + geom_point()
# The crossbar geom needs grouping to be specified when used with
# a continuous x axis.
d + stat_sum_df("mean_cl_boot", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", fun.args = list(mult = 1), mapping = aes(group = cyl))
d + stat_sum_df("median_hilow", mapping = aes(group = cyl))
# An example with highly skewed distributions:
if (require("ggplot2movies")) {
set.seed(596)
mov <- movies[sample(nrow(movies), 1000), ]
m2 <- ggplot(mov, aes(x = factor(round(rating)), y = votes)) + geom_point()
m2 <- m2 + stat_summary(fun.data = "mean_cl_boot", geom = "crossbar",
colour = "red", width = 0.3) + xlab("rating")
m2
# Notice how the overplotting skews off visual perception of the mean
# supplementing the raw data with summary statistics is _very_ important
# Next, we'll look at votes on a log scale.
# Transforming the scale means the data are transformed
# first, after which statistics are computed:
m2 + scale_y_log10()
# Transforming the coordinate system occurs after the
# statistic has been computed. This means we're calculating the summary on the raw data
# and stretching the geoms onto the log scale. Compare the widths of the
# standard errors.
m2 + coord_trans(y="log10")
}
# }
```

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