Summarise y values at every unique x.

Usage

stat_summary(mapping = NULL, data = NULL, geom = "pointrange", position = "identity", 
  ...)

Arguments

mapping

The aesthetic mapping, usually constructed with aes or aes_string. Only needs to be set at the layer level if you are overriding the plot defaults.

data

A layer specific dataset - only needed if you want to override the plot defaults.

geom

The geometric object to use display the data

position

The position adjustment to use for overlappling points on this layer

...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Examples

# Basic operation on a small dataset
d <- qplot(cyl, mpg, data=mtcars)
d + stat_summary(fun.data = "mean_cl_boot", colour = "red")



p <- qplot(cyl, mpg, data = mtcars, stat="summary", fun.y = "mean")
p


# Don't use ylim to zoom into a summary plot - this throws the
# data away
p + ylim(15, 30)

Warning message:
Removed 9 rows containing missing values (stat_summary).

# Instead use coord_cartesian
p + coord_cartesian(ylim = c(15, 30))



# You can supply individual functions to summarise the value at
# each x:

stat_sum_single <- function(fun, geom="point", ...) {
  stat_summary(fun.y=fun, colour="red", geom=geom, size = 3, ...)
}

d + stat_sum_single(mean)


d + stat_sum_single(mean, geom="line")


d + stat_sum_single(median)


d + stat_sum_single(sd)



d + stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max,
  colour = "red")



d + aes(colour = factor(vs)) + stat_summary(fun.y = mean, geom="line")



# Alternatively, you can supply a function that operates on a data.frame.
# 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 + stat_sum_df("mean_cl_boot")


d + stat_sum_df("mean_sdl")


d + stat_sum_df("mean_sdl", mult=1)


d + stat_sum_df("median_hilow")



# There are lots of different geoms you can use to display the summaries

d + stat_sum_df("mean_cl_normal")


d + stat_sum_df("mean_cl_normal", geom = "errorbar")


d + stat_sum_df("mean_cl_normal", geom = "pointrange")


d + stat_sum_df("mean_cl_normal", geom = "smooth")



# Summaries are more useful with a bigger data set:
mpg2 <- subset(mpg, cyl != 5L)
m <- ggplot(mpg2, aes(x=cyl, y=hwy)) +
        geom_point() +
        stat_summary(fun.data = "mean_sdl", geom = "linerange",
                     colour = "red", size = 2, mult = 1) +
       xlab("cyl")
m


# An example with highly skewed distributions:
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")

See also