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% Generated by roxygen2 (4.0.1): do not edit by hand
\name{stat_summary}
\alias{stat_summary}
\title{Summarise y values at every unique x.}
\usage{
stat_summary(mapping = NULL, data = NULL, geom = "pointrange",
position = "identity", ...)
}
\arguments{
\item{mapping}{The aesthetic mapping, usually constructed with
\code{\link{aes}} or \code{\link{aes_string}}. Only needs to be set
at the layer level if you are overriding the plot defaults.}
\item{data}{A layer specific dataset - only needed if you want to override
the plot defaults.}
\item{geom}{The geometric object to use display the data}
\item{position}{The position adjustment to use for overlappling points
on this layer}
\item{...}{other arguments passed on to \code{\link{layer}}. This can
include aesthetics whose values you want to set, not map. See
\code{\link{layer}} for more details.}
}
\value{
a data.frame with additional columns:
\item{fun.data}{Complete summary function. Should take data frame as
input and return data frame as output}
\item{fun.ymin}{ymin summary function (should take numeric vector and
return single number)}
\item{fun.y}{y summary function (should take numeric vector and return
single number)}
\item{fun.ymax}{ymax summary function (should take numeric vector and
return single number)}
}
\description{
\code{stat_summary} allows for tremendous flexibilty in the specification
of summary functions. The summary function can either supply individual
summary functions for each of y, ymin and ymax (with \code{fun.y},
\code{fun.ymax}, \code{fun.ymin}), or return a data frame containing any
number of aesthetiics with with \code{fun.data}. All summary functions
are called with a single vector of values, \code{x}.
}
\details{
A simple vector function is easiest to work with as you can return a single
number, but is somewhat less flexible. If your summary function operates
on a data.frame it should return a data frame with variables that the geom
can use.
}
\section{Aesthetics}{
\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "summary")}
}
\examples{
\donttest{
# 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)
# 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, ...)
}
# 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", mult = 1, mapping = aes(group = cyl))
d + stat_sum_df("median_hilow", mapping = aes(group = cyl))
# There are lots of different geoms you can use to display the summaries
d + stat_sum_df("mean_cl_normal", mapping = aes(group = cyl))
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")
}
}
\seealso{
\code{\link{geom_errorbar}}, \code{\link{geom_pointrange}},
\code{\link{geom_linerange}}, \code{\link{geom_crossbar}} for geoms to
display summarised data
}
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