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% Generated by roxygen2 (4.0.1): do not edit by hand
\name{stat_sum}
\alias{stat_sum}
\title{Sum unique values. Useful for overplotting on scatterplots.}
\usage{
stat_sum(mapping = NULL, data = NULL, geom = "point",
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{n}{number of observations at position}
\item{prop}{percent of points in that panel at that position}
}
\description{
Sum unique values. Useful for overplotting on scatterplots.
}
\section{Aesthetics}{
\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "sum")}
}
\examples{
\donttest{
d <- ggplot(diamonds, aes(x = cut, y = clarity))
# By default, all categorical variables in the plot form grouping
# variables, and the default behavior in stat_sum is to show the
# proportion. Specifying stat_sum with no group identifier leads to
# a plot which is not meaningful:
d + stat_sum()
# To correct this problem and achieve a more desirable plot, we need
# to specify which group the proportion is to be calculated over.
# There are several ways to do this:
# by overall proportion
d + stat_sum(aes(group = 1))
d + stat_sum(aes(group = 1)) + scale_size(range = c(3, 10))
d + stat_sum(aes(group = 1)) + scale_size_area(max_size = 10)
# by cut
d + stat_sum(aes(group = cut))
d + stat_sum(aes(group = cut, colour = cut))
# by clarity
d + stat_sum(aes(group = clarity))
d + stat_sum(aes(group = clarity, colour = cut))
# Instead of proportions, can also use sums
d + stat_sum(aes(size = ..n..))
# Can also weight by another variable
d + stat_sum(aes(group = 1, weight = price))
d + stat_sum(aes(group = 1, weight = price, size = ..n..))
# Or using qplot
qplot(cut, clarity, data = diamonds)
qplot(cut, clarity, data = diamonds, stat = "sum", group = 1)
}
}
\seealso{
\code{\link{ggfluctuation}} for a fluctuation diagram,
}
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