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\title{1d kernel density estimate.}
stat_density(mapping = NULL, data = NULL, geom = "area",
position = "stack", adjust = 1, kernel = "gaussian",
trim = FALSE, na.rm = FALSE, ...)
\item{adjust}{see \code{\link{density}} for details}
\item{kernel}{kernel used for density estimation, see
\code{\link{density}} for details}
\item{trim}{if \code{TRUE}, the default, densities are
trimmed to the actual range of the data. If
\code{FALSE}, they are extended by the default 3
bandwidths (as specified by the \code{cut} parameter to
\item{na.rm}{If \code{FALSE} (the default), removes
missing values with a warning. If \code{TRUE} silently
removes missing values.}
\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.}
data.frame with additional columns:
\item{density}{density estimate} \item{count}{density *
number of points - useful for stacked density plots}
\item{scaled}{density estimate, scaled to maximum of 1}
1d kernel density estimate.
m <- ggplot(movies, aes(x = rating))
m + geom_density()
# Adjust parameters
m + geom_density(kernel = "rectangular")
m + geom_density(kernel = "biweight")
m + geom_density(kernel = "epanechnikov")
m + geom_density(adjust=1/5) # Very rough
m + geom_density(adjust=5) # Very smooth
# Adjust aesthetics
m + geom_density(aes(fill=factor(Drama)), size=2)
# Scale so peaks have same height:
m + geom_density(aes(fill=factor(Drama), y = ..scaled..), size=2)
m + geom_density(colour="darkgreen", size=2)
m + geom_density(colour="darkgreen", size=2, fill=NA)
m + geom_density(colour="darkgreen", size=2, fill="green")
# Change scales
(m <- ggplot(movies, aes(x=votes)) + geom_density(trim = TRUE))
m + scale_x_log10()
m + coord_trans(x="log10")
m + scale_x_log10() + coord_trans(x="log10")
# Also useful with
m + stat_bin()
# Make a volcano plot
ggplot(diamonds, aes(x = price)) +
stat_density(aes(ymax = ..density.., ymin = -..density..),
fill = "grey50", colour = "grey50",
geom = "ribbon", position = "identity") +
facet_grid(. ~ cut) +
# Stacked density plots
# If you want to create a stacked density plot, you need to use
# the 'count' (density * n) variable instead of the default density
# Loses marginal densities
qplot(rating, ..density.., data=movies, geom="density", fill=mpaa, position="stack")
# Preserves marginal densities
qplot(rating, ..count.., data=movies, geom="density", fill=mpaa, position="stack")
# You can use position="fill" to produce a conditional density estimate
qplot(rating, ..count.., data=movies, geom="density", fill=mpaa, position="fill")
# Need to be careful with weighted data
m <- ggplot(movies, aes(x=rating, weight=votes))
m + geom_histogram(aes(y = ..count..)) + geom_density(fill=NA)
m <- ggplot(movies, aes(x=rating, weight=votes/sum(votes)))
m + geom_histogram(aes(y=..density..)) + geom_density(fill=NA, colour="black")
library(plyr) # to access round_any
movies$decade <- round_any(movies$year, 10)
m <- ggplot(movies, aes(x=rating, colour=decade, group=decade))
m + geom_density(fill=NA)
m + geom_density(fill=NA) + aes(y = ..count..)
# Use qplot instead
qplot(length, data=movies, geom="density", weight=rating)
qplot(length, data=movies, geom="density", weight=rating/sum(rating))
\code{\link{stat_bin}} for the histogram
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