geom_bar.Rd

% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/geom-bar-.r
\name{geom_bar}
\alias{geom_bar}
\title{Bars, rectangles with bases on x-axis}
\usage{
geom_bar(mapping = NULL, data = NULL, stat = "bin", position = "stack",
  show_guide = NA, ...)
}
\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 data frame. If specified, overrides the default data frame
defined at the top level of the plot.}

\item{stat}{The statistical transformation to use on the data for this
layer, as a string.}

\item{position}{Postion adjustment, either as a string, or the result of
a call to a position adjustment function.}

\item{show_guide}{logical. Should this layer be included in the legends?
\code{NA}, the default, includes if any aesthetics are mapped.
\code{FALSE} never includes, and \code{TRUE} always includes.}

\item{...}{other arguments passed on to \code{\link{layer}}. There are
  three types of arguments you can use here:

  \itemize{
  \item Aesthetics: to set an aesthetic to a fixed value, like
     \code{color = "red"} or \code{size = 3}.
  \item Other arguments to the layer, for example you override the
    default \code{stat} associated with the layer.
  \item Other arguments passed on to the stat.
  }}
}
\description{
The bar geom is used to produce 1d area plots: bar charts for categorical
x, and histograms for continuous y.  stat_bin explains the details of
these summaries in more detail.  In particular, you can use the
\code{weight} aesthetic to create weighted histograms and barcharts where
the height of the bar no longer represent a count of observations, but a
sum over some other variable.  See the examples for a practical
example.
}
\details{
The heights of the bars commonly represent one of two things: either a
count of cases in each group, or the values in a column of the data frame.
By default, \code{geom_bar} uses \code{stat="bin"}. This makes the height
of each bar equal to the number of cases in each group, and it is
incompatible with mapping values to the \code{y} aesthetic. If you want
the heights of the bars to represent values in the data, use
\code{stat="identity"} and map a value to the \code{y} aesthetic.

By default, multiple x's occuring in the same place will be stacked a top
one another by position_stack.  If you want them to be dodged from
side-to-side, see \code{\link{position_dodge}}. Finally,
\code{\link{position_fill}} shows relative propotions at each x by stacking
the bars and then stretching or squashing to the same height.

Sometimes, bar charts are used not as a distributional summary, but
instead of a dotplot.  Generally, it's preferable to use a dotplot (see
\code{geom_point}) as it has a better data-ink ratio.  However, if you do
want to create this type of plot, you can set y to the value you have
calculated, and use \code{stat='identity'}

A bar chart maps the height of the bar to a variable, and so the base of
the bar must always been shown to produce a valid visual comparison.
Naomi Robbins has a nice
\href{http://www.b-eye-network.com/view/index.php?cid=2468}{article on this topic}.
This is the reason it doesn't make sense to use a log-scaled y axis with a bar chart
}
\section{Aesthetics}{

\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("geom", "bar")}
}
\examples{
\donttest{
# Generate data
g <- ggplot(mtcars, aes(factor(cyl)))

# By default, uses stat = "bin", which gives the count in each category
g + geom_bar()
g + geom_bar(width = 0.5)
g + geom_bar() +
  coord_flip()
g + geom_bar(fill = "white", colour = "darkgreen")

# When the data contains y values in a column, use stat = "identity"
library(plyr)
# Calculate the mean mpg for each level of cyl
mm <- ddply(mtcars, "cyl", summarise, mmpg = mean(mpg))
ggplot(mm, aes(factor(cyl), mmpg)) +
  geom_bar(stat = "identity")

# Stacked bar charts
g <- ggplot(mtcars, aes(factor(cyl)))
g + geom_bar(aes(fill = factor(vs)))
g + geom_bar(aes(fill = factor(gear)))

# Stacked bar charts are easy in ggplot2, but not effective visually,
# particularly when there are many different things being stacked
ggplot(diamonds, aes(clarity, fill = cut)) +
  geom_bar()
ggplot(diamonds, aes(color, fill = cut)) +
  geom_bar() +
  coord_flip()

# Faceting is a good alternative:
ggplot(diamonds, aes(clarity)) +
  geom_bar() +
  facet_wrap(~ cut)
# If the x axis is ordered, using a line instead of bars is another
# possibility:
ggplot(diamonds, aes(clarity)) +
  geom_freqpoly(aes(group = cut, colour = cut))

# Dodged bar charts
ggplot(diamonds, aes(clarity, fill = cut)) +
  geom_bar(position = "dodge")
# compare with
ggplot(diamonds, aes(cut, fill = cut)) +
  geom_bar() +
  facet_grid(. ~ clarity)

# But again, probably better to use frequency polygons instead:
ggplot(diamonds, aes(clarity, colour = cut)) +
  geom_freqpoly(aes(group = cut))

# Often we don't want the height of the bar to represent the
# count of observations, but the sum of some other variable.
# For example, the following plot shows the number of diamonds
# of each colour

ggplot(diamonds) +
  geom_bar(aes(color))
# If, however, we want to see the total number of carats in each colour
# we need to weight by the carat variable
ggplot(diamonds) +
  geom_bar(aes(color, weight = carat)) +
  ylab("carat")

# A bar chart used to display means
meanprice <- tapply(diamonds$price, diamonds$cut, mean)
cut <- factor(levels(diamonds$cut), levels = levels(diamonds$cut))
df <- data.frame(meanprice, cut)
g <- ggplot(df, aes(cut, meanprice))
g + geom_bar(stat = "identity")
g + geom_bar(stat = "identity", fill = "grey50")

# Another stacked bar chart example
k <- ggplot(mpg, aes(x = manufacturer, fill = class))
k + geom_bar()
# Use scales to change aesthetics defaults
k + geom_bar() + scale_fill_brewer()
k + geom_bar() + scale_fill_grey()

# To change plot order of class varible
# use factor() to change order of levels
mpg$class <- factor(mpg$class, levels = c("midsize", "minivan",
"suv", "compact", "2seater", "subcompact", "pickup"))
m <- ggplot(mpg, aes(x = manufacturer, fill = class))
m + geom_bar()
}
}
\seealso{
\code{\link{stat_bin}} for more details of the binning algorithm,
  \code{\link{position_dodge}} for creating side-by-side barcharts,
  \code{\link{position_stack}} for more info on stacking,
}