% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom-bar.r, R/geom-col.r, R/stat-count.r
\name{geom_bar}
\alias{geom_bar}
\alias{geom_col}
\alias{stat_count}
\title{Bar charts}
\usage{
geom_bar(mapping = NULL, data = NULL, stat = "count",
  position = "stack", ..., width = NULL, binwidth = NULL,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

geom_col(mapping = NULL, data = NULL, position = "stack", ...,
  width = NULL, na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE)

stat_count(mapping = NULL, data = NULL, geom = "bar",
  position = "stack", ..., width = NULL, na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE)
}
\arguments{
\item{mapping}{Set of aesthetic mappings created by \code{\link[=aes]{aes()}} or
\code{\link[=aes_]{aes_()}}. If specified and \code{inherit.aes = TRUE} (the
default), it is combined with the default mapping at the top level of the
plot. You must supply \code{mapping} if there is no plot mapping.}

\item{data}{The data to be displayed in this layer. There are three
options:

If \code{NULL}, the default, the data is inherited from the plot
data as specified in the call to \code{\link[=ggplot]{ggplot()}}.

A \code{data.frame}, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
\code{\link[=fortify]{fortify()}} for which variables will be created.

A \code{function} will be called with a single argument,
the plot data. The return value must be a \code{data.frame}, and
will be used as the layer data. A \code{function} can be created
from a \code{formula} (e.g. \code{~ head(.x, 10)}).}

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

\item{...}{Other arguments passed on to \code{\link[=layer]{layer()}}. These are
often aesthetics, used to set an aesthetic to a fixed value, like
\code{colour = "red"} or \code{size = 3}. They may also be parameters
to the paired geom/stat.}

\item{width}{Bar width. By default, set to 90\% of the resolution of the data.}

\item{binwidth}{\code{geom_bar()} no longer has a binwidth argument - if
you use it you'll get an warning telling to you use
\code{\link[=geom_histogram]{geom_histogram()}} instead.}

\item{na.rm}{If \code{FALSE}, the default, missing values are removed with
a warning. If \code{TRUE}, missing values are silently removed.}

\item{show.legend}{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.
It can also be a named logical vector to finely select the aesthetics to
display.}

\item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. \code{\link[=borders]{borders()}}.}

\item{geom, stat}{Override the default connection between \code{geom_bar()} and
\code{stat_count()}.}
}
\description{
There are two types of bar charts: \code{geom_bar()} and \code{geom_col()}.
\code{geom_bar()} makes the height of the
bar proportional to the number of cases in each group (or if the
\code{weight} aesthetic is supplied, the sum of the weights). If you want the
heights of the bars to represent values in the data, use
\code{geom_col()} instead. \code{geom_bar()} uses \code{stat_count()} by
default: it counts the number of cases at each x position. \code{geom_col()}
uses \code{stat_identity()}: it leaves the data as is.
}
\details{
A bar chart uses height to represent a value, and so the base of the
bar must always be shown to produce a valid visual comparison. This is why
it doesn't make sense to use a log-scaled y axis with a bar chart.

By default, multiple bars occupying the same \code{x} position will be stacked
atop one another by \code{\link[=position_stack]{position_stack()}}. If you want them to be dodged
side-to-side, use \code{\link[=position_dodge]{position_dodge()}} or \code{\link[=position_dodge2]{position_dodge2()}}. Finally,
\code{\link[=position_fill]{position_fill()}} shows relative proportions at each \code{x} by stacking the bars
and then standardising each bar to have the same height.
}
\section{Aesthetics}{

\code{geom_bar()} understands the following aesthetics (required aesthetics are in bold):
\itemize{
\item \strong{\code{x}}
\item \strong{\code{y}}
\item \code{alpha}
\item \code{colour}
\item \code{fill}
\item \code{group}
\item \code{linetype}
\item \code{size}
}
Learn more about setting these aesthetics in \code{vignette("ggplot2-specs")}.


\code{geom_col()} understands the following aesthetics (required aesthetics are in bold):
\itemize{
\item \strong{\code{x}}
\item \strong{\code{y}}
\item \code{alpha}
\item \code{colour}
\item \code{fill}
\item \code{group}
\item \code{linetype}
\item \code{size}
}
Learn more about setting these aesthetics in \code{vignette("ggplot2-specs")}.


\code{stat_count()} understands the following aesthetics (required aesthetics are in bold):
\itemize{
\item \strong{\code{x}}
\item \code{group}
\item \code{weight}
\item \code{y}
}
Learn more about setting these aesthetics in \code{vignette("ggplot2-specs")}.
}

\section{Computed variables}{

\describe{
\item{count}{number of points in bin}
\item{prop}{groupwise proportion}
}
}

\examples{
# geom_bar is designed to make it easy to create bar charts that show
# counts (or sums of weights)
g <- ggplot(mpg, aes(class))
# Number of cars in each class:
g + geom_bar()
# Total engine displacement of each class
g + geom_bar(aes(weight = displ))

# Bar charts are automatically stacked when multiple bars are placed
# at the same location. The order of the fill is designed to match
# the legend
g + geom_bar(aes(fill = drv))

# If you need to flip the order (because you've flipped the plot)
# call position_stack() explicitly:
g +
 geom_bar(aes(fill = drv), position = position_stack(reverse = TRUE)) +
 coord_flip() +
 theme(legend.position = "top")

# To show (e.g.) means, you need geom_col()
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))
ggplot(df, aes(trt, outcome)) +
  geom_col()
# But geom_point() displays exactly the same information and doesn't
# require the y-axis to touch zero.
ggplot(df, aes(trt, outcome)) +
  geom_point()

# You can also use geom_bar() with continuous data, in which case
# it will show counts at unique locations
df <- data.frame(x = rep(c(2.9, 3.1, 4.5), c(5, 10, 4)))
ggplot(df, aes(x)) + geom_bar()
# cf. a histogram of the same data
ggplot(df, aes(x)) + geom_histogram(binwidth = 0.5)
}
\seealso{
\code{\link[=geom_histogram]{geom_histogram()}} for continuous data,
\code{\link[=position_dodge]{position_dodge()}} and \code{\link[=position_dodge2]{position_dodge2()}} for creating side-by-side
bar charts.

\code{\link[=stat_bin]{stat_bin()}}, which bins data in ranges and counts the
cases in each range. It differs from \code{stat_count}, which counts the
number of cases at each \code{x} position (without binning into ranges).
\code{\link[=stat_bin]{stat_bin()}} requires continuous \code{x} data, whereas
\code{stat_count} can be used for both discrete and continuous \code{x} data.
}
