geom_bar.Rd

\name{geom_bar}
\alias{geom_bar}
\title{Bars, rectangles with bases on x-axis...}
\usage{geom_bar(mapping, data, stat="bin", position="stack", ...)}

\description{
  Bars, rectangles with bases on x-axis
}

\details{
  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  where the height of the bar no longer represent a count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.

  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.

  If you have presummarised data, use
  \code{stat="identity"} to turn off the default summary.

  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
}
\seealso{\code{\link{stat_bin}} for more details of the binning alogirithm, 
\code{\link{position_dodge}} for creating side-by-side barcharts, 
\code{\link{position_stack}} for more info on stacking,}
\examples{# Generate data
c <- ggplot(mtcars, aes(factor(cyl)))

c + geom_bar()
c + geom_bar(width=.5)
c + geom_bar() + coord_flip()
c + geom_bar(fill="white", colour="darkgreen")

# Use qplot
qplot(factor(cyl), data=mtcars, geom="bar")
qplot(factor(cyl), data=mtcars, geom="bar", fill=factor(cyl))

# Stacked bar charts    
qplot(factor(cyl), data=mtcars, geom="bar", fill=factor(vs))
qplot(factor(cyl), data=mtcars, geom="bar", 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
qplot(color, data=diamonds, geom="bar")
# If, however, we want to see the total number of carats in each colour
# we need to weight by the carat variable
qplot(color, data=diamonds, geom="bar", 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))
qplot(cut, meanprice)
qplot(cut, meanprice, geom="bar", stat="identity")
qplot(cut, meanprice, geom="bar", stat="identity", fill = I("grey50"))

# Another stacked bar chart example
k <- ggplot(mpg, aes(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(manufacturer, fill=class))
m + geom_bar()}