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where the height of the bar no longer represent a count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.geom_bar(mapping = NULL, data = NULL, stat = "bin",
position = "stack", ...)aes or aes_string. Only
needs to be set at the layer level if you are overriding
the plot defaults.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()
stat_bin for more details of the binning
alogirithm, position_dodge for creating
side-by-side barcharts, position_stack for
more info on stacking,position_dodge. Finally,
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
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 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 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