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the height of the bar no longer represent a count of observations, but a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.# By default, uses stat="bin", which gives the count in each category 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))
# 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(x = factor(cyl), y = mmpg)) + geom_bar(stat = "identity")
# 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,geom_bar uses 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 y aesthetic. If you want
the heights of the bars to represent values in the data, use
stat="identity" and map a value to the 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 position_dodge. Finally,
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
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