Stack overlapping objects on top of one another.
position_fill
additionally standardises each stack to have unit
height.
position_fill() position_stack()
Other position adjustments:
position_dodge()
,
position_identity()
,
position_jitterdodge()
,
position_jitter()
,
position_nudge()
# Stacking is the default behaviour for most area plots: ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar() # Fill makes it easier to compare proportions ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar(position = "fill") # To change stacking order, use factor() to change order of levels mtcars$vs <- factor(mtcars$vs, levels = c(1,0)) ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar() ggplot(diamonds, aes(price, fill = cut)) + geom_histogram(binwidth = 500) # When used with a histogram, position_fill creates a conditional density # estimate ggplot(diamonds, aes(price, fill = cut)) + geom_histogram(binwidth = 500, position = "fill") # Stacking is also useful for time series data.set <- data.frame( Time = c(rep(1, 4),rep(2, 4), rep(3, 4), rep(4, 4)), Type = rep(c('a', 'b', 'c', 'd'), 4), Value = rpois(16, 10) ) ggplot(data.set, aes(Time, Value)) + geom_area(aes(fill = Type)) # If you want to stack lines, you need to say so: ggplot(data.set, aes(Time, Value)) + geom_line(aes(colour = Type)) ggplot(data.set, aes(Time, Value)) + geom_line(position = "stack", aes(colour = Type)) # But realise that this makes it *much* harder to compare individual # trends
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