7.4: Plotting the Distribution of a Single Variable
- Page ID
- 8742
How do you choose which geometry to use? ggplot allows you to choose from a number of geometries. This choice will determine what sort of plot you create. We will use the built-in mpg dataset, which contains fuel efficiency data for a number of different cars.
7.4.1 Histogram
The histogram shows the ogerall distribution of the data. Here we use the nclass.FD function to compute the optimal bin size.
ggplot(mpg, aes(hwy)) +
geom_histogram(bins = nclass.FD(mpg$hwy)) +
xlab('Highway mileage')
Instead of creating discrete bins, we can look at relative density continuously.
7.4.2 Density plot
ggplot(mpg, aes(hwy)) +
geom_density() +
xlab('Highway mileage')
A note on defaults: The default statistic (or “stat”) underlying geom_density
is called “density” – not surprising. The default stat for geom_histogram
is “count”. What do you think would happen if you overrode the default and set stat="count"
?
ggplot(mpg, aes(hwy)) +
geom_density(stat = "count")
What we discover is that the geometric difference between geom_histogram
and geom_density
can actually be generalized. geom_histogram
is a shortcut for working with geom_bar
, and geom_density
is a shortcut for working with geom_line
.
7.4.3 Bar vs. line plots
ggplot(mpg, aes(hwy)) +
geom_bar(stat = "count")
Note that the geometry tells ggplot what kind of plot to use, and the statistic (stat) tells it what kind of summary to present.
ggplot(mpg, aes(hwy)) +
geom_line(stat = "density")