geom_jitter(mapping = NULL, data = NULL, width = NULL, height = NULL, stat = "identity", position = "jitter", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
aes
or
aes_
. If specified and inherit.aes = TRUE
(the
default), is combined with the default mapping at the top level of the
plot. You only need to supply mapping
if there isn't a mapping
defined for the plot.FALSE
(the default), removes missing values with
a warning. If TRUE
silently removes missing values.NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.FALSE
, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. borders
.layer
. There are
three types of arguments you can use here:
color = "red"
or size = 3
.
stat
associated with the layer.
The jitter geom is a convenient default for geom_point with position = 'jitter'. It's a useful way of handling overplotting caused by discreteness in smaller datasets.
geom_point
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
shape
size
stroke
p <- ggplot(mpg, aes(cyl, hwy)) p + geom_point()
p + geom_jitter()
# Add aesthetic mappings p + geom_jitter(aes(colour = class))
# Use smaller width/height to emphasise categories ggplot(mpg, aes(cyl, hwy)) + geom_jitter()
ggplot(mpg, aes(cyl, hwy)) + geom_jitter(width = 0.25)
# Use larger width/height to completely smooth away discreteness ggplot(mpg, aes(cty, hwy)) + geom_jitter()
ggplot(mpg, aes(cty, hwy)) + geom_jitter(width = 0.5, height = 0.5)
geom_point
for regular, unjittered points,
geom_boxplot
for another way of looking at the conditional
distribution of a variable