geom_count(mapping = NULL, data = NULL, stat = "sum", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)stat_sum(mapping = NULL, data = NULL, geom = "point", position = "identity", 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.
geom_count
and stat_sum
.This is a variant geom_point
that counts the number of
observations at each location, then maps the count to point size. It
useful when you have discrete data.
geom_point
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
shape
size
stroke
ggplot(mpg, aes(cty, hwy)) + geom_point()
ggplot(mpg, aes(cty, hwy)) + geom_count()
# Best used in conjunction with scale_size_area which ensures that # counts of zero would be given size 0. Doesn't make much different # here because the smallest count is already close to 0. ggplot(mpg, aes(cty, hwy)) + geom_count()
scale_size_area()<ggproto object: Class ScaleContinuous, Scale> aesthetics: size break_info: function break_positions: function breaks: waiver call: call clone: function dimension: function expand: waiver get_breaks: function get_breaks_minor: function get_labels: function get_limits: function guide: legend is_discrete: function is_empty: function labels: waiver limits: NULL map: function map_df: function minor_breaks: waiver na.value: NA name: waiver oob: function palette: function range: <ggproto object: Class RangeContinuous, Range> range: NULL reset: function train: function super: <ggproto object: Class RangeContinuous, Range> rescaler: function reset: function scale_name: area train: function train_df: function trans: trans transform: function transform_df: function super: <ggproto object: Class ScaleContinuous, Scale># Display proportions instead of counts ------------------------------------- # By default, all categorical variables in the plot form the groups. # Specifying geom_count without a group identifier leads to a plot which is # not useful: d <- ggplot(diamonds, aes(x = cut, y = clarity)) d + geom_count(aes(size = ..prop..))
# To correct this problem and achieve a more desirable plot, we need # to specify which group the proportion is to be calculated over. d + geom_count(aes(size = ..prop.., group = 1)) + scale_size_area(max_size = 10)
# Or group by x/y variables to have rows/columns sum to 1. d + geom_count(aes(size = ..prop.., group = cut)) + scale_size_area(max_size = 10)
d + geom_count(aes(size = ..prop.., group = clarity)) + scale_size_area(max_size = 10)