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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_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", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame.
, and
will be used as the layer data.
layer
. These are
often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.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
.geom_count
and stat_sum
.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()
# 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)
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