geom_count | R Documentation |
This is a variant geom_point()
that counts the number of
observations at each location, then maps the count to point area. It
useful when you have discrete data and overplotting.
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
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
... |
Other arguments passed on to
|
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
geom , stat |
Use to override the default connection between
|
geom_point()
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
group
shape
size
stroke
Learn more about setting these aesthetics in vignette("ggplot2-specs")
.
These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.
after_stat(n)
Number of observations at position.
after_stat(prop)
Percent of points in that panel at that position.
For continuous x
and y
, use geom_bin_2d()
.
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 = after_stat(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 = after_stat(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 = after_stat(prop), group = cut)) +
scale_size_area(max_size = 10)
d + geom_count(aes(size = after_stat(prop), group = clarity)) +
scale_size_area(max_size = 10)
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