geom_pointthat 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)
NULL, the default, the data is inherited from the plot
data as specified in the call to
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.
function will be called with a single argument,
the plot data. The return value must be a
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
TRUEsilently removes missing values.
NA, the default, includes if any aesthetics are mapped.
FALSEnever includes, and
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.
geom_pointunderstands the following aesthetics (required aesthetics are in bold):
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)