Ribbons and area plots.
For each continuous x value,
geom_interval displays a y interval.
geom_area is a special case of
geom_ribbon, where the
minimum of the range is fixed to 0.
geom_ribbon(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)geom_area(mapping = NULL, data = NULL, stat = "identity", position = "stack", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
- Set of aesthetic mappings created by
aes_. If specified and
inherit.aes = TRUE(the default), it is combined with the default mapping at the top level of the plot. You must supply
mappingif there is no plot mapping.
- The data to be displayed in this layer. There are three
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
fortifyfor which variables will be created.
functionwill 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.
- The statistical transformation to use on the data for this layer, as a string.
- Position adjustment, either as a string, or the result of a call to a position adjustment function.
- other arguments passed on to
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.
- logical. Should this layer be included in the legends?
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.
An area plot is the continuous analog of a stacked bar chart (see
geom_bar), and can be used to show how composition of the
whole varies over the range of x. Choosing the order in which different
components is stacked is very important, as it becomes increasing hard to
see the individual pattern as you move up the stack.
geom_ribbon understands the following aesthetics (required aesthetics are in bold):
# Generate data huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron)) h <- ggplot(huron, aes(year)) h + geom_ribbon(aes(ymin=0, ymax=level)) h + geom_area(aes(y = level)) # Add aesthetic mappings h + geom_ribbon(aes(ymin = level - 1, ymax = level + 1), fill = "grey70") + geom_line(aes(y = level))