geom_contour(mapping = NULL, data = NULL, stat = "contour", position = "identity", lineend = "butt", linejoin = "round", linemitre = 1, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)stat_contour(mapping = NULL, data = NULL, geom = "contour", position = "identity", na.rm = FALSE, 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.
Display contours of a 3d surface in 2d.
geom_contour
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
linetype
size
weight
#' # Basic plot v <- ggplot(faithfuld, aes(waiting, eruptions, z = density)) v + geom_contour()
# Or compute from raw data ggplot(faithful, aes(waiting, eruptions)) + geom_density2d()
# Setting bins creates evenly spaced contours in the range of the data v + geom_contour(bins = 2)
v + geom_contour(bins = 10)
# Setting binwidth does the same thing, parameterised by the distance # between contours v + geom_contour(binwidth = 0.01)
v + geom_contour(binwidth = 0.001)
# Other parameters v + geom_contour(aes(colour = ..level..))
v + geom_contour(colour = "red")
v + geom_raster(aes(fill = density)) + geom_contour(colour = "white")
geom_density2d
: 2d density contours