Rug plots in the margins
A rug plot is a compact visualisation designed to supplement a 2d display with the two 1d marginal distributions. Rug plots display individual cases so are best used with smaller datasets.
geom_rug(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., sides = "bl", 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.
- A string that controls which sides of the plot the rugs appear on.
It can be set to a string containing any of
"trbl", for top, right, bottom, and left.
FALSE, the default, missing values are removed with a warning. If
TRUE, missing values are silently removed.
- 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.
The rug lines are drawn with a fixed size (3 are dependent on the overall scale expansion in order not to overplot existing data.
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point() p p + geom_rug() p + geom_rug(sides="b") # Rug on bottom only p + geom_rug(sides="trbl") # All four sides # Use jittering to avoid overplotting for smaller datasets ggplot(mpg, aes(displ, cty)) + geom_point() + geom_rug() ggplot(mpg, aes(displ, cty)) + geom_jitter() + geom_rug(alpha = 1/2, position = "jitter")