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", ..., outside = FALSE, sides = "bl", length = unit(0.03, "npc"), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )
Set of aesthetic mappings created by
The data to be displayed in this layer. There are three options:
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
logical that controls whether to move the rug tassels outside of the plot area. Default is off (FALSE). You will also need to use
A string that controls which sides of the plot the rugs appear on.
It can be set to a string containing any of
logical. Should this layer be included in the legends?
By default, the rug lines are drawn with a length that corresponds to 3% of the total plot size. Since the default scale expansion of for continuous variables is 5% at both ends of the scale, the rug will not overlap with any data points under the default settings.
geom_rug() understands the following aesthetics (required aesthetics are in bold):
Learn more about setting these aesthetics in
p + geom_rug()p + geom_rug(sides="b") # Rug on bottom onlyp + geom_rug(sides="trbl") # All four sides# Use jittering to avoid overplotting for smaller datasets ggplot(mpg, aes(displ, cty)) + geom_point() + geom_rug()# move the rug tassels to outside the plot # remember to set clip = "off". p + geom_rug(outside = TRUE) + coord_cartesian(clip = "off")# set sides to top right, and then move the margins p + geom_rug(outside = TRUE, sides = "tr") + coord_cartesian(clip = "off") + theme(plot.margin = margin(1, 1, 1, 1, "cm"))# increase the line length and # expand axis to avoid overplotting p + geom_rug(length = unit(0.05, "npc")) + scale_y_continuous(expand = c(0.1, 0.1))