These versions of the histogram and density geoms have been designed
specifically for diagonal plotting with
facet_matrix(). They differ from
ggplot2::geom_density() in that they
defaults to mapping
they ignore the y scale of the panel and fills it out, and they work for both
continuous and discrete x scales.
geom_autodensity(mapping = NULL, data = NULL, stat = "autodensity", position = "floatstack", ..., bw = "nrd0", adjust = 1, kernel = "gaussian", n = 512, trim = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) geom_autohistogram(mapping = NULL, data = NULL, stat = "autobin", position = "floatstack", ..., bins = NULL, 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:
Use to override the default connection between
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Other arguments passed on to
The smoothing bandwidth to be used.
If numeric, the standard deviation of the smoothing kernel.
If character, a rule to choose the bandwidth, as listed in
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
Kernel. See list of available kernels in
number of equally spaced points at which the density is to be
estimated, should be a power of two, see
This parameter only matters if you are displaying multiple
densities in one plot. If
logical. Should this layer be included in the legends?
Number of bins. Overridden by
facet_matrix for creating matrix grids
# A matrix plot with a mix of discrete and continuous variables p <- ggplot(mpg) + geom_autopoint() + facet_matrix(vars(drv:fl), layer.diag = 2, grid.y.diag = FALSE) p# Diagonal histograms p + geom_autohistogram()# Diagonal density distributions p + geom_autodensity()# You can use them like regular layers with groupings etc p + geom_autodensity(aes(colour = drv, fill = drv), alpha = 0.4)