View source: R/autohistogram.R
geom_autodensity | R Documentation |
These versions of the histogram and density geoms have been designed
specifically for diagonal plotting with facet_matrix()
. They differ from
ggplot2::geom_histogram()
and ggplot2::geom_density()
in that they
defaults to mapping x
and y
to .panel_x
and .panel_y
respectively,
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,
outline.type = "upper"
)
geom_autohistogram(
mapping = NULL,
data = NULL,
stat = "autobin",
position = "floatstack",
...,
bins = NULL,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data for this
layer, either as a |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
... |
Other arguments passed on to |
bw |
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
|
adjust |
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
For example, |
kernel |
Kernel. See list of available kernels in |
n |
number of equally spaced points at which the density is to be
estimated, should be a power of two, see |
trim |
If |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
outline.type |
Type of the outline of the area; |
bins |
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)
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