Description Usage Arguments See Also Examples
View source: R/autohistogram.R
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  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
)

mapping 
Set of aesthetic mappings created by 
data 
The data to be displayed in this layer. There are three options: If A A 
stat 
Use to override the default connection between

position 
Position adjustment, either as a string, or the result of a call to a position adjustment function. 
... 
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 
bins 
Number of bins. Overridden by 
facet_matrix for creating matrix grids
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  # 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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