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, outline.type = "upper" ) 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
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
mapping if there is no plot
The data to be displayed in this layer. There are three options:
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
fortify() for which variables will be created.
function will be called with a single argument,
the plot data. The return value must be a
will be used as the layer data. A
function can be created
~ head(.x, 10)).
The statistical transformation to use on the data for this
layer, either as a
Geom subclass or as a string naming the
stat stripped of the
stat_ prefix (e.g.
"count" rather than
Position adjustment, either as a string naming the adjustment
"jitter" to use
position_jitter), or the result of a call to a
position adjustment function. Use the latter if you need to change the
settings of the adjustment.
Other arguments passed on to
layer(). These are
often aesthetics, used to set an aesthetic to a fixed value, like
colour = "red" or
size = 3. They may also be parameters
to the paired geom/stat.
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.
adjust = 1/2 means use half of the default bandwidth.
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
FALSE, the default, each density is computed on the
full range of the data. If
TRUE, each density is computed over the
range of that group: this typically means the estimated x values will
not line-up, and hence you won't be able to stack density values.
This parameter only matters if you are displaying multiple densities in
one plot or if you are manually adjusting the scale limits.
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.
FALSE never includes, and
TRUE always includes.
It can also be a named logical vector to finely select the aesthetics to
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.
Type of the outline of the area;
"both" draws both the
upper and lower lines,
"lower" draws the respective lines only.
"full" draws a closed polygon around the area.
Number of bins. Overridden by
binwidth. Defaults to 30.
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)