Contour and filled contour plots for tidy kernel estimates for 2-dimensional data.
geom_contour_ks(mapping=NULL, data=NULL, stat="contour_ks",
position="identity", ..., cont=c(25,50,75), label_percent=NULL,
breaks=NULL, show.legend=NA, inherit.aes=TRUE)
stat_contour_ks(mapping=NULL, data=NULL, geom="contour_ks",
position="identity", ..., cont=c(25,50,75), label_percent=NULL,
breaks=NULL, show.legend=NA, inherit.aes=TRUE)
geom_contour_filled_ks(mapping=NULL, data=NULL, stat="contour_filled_ks",
position="identity", ..., cont=c(25,50,75), label_percent=NULL,
breaks=NULL, show.legend=NA, inherit.aes=TRUE)
stat_contour_filled_ks(mapping=NULL, data=NULL, geom="contour_filled_ks",
position="identity", ..., cont=c(25,50,75), label_percent=NULL,
breaks=NULL, show.legend=NA, inherit.aes=TRUE)Similar output as the standard layer functions ggplot2::geom_contour, geom_contour_filled and ggplot2::stat_contour, stat_contour_filled.
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 mapping.
The data to be displayed in this layer. There are three options:
If NULL, the default, the data is inherited from the plot
data as specified in the call to ggplot().
A 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.
A function will be called with a single argument,
the plot data. The return value must be a data.frame, and
will be used as the layer data. A function can be created
from a formula (e.g. ~ head(.x, 10)).
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 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.
Vector of contour probabilities. Default value is cont=c(25,50,75).
Flag for legend label as percentage. Default is TRUE.
Numeric vector to set the contour breaks e.g. output from contour_breaks. Overrides cont.
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
display.
If 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. borders().
The geometric object to use display the data.
These layer functions are modifications of the standard layer functions ggplot2::geom_contour, geom_contour_filled and ggplot2::stat_contour, stat_contour_filled. Their usage and output are similar, except that they require a tidy kernel estimate as the input, rather than the observations themselves, and that the underlying choice of the contour levels is different. For most cases, geom_contour_ks is equivalent to geom_contour(stat="contour_ks"), and likewise for geom_contour_filled_ks.
The choice of the contour levels are based on probability contours. A 25% contour region is the smallest region that contains 25% of the probability mass defined by the kernel estimate. Probability contours offer a more intuitive approach to selecting the contour levels that reveal the pertinent characteristics of the kernel estimates. See Chacon & Duong (2018, Chapter 2.2). They are specified by the cont parameter: the default value is cont=c(25,50,75), which computes the upper quartile, median and lower quartile probability contours. If percent_label=TRUE, then the legend labels are given as these percentage in cont. Otherwise, the labels are the contour levels themselves.
Since these probability contours are computed for each group of the grouping variable in data, then these relative contour levels are different for each group. To produce a contour plot with fixed contour levels across all groups, then these can be supplied in breaks: a possible choice is provided by contour_breaks.
Chacon, J.E. & Duong, T. (2018) Multivariate Kernel Smoothing and Its Applications. Chapman & Hall/CRC, Boca Raton.
contour_breaks
library(ggplot2)
data(crabs, package="MASS")
crabs2 <- dplyr::select(crabs, FL, CW, sp)
crabs2 <- dplyr::group_by(crabs2, sp)
tt <- tidy_kde(crabs2)
gt <- ggplot(tt, aes(x=FL, y=CW))
gt + geom_contour_ks() + facet_wrap(~sp)
gt + geom_contour(stat="contour_ks") + facet_wrap(~sp) ## same output
gt + geom_contour_filled_ks(colour=1) + facet_wrap(~sp)
gt + geom_contour_filled(stat="contour_filled_ks", colour=1) +
facet_wrap(~sp) ## same output
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