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Rug and scatter plots for tidy kernel estimates

geom_point_ks.Rd

Rug and scatter plots for tidy kernel estimates for 1- and 2-dimensional data.

Usage

geom_point_ks(mapping=NULL, data=NULL, stat="point_ks", position="identity", 
    ..., na.rm=FALSE, jitter=FALSE, show.legend=NA, inherit.aes=TRUE) 
stat_point_ks(mapping=NULL, data=NULL, geom="point_ks", position="identity", 
    ..., na.rm=FALSE, show.legend=NA, inherit.aes=TRUE)
geom_rug_ks(mapping=NULL, data=NULL, stat="rug_ks", position="identity", 
    ..., outside=FALSE, sides="bl", length=unit(0.03, "npc"), na.rm=FALSE, 
    jitter=FALSE, show.legend=NA, inherit.aes=TRUE) 
stat_rug_ks(mapping=NULL, data=NULL, geom="rug_ks", position="identity", 
    ..., na.rm=FALSE, show.legend=NA, inherit.aes=TRUE)

Arguments

mapping

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.

data

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)).

stat

The statistical transformation to use on the data for this layer, as a string.

position

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.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

jitter

Flag to jitter data before plot. Default value is FALSE.

outside

logical that controls whether to move the rug tassels outside of the plot area. Default is off (FALSE). You will also need to use coord_cartesian(clip = "off"). When set to TRUE, also consider changing the sides argument to "tr". See examples.

sides

A string that controls which sides of the plot the rugs appear on. It can be set to a string containing any of "trbl", for top, right, bottom, and left.

length

A grid::unit() object that sets the length of the rug lines. Use scale expansion to avoid overplotting of data.

show.legend

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.

inherit.aes

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().

geom

The geometric object to use display the data

Value

Similar output as the standard layer functions ggplot2::geom_point, ggplot2::geom_rug and ggplot2::stat_point.

Details

These layer functions are modifications of the standard layer functions ggplot2::geom_point, ggplot2::geom_rug and ggplot2::stat_point. Their usage and output are similar, except that they require a tidy kernel estimate as the input, rather than the observations themselves. For most cases, geom_rug_ks is equivalent to geom_rug(stat="rug_ks"), and likewise for geom_point_ks.

Examples

library(ggplot2)
data(crabs, package="MASS")

## rug plot for tidy 1-d kernel density estimate
crabs1 <- dplyr::select(crabs, FL)
t1 <- tidy_kde(crabs1)
g1 <- ggplot(t1, aes(x=FL)) + geom_line()
g1 + geom_rug_ks(colour=4) 


## scatter plot for tidy 2-d kernel density estimate
crabs2 <- dplyr::select(crabs, FL, CW)
t2 <- tidy_kde(crabs2)
g2 <- ggplot(t2, aes(x=FL, y=CW)) 
g2 + geom_contour_ks(aes(colour=after_stat(estimate))) + 
    geom_point_ks(colour=1)