This ggplot2 scale linearly scales all
thickness values of geoms
that support the
thickness aesthetic (such as
can be used to align the
thickness scales across multiple geoms (by default,
thickness is normalized on a per-geom level instead of as a global scale).
The name of the scale. Used as the axis or legend title. If
waiver(), the default, the name of the scale is taken from the first
mapping used for that aesthetic. If
NULL, the legend title will be
NULL for no labels
waiver() for the default labels computed by the
A character vector giving labels (must be same length as
An expression vector (must be the same length as breaks). See ?plotmath for details.
A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation.
NULL to use the default scale range
A numeric vector of length two providing limits of the scale.
NA to refer to the existing minimum or maximum
A function that accepts the existing (automatic) limits and returns
new limits. Also accepts rlang lambda function
Note that setting limits on positional scales will remove data outside of the limits.
If the purpose is to zoom, use the limit argument in the coordinate system
When mapping values to the
thickness scale, should those
values be allowed to be renormalized by geoms (e.g. via the
geom_slabinterval())? The default is
is in use, the geom-specific
normalize parameter is ignored (this is achieved
by flagging values as already normalized by wrapping them in
Set this to
TRUE to allow geoms to also apply their own normalization.
Function that handles limits outside of the scale limits (out of bounds). Also accepts rlang lambda function notation.
The default (
scales::censor()) replaces out of
bounds values with
scales::squish() for squishing out of bounds values into range.
scales::squish_infinite() for squishing infinite values into range.
A function used to create a guide or its name. See
guides() for more information.
Arguments passed on to
The names of the aesthetics that this scale works with.
The name of the scale that should be used for error messages associated with this scale.
A palette function that when called with a numeric vector with
values between 0 and 1 returns the corresponding output values
An integer guiding the number of major breaks. The algorithm
may choose a slightly different number to ensure nice break labels. Will
only have an effect if
breaks = waiver(). Use
NULL to use the default
number of breaks given by the transformation.
A function used to scale the input values to the
range [0, 1]. This is always
scales::rescale(), except for
diverging and n colour gradients (i.e.,
rescaler is ignored by position
scales, which always use
scales::rescale(). Also accepts rlang
lambda function notation.
For position scales, a vector of range expansion constants used to add some
padding around the data to ensure that they are placed some distance
away from the axes. Use the convenience function
to generate the values for the
expand argument. The defaults are to
expand the scale by 5% on each side for continuous variables, and by
0.6 units on each side for discrete variables.
Missing values will be replaced with this value.
For continuous scales, the name of a transformation object or the object itself. Built-in transformations include "asn", "atanh", "boxcox", "date", "exp", "hms", "identity", "log", "log10", "log1p", "log2", "logit", "modulus", "probability", "probit", "pseudo_log", "reciprocal", "reverse", "sqrt" and "time".
A transformation object bundles together a transform, its inverse,
and methods for generating breaks and labels. Transformation objects
are defined in the scales package, and are called
scales::boxcox_trans()). You can create your own
For position scales, The position of the axis.
right for y axes,
bottom for x axes.
The super class to use for the constructed scale
An object (typically a
numeric()) to be converted to a
A ggplot2::Scale representing a scale for the
ggdist geoms. Can be added to a
By default, normalization/scaling of slab thicknesses is controlled by geometries,
not by a ggplot2 scale function. This allows various functionality not
otherwise possible, such as (1) allowing different geometries to have different
thickness scales and (2) allowing the user to control at what level of aggregation
(panels, groups, the entire plot, etc) thickness scaling is done via the
However, this default approach has one drawback: two different geoms will always
have their own scaling of
scale_thickness_shared() offers an
alternative approach: when added to a chart, all geoms will use the same
thickness scale, and geom-level normalization (via their
is ignored. This is achieved by "marking" thickness values as already
normalized by wrapping them in the
thickness() data type (this can be
disabled by setting
renormalize = TRUE).
thickness() is used by
scale_thickness_shared() to create
objects marked as being in units of slab "thickness". Unlike regular
thickness() values mapped onto the
thickness aesthetic are not rescaled by
geom_slabinterval(). In most cases
is not useful directly; though it can be used to mark values that should not be
rescaled---see the definitions of
for some usages.
Note: while a slightly more typical name for
scale_thickness_continuous(), the latter name would cause this scale
to be applied to all
thickness aesthetics by default according to the rules
ggplot2 uses to find default scales. Thus, to retain the usual behavior
stat_slabinterval() (per-geom normalization of
thickness), this scale
library(distributional) library(ggplot2) library(dplyr) prior_post = data.frame( prior = dist_normal(0, 1), posterior = dist_normal(0.1, 0.5) ) # By default, separate geoms have their own thickness scales, which means # distributions plotted using two separate geoms will not have their slab # functions drawn on the same scale (thus here, the two distributions have # different areas under their density curves): prior_post %>% ggplot() + stat_halfeye(aes(xdist = posterior)) + stat_slab(aes(xdist = prior), fill = NA, color = "red") # For this kind of prior/posterior chart, it makes more sense to have the # densities on the same scale; thus, the areas under both would be the same. # We can do that using scale_thickness_shared(): prior_post %>% ggplot() + stat_halfeye(aes(xdist = posterior)) + stat_slab(aes(xdist = prior), fill = NA, color = "#e41a1c") + scale_thickness_shared()