Stats for computing distribution functions (densities or CDFs) + intervals for use with
geom_slabinterval(). Uses dist aesthetic to specify a distribution name
and arg1, ... arg9 aesthetics (or args as a list column) to specify distribution
arguments.
stat_dist_slabinterval(
mapping = NULL,
data = NULL,
geom = "slabinterval",
position = "identity",
...,
slab_type = c("pdf", "cdf", "ccdf"),
p_limits = c(0.001, 0.999),
orientation = c("vertical", "horizontal"),
limits = NULL,
n = 501,
.width = c(0.66, 0.95),
show_slab = TRUE,
show_interval = TRUE,
na.rm = FALSE,
show.legend = c(size = FALSE),
inherit.aes = TRUE
)stat_dist_halfeye(...)
stat_dist_halfeyeh(..., orientation = "horizontal")
stat_dist_eye(..., side = "both")
stat_dist_eyeh(..., side = "both", orientation = "horizontal")
stat_dist_ccdfinterval(
...,
slab_type = "ccdf",
justification = 0.5,
side = "left",
normalize = "none"
)
stat_dist_ccdfintervalh(
...,
slab_type = "ccdf",
justification = 0.5,
side = "top",
orientation = "horizontal",
normalize = "none"
)
stat_dist_cdfinterval(
...,
slab_type = "cdf",
justification = 0.5,
side = "left",
normalize = "none"
)
stat_dist_cdfintervalh(
...,
slab_type = "cdf",
justification = 0.5,
side = "top",
orientation = "horizontal",
normalize = "none"
)
stat_dist_gradientinterval(
mapping = NULL,
data = NULL,
geom = "slabinterval",
position = "identity",
...,
justification = 0.5,
thickness = 1,
show.legend = c(size = FALSE, slab_alpha = FALSE),
inherit.aes = TRUE
)
stat_dist_gradientintervalh(..., orientation = "horizontal")
stat_dist_pointinterval(..., show_slab = FALSE)
stat_dist_pointintervalh(..., show_slab = FALSE, orientation = "horizontal")
stat_dist_interval(
mapping = NULL,
data = NULL,
geom = "interval",
position = "identity",
...,
show_slab = FALSE,
show_point = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
stat_dist_intervalh(..., orientation = "horizontal")
stat_dist_slab(
mapping = NULL,
data = NULL,
geom = "slab",
position = "identity",
...,
show.legend = NA,
inherit.aes = TRUE
)
stat_dist_slabh(..., orientation = "horizontal")
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)).
Use to override the default connection between
stat_slabinterval and geom_slabinterval()
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Other arguments passed to layer(). They may also be arguments to the paired geom
(e.g., geom_pointinterval())
The type of slab function to calculate: probability density (or mass) function ("pdf"),
cumulative distribution function ("cdf"), or complementary CDF ("ccdf").
Probability limits (as a vector of size 2) used to determine the lower and upper
limits of the slab. E.g., if this is c(.001, .999) (the default), then a slab is drawn
for the distribution from the quantile at p = .001 to the quantile at p = .999.
Whether this geom is drawn horizontally ("horizontal") or
vertically ("vertical"). When horizontal (resp. vertical), the geom uses the y (resp. x)
aesthetic to identify different groups, then for each group uses the x (resp. y) aesthetic and the
thickness aesthetic to draw a function as an slab, and draws points and intervals horizontally
(resp. vertically) using the xmin, x, and xmax (resp. ymin, y, and ymax)
aesthetics.
Manually-specified limits for the slab, as a vector of length two. These limits are combined with those
computed based on p_limits as well as the limits defined by the scales of the plot to determine the
limits used to draw the slab functions: these limits specify the maximal limits; i.e., if specified, the limits
will not be wider than these (but may be narrower).Use NA to leave a limit alone; e.g.
limits = c(0, NA) will ensure that the lower limit does not go below 0, but let the upper limit
be determined by either p_limits or the scale settings.
Number of points at which to evaluate slab_function
The .width argument passed to interval_function or point_interval.
Should the slab portion of the geom be drawn? Default TRUE.
Should the interval portion of the geom be drawn? Default TRUE.
If FALSE, the default, missing values are removed with a warning. If TRUE, missing
values are silently removed.
Should this layer be included in the legends? Default is c(size = FALSE), unlike most geoms,
to match its common use cases. FALSE hides all legends, TRUE shows all legends, and NA shows only
those that are mapped (the default for most geoms).
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().
Which side to draw the slab on. "topright", "top", and "right" are synonyms
which cause the slab to be drawn on the top or the right depending on if orientation is "horizontal"
or "vertical". "bottomleft", "bottom", and "left" are synonyms which cause the slab
to be drawn on the bottom of the left depending on if orientation is "horizontal" or
"vertical". "both" draws the slab mirrored on both sides (as in a violin plot).
Justification of the interval relative to the slab, where 0 indicates bottom/left
justification and 1 indicates top/right justification (depending on orientation). If justification
is NULL (the default), then it is set automatically based on the value of side: when side is
"top"/"right" justification is set to 0, when side is "bottom"/"left"
justification is set to 1, and when side is "both" justification is set to
0.5.
How to normalize heights of functions input to the thickness aesthetic. If "all"
(the default), normalize so that the maximum height across all data is 1; if "panels", normalize within
panels so that the maximum height in each panel is 1; if "xy", normalize within
the x/y axis opposite the orientation of this geom so that the maximum height at each value of the
opposite axis is 1; if "groups", normalize within values of the opposite axis and within
groups so that the maximum height in each group is 1; if "none", values are taken as is with no
normalization (this should probably only be used with functions whose values are in [0,1], such as CDFs).
Override for the thickness aesthetic in geom_slabinterval(): the thickness
of the slab at each x / y value of the slab (depending on orientation).
Should the point portion of the geom be drawn? Default TRUE.
These stats support the following aesthetics:
dist
args
arg1
arg2
arg3
arg4
arg5
arg6
arg7
arg8
arg9
x
y
datatype
thickness
size
group
In addition, in their default configuration (paired with geom_slabinterval()) the following aesthetics are supported by the underlying geom:
datatype
alpha
colour
linetype
fill
shape
stroke
point_colour
point_fill
point_alpha
point_size
size
interval_colour
interval_alpha
interval_size
interval_linetype
slab_size
slab_colour
slab_fill
slab_alpha
slab_linetype
y
ymin
ymax
x
xmin
xmax
width
height
thickness
group
See examples of some of these aesthetics in action in vignette("slabinterval").
Learn more about the sub-geom aesthetics (like interval_color) in the scales documentation.
Learn more about basic ggplot aesthetics in vignette("ggplot2-specs").
x or y: For slabs, the input values to the slab function.
For intervals, the point summary from the interval function. Whether it is x or y depends on orientation
xmin or ymin: For intervals, the lower end of the interval from the interval function.
xmax or ymax: For intervals, the upper end of the interval from the interval function.
f: For slabs, the output values from the slab function.
A highly configurable stat for generating a variety of plots that combine a "slab" that describes a distribution plus an interval. Several "shortcut" stats are provided which combine multiple options to create useful geoms, particularly eye plots (a combination of a violin plot and interval), half-eye plots (a density plus interval), and CCDF bar plots (a complementary CDF plus interval).
The shortcut stat names follow the pattern stat_dist_[name][h|], where the trailing
h (if present) indicates the horizontal version of the stat.
Stats include:
stat_dist_eye / stat_dist_eyeh: Eye plots (violin + interval)
stat_dist_halfeye / stat_dist_halfeyeh: Half-eye plots (density + interval)
stat_dist_ccdfinterval / stat_dist_ccdfintervalh: CCDF bar plots (CCDF + interval)
stat_dist_cdfinterval / stat_dist_cdfintervalh: CDF bar plots (CDF + interval)
stat_dist_gradientinterval / stat_dist_gradientintervalh: Density gradient + interval plots
stat_dist_pointinterval / stat_dist_pointintervalh: Point + interval plots
stat_dist_interval / stat_dist_intervalh: Interval plots
These stats expect a dist aesthetic to specify a distribution name
and arg1, ... arg9 aesthetics (or args as a list column) to specify distribution
arguments. Distribution names should correspond to R functions that have "p", "q", and
"d" functions; e.g. "norm" is a valid distribution name because R defines the
pnorm(), qnorm(), and dnorm() functions for Normal distributions.
See the parse_dist() function for a useful way to generate dist and args
values from human-readable distribution specs (like "normal(0,1)"). Such specs are also
produced by other packages (like the brms::get_prior() function in brms); thus,
parse_dist() combined with the stats described here can help you visualize the output
of those functions.
See geom_slabinterval() for more information on the geom these stats
use by default and some of the options they have. See stat_sample_slabinterval()
for the versions of these stats that can be used on samples.
See vignette("slabinterval") for a variety of examples of use.
# NOT RUN {
library(dplyr)
library(ggplot2)
tribble(
~group, ~subgroup, ~mean, ~sd,
"a", "h", 5, 1,
"b", "h", 7, 1.5,
"c", "h", 8, 1,
"c", "i", 9, 1,
"c", "j", 7, 1
) %>%
ggplot(aes(x = group, dist = "norm", arg1 = mean, arg2 = sd, fill = subgroup)) +
stat_dist_eye(position = "dodge")
# the stat_dist_... family applies a Jacobian adjustment to densities
# when plotting on transformed scales in order to plot them correctly.
# For example, here is a log-Normal distribution plotted on the log
# scale, where it will appear Normal:
data.frame(dist = "lnorm") %>%
ggplot(aes(y = 1, dist = dist, arg1 = log(10), arg2 = 2*log(10))) +
stat_dist_halfeyeh() +
scale_x_log10(breaks = 10^seq(-5,7, by = 2))
# see vignette("slabinterval") for many more examples.
# }
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