Shortcut version of `stat_slabinterval()`

with `geom_interval()`

for
creating multiple-interval plots.

Roughly equivalent to:

```
stat_slabinterval(
aes(colour = after_stat(level), size = NULL),
geom = "interval",
show_point = FALSE, .width = c(0.5, 0.8, 0.95), show_slab = FALSE,
show.legend = NA
)
```

```
stat_interval(
mapping = NULL,
data = NULL,
geom = "interval",
position = "identity",
...,
.width = c(0.5, 0.8, 0.95),
point_interval = "median_qi",
orientation = NA,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
```

- 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)`

).- geom
Use to override the default connection between

`stat_interval()`

and`geom_interval()`

- position
Position adjustment, either as a string, or the result of a call to a position adjustment function. Setting this equal to

`"dodge"`

(`position_dodge()`

) or`"dodgejust"`

(`position_dodgejust()`

) can be useful if you have overlapping geometries.- ...
Other arguments passed to

`layer()`

. These are often aesthetics, used to set an aesthetic to a fixed value, like`colour = "red"`

or`linewidth = 3`

(see**Aesthetics**, below). They may also be parameters to the paired geom/stat. When paired with the default geom,`geom_interval()`

, these include:`interval_size_range`

A length-2 numeric vector. This geom scales the raw size aesthetic values when drawing interval and point sizes, as they tend to be too thick when using the default settings of

`scale_size_continuous()`

, which give sizes with a range of`c(1, 6)`

. The`interval_size_domain`

value indicates the input domain of raw size values (typically this should be equal to the value of the`range`

argument of the`scale_size_continuous()`

function), and`interval_size_range`

indicates the desired output range of the size values (the min and max of the actual sizes used to draw intervals). Most of the time it is not recommended to change the value of this argument, as it may result in strange scaling of legends; this argument is a holdover from earlier versions that did not have size aesthetics targeting the point and interval separately. If you want to adjust the size of the interval or points separately, you can also use the`linewidth`

or`point_size`

aesthetics; see scales.`interval_size_domain`

A length-2 numeric vector giving the minimum and maximum of the values of the

`size`

and`linewidth`

aesthetics that will be translated into actual sizes for intervals drawn according to`interval_size_range`

(see the documentation for that argument.)

- .width
The

`.width`

argument passed to`point_interval`

: a vector of probabilities to use that determine the widths of the resulting intervals. If multiple probabilities are provided, multiple intervals per group are generated, each with a different probability interval (and value of the corresponding`.width`

and`level`

generated variables).- point_interval
A function from the

`point_interval()`

family (e.g.,`median_qi`

,`mean_qi`

,`mode_hdi`

, etc), or a string giving the name of a function from that family (e.g.,`"median_qi"`

,`"mean_qi"`

,`"mode_hdi"`

, etc; if a string, the caller's environment is searched for the function, followed by the ggdist environment). This function determines the point summary (typically mean, median, or mode) and interval type (quantile interval,`qi`

; highest-density interval,`hdi`

; or highest-density continuous interval,`hdci`

). Output will be converted to the appropriate`x`

- or`y`

-based aesthetics depending on the value of`orientation`

. See the`point_interval()`

family of functions for more information.- orientation
Whether this geom is drawn horizontally or vertically. One of:

`NA`

(default): automatically detect the orientation based on how the aesthetics are assigned. Automatic detection works most of the time.`"horizontal"`

(or`"y"`

): draw horizontally, using the`y`

aesthetic to identify different groups. For each group, uses the`x`

,`xmin`

,`xmax`

, and`thickness`

aesthetics to draw points, intervals, and slabs.`"vertical"`

(or`"x"`

): draw vertically, using the`x`

aesthetic to identify different groups. For each group, uses the`y`

,`ymin`

,`ymax`

, and`thickness`

aesthetics to draw points, intervals, and slabs.

For compatibility with the base ggplot naming scheme for

`orientation`

,`"x"`

can be used as an alias for`"vertical"`

and`"y"`

as an alias for`"horizontal"`

(ggdist had an`orientation`

parameter before base ggplot did, hence the discrepancy).- na.rm
If

`FALSE`

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

, missing values are silently removed.- show.legend
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).- 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()`

.

A ggplot2::Stat representing a multiple-interval geometry which can
be added to a `ggplot()`

object.

**To visualize sample data**, such as a data distribution, samples from a
bootstrap distribution, or a Bayesian posterior, you can supply samples to
the `x`

or `y`

aesthetic.

**To visualize analytical distributions**, you can use the `xdist`

or `ydist`

aesthetic. For historical reasons, you can also use `dist`

to specify the distribution, though
this is not recommended as it does not work as well with orientation detection.
These aesthetics can be used as follows:

`xdist`

,`ydist`

, and`dist`

can be any distribution object from the distributional package (`dist_normal()`

,`dist_beta()`

, etc) or can be a`posterior::rvar()`

object. Since these functions are vectorized, other columns can be passed directly to them in an`aes()`

specification; e.g.`aes(dist = dist_normal(mu, sigma))`

will work if`mu`

and`sigma`

are columns in the input data frame.`dist`

can be a character vector giving the distribution name. Then the`arg1`

, ...`arg9`

aesthetics (or`args`

as a list column) 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.

The following variables are computed by this stat and made available for
use in aesthetic specifications (`aes()`

) using the `after_stat()`

function or the `after_stat`

argument of `stage()`

:

`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.`.width`

: For intervals, the interval width as a numeric value in`[0, 1]`

. For slabs, the width of the smallest interval containing that value of the slab.`level`

: For intervals, the interval width as an ordered factor. For slabs, the level of the smallest interval containing that value of the slab.`pdf`

: For slabs, the probability density function (PDF). If`options("ggdist.experimental.slab_data_in_intervals")`

is`TRUE`

: For intervals, the PDF at the point summary; intervals also have`pdf_min`

and`pdf_max`

for the PDF at the lower and upper ends of the interval.`cdf`

: For slabs, the cumulative distribution function. If`options("ggdist.experimental.slab_data_in_intervals")`

is`TRUE`

: For intervals, the CDF at the point summary; intervals also have`cdf_min`

and`cdf_max`

for the CDF at the lower and upper ends of the interval.

The slab+interval `stat`

s and `geom`

s have a wide variety of aesthetics that control
the appearance of their three sub-geometries: the **slab**, the **point**, and
the **interval**.

These `stat`

s support the following aesthetics:

`x`

: x position of the geometry (when orientation =`"vertical"`

); or sample data to be summarized (when`orientation = "horizontal"`

with sample data).`y`

: y position of the geometry (when orientation =`"horizontal"`

); or sample data to be summarized (when`orientation = "vertical"`

with sample data).`xdist`

: When using analytical distributions, distribution to map on the x axis: a distributional object (e.g.`dist_normal()`

) or a`posterior::rvar()`

object.`ydist`

: When using analytical distributions, distribution to map on the y axis: a distributional object (e.g.`dist_normal()`

) or a`posterior::rvar()`

object.`dist`

: When using analytical distributions, a name of a distribution (e.g.`"norm"`

), a distributional object (e.g.`dist_normal()`

), or a`posterior::rvar()`

object. See**Details**.`args`

: Distribution arguments (`args`

or`arg1`

, ...`arg9`

). See**Details**.

In addition, in their default configuration (paired with `geom_interval()`

)
the following aesthetics are supported by the underlying geom:

**Interval-specific aesthetics**

`xmin`

: Left end of the interval sub-geometry (if`orientation = "horizontal"`

).`xmax`

: Right end of the interval sub-geometry (if`orientation = "horizontal"`

).`ymin`

: Lower end of the interval sub-geometry (if`orientation = "vertical"`

).`ymax`

: Upper end of the interval sub-geometry (if`orientation = "vertical"`

).

**Color aesthetics**

`colour`

: (or`color`

) The color of the**interval**and**point**sub-geometries. Use the`slab_color`

,`interval_color`

, or`point_color`

aesthetics (below) to set sub-geometry colors separately.`fill`

: The fill color of the**slab**and**point**sub-geometries. Use the`slab_fill`

or`point_fill`

aesthetics (below) to set sub-geometry colors separately.`alpha`

: The opacity of the**slab**,**interval**, and**point**sub-geometries. Use the`slab_alpha`

,`interval_alpha`

, or`point_alpha`

aesthetics (below) to set sub-geometry colors separately.`colour_ramp`

: (or`color_ramp`

) A secondary scale that modifies the`color`

scale to "ramp" to another color. See`scale_colour_ramp()`

for examples.`fill_ramp`

: A secondary scale that modifies the`fill`

scale to "ramp" to another color. See`scale_fill_ramp()`

for examples.

**Line aesthetics**

`linewidth`

: Width of the line used to draw the**interval**(except with`geom_slab()`

: then it is the width of the**slab**). With composite geometries including an interval and slab, use`slab_linewidth`

to set the line width of the**slab**(see below). For**interval**, raw`linewidth`

values are transformed according to the`interval_size_domain`

and`interval_size_range`

parameters of the`geom`

(see above).`size`

: Determines the size of the**point**. If`linewidth`

is not provided,`size`

will also determines the width of the line used to draw the**interval**(this allows line width and point size to be modified together by setting only`size`

and not`linewidth`

). Raw`size`

values are transformed according to the`interval_size_domain`

,`interval_size_range`

, and`fatten_point`

parameters of the`geom`

(see above). Use the`point_size`

aesthetic (below) to set sub-geometry size directly without applying the effects of`interval_size_domain`

,`interval_size_range`

, and`fatten_point`

.`stroke`

: Width of the outline around the**point**sub-geometry.`linetype`

: Type of line (e.g.,`"solid"`

,`"dashed"`

, etc) used to draw the**interval**and the outline of the**slab**(if it is visible). Use the`slab_linetype`

or`interval_linetype`

aesthetics (below) to set sub-geometry line types separately.

**Interval-specific color/line override aesthetics**

`interval_colour`

: (or`interval_color`

) Override for`colour`

/`color`

: the color of the interval.`interval_alpha`

: Override for`alpha`

: the opacity of the interval.`interval_linetype`

: Override for`linetype`

: the line type of the interval.

**Deprecated aesthetics**

`interval_size`

: Use`interval_linewidth`

.

**Other aesthetics** (these work as in standard `geom`

s)

`width`

`height`

`group`

See examples of some of these aesthetics in action in `vignette("slabinterval")`

.
Learn more about the sub-geom override aesthetics (like `interval_color`

) in the
scales documentation. Learn more about basic ggplot aesthetics in
`vignette("ggplot2-specs")`

.

See `geom_interval()`

for the geom underlying this stat.
See `stat_slabinterval()`

for the stat this shortcut is based on.

Other slabinterval stats:
`stat_ccdfinterval()`

,
`stat_cdfinterval()`

,
`stat_eye()`

,
`stat_gradientinterval()`

,
`stat_halfeye()`

,
`stat_histinterval()`

,
`stat_pointinterval()`

,
`stat_slab()`

```
library(dplyr)
library(ggplot2)
library(distributional)
theme_set(theme_ggdist())
# ON SAMPLE DATA
set.seed(1234)
df = data.frame(
group = c("a", "b", "c"),
value = rnorm(1500, mean = c(5, 7, 9), sd = c(1, 1.5, 1))
)
df %>%
ggplot(aes(x = value, y = group)) +
stat_interval() +
scale_color_brewer()
# ON ANALYTICAL DISTRIBUTIONS
dist_df = data.frame(
group = c("a", "b", "c"),
mean = c( 5, 7, 8),
sd = c( 1, 1.5, 1)
)
# Vectorized distribution types, like distributional::dist_normal()
# and posterior::rvar(), can be used with the `xdist` / `ydist` aesthetics
dist_df %>%
ggplot(aes(y = group, xdist = dist_normal(mean, sd))) +
stat_interval() +
scale_color_brewer()
```