This vignette describes the dots+interval geoms and stats in
`ggdist`

. This is a flexible sub-family of stats and geoms
designed to make plotting dotplots straightforward. In particular, it
supports a selection of useful layouts (including the classic Wilkinson
layout, a weave layout, and a beeswarm layout) and can automatically
select the dot size so that the dotplot stays within the bounds of the
plot.

The following libraries are required to run this vignette:

`geom_dotsinterval()`

The `dotsinterval`

family of geoms and stats is a
sub-family of slabinterval (see `vignette("slabinterval")`

),
where the “slab” is a collection of dots forming a dotplot and the
interval is a summary point (e.g., mean, median, mode) with an arbitrary
number of intervals.

The base `geom_dotsinterval()`

uses a variety of custom
aesthetics to create the composite geometry:

Depending on whether you want a horizontal or vertical orientation,
you can provide `ymin`

and `ymax`

instead of
`xmin`

and `xmax`

. By default, some aesthetics
(e.g., `fill`

, `color`

, `size`

,
`alpha`

) set properties of multiple sub-geometries at once.
For example, the `color`

aesthetic by default sets both the
color of the point and the interval, but can also be overridden by
`point_color`

or `interval_color`

to set the color
of each sub-geometry separately.

Due to its relationship to the `geom_slabinterval()`

family, aesthetics specific to the “dots” sub-geometry are referred to
with the prefix `slab_`

. When using the standalone
`geom_dots()`

geometry, it is not necessary to use these
custom aesthetics:

`geom_dotsinterval()`

is often most useful when paired
with `stat_dotsinterval()`

, which will automatically
calculate points and intervals and map these onto endpoints of the
interval sub-geometry.

`stat_dotsinterval()`

and `stat_dots()`

can be
used on two types of data, depending on what aesthetic mappings you
provide:

**Sample data**; e.g. draws from a data distribution, bootstrap distribution, Bayesian posterior distribution (or any other distribution, really). To use the stats on sample data, map sample values onto the`x`

or`y`

aesthetic.**Distribution objects and analytical distributions**. To use the stats on this type of data, you must use the`xdist`

, or`ydist`

aesthetics, which take distributional objects,`posterior::rvar()`

objects, or distribution names (e.g.`"norm"`

, which refers to the Normal distribution provided by the`dnorm/pnorm/qnorm`

functions). When used on analytical distributions (e.g.`distributional::dist_normal()`

), the`quantiles`

argument determines the number of quantiles used (and therefore the number of dots shown); the default is`100`

.

All `dotsinterval`

geoms can be plotted horizontally or
vertically. Depending on how aesthetics are mapped, they will attempt to
automatically determine the orientation; if this does not produce the
correct result, the orientation can be overridden by setting
`orientation = "horizontal"`

or
`orientation = "vertical"`

.

Size and layout of dots in the dotplot are controlled by four
parameters: `scale`

, `binwidth`

,
`dotsize`

, and `stackratio`

.

`scale`

: If`binwidth`

is not set (is`NA`

), then the`binwidth`

is determined automatically so that the height of the highest stack of dots is less than`scale`

. The default value of`scale`

, 0.9, ensures there is a small gap between dotplots when multiple dotplots are drawn.`binwidth`

: The width of the bins used to lay out the dots:`NA`

(default): Use`scale`

to determine bin width.- A single numeric or
`unit()`

: the exact bin width to use. If it is`numeric`

, the bin width is expressed in data units; use`unit()`

to specify the width in terms of screen coordinates (e.g.`unit(0.1, "npc")`

would make the bin width 0.1*normalized parent coordinates*, which would be 10% of the plot width.) - A 2-vector of numerics or
`unit()`

s giving an acceptable minimum and maximum width. The automatic bin width algorithm will attempt to find the largest bin width between these two values that also keeps the tallest stack of dots shorter than`scale`

.

`dotsize`

: The size of the dots as a percentage of`binwidth`

. The default value is`1.07`

rather than`1`

. This value was chosen largely by trial and error, to find a value that gives nice-looking layouts with circular dots on continuous distributions, accounting for the fact that a slight overlap of dots tends to give a nicer apparent visual distance between adjacent stacks than the precise value of`1`

.`stackratio`

: The distance between the centers of dots in a stack as a proportion of the height of each dot.`stackratio = 1`

, the default, mean dots will just touch;`stackratio < 1`

means dots will overlap each other, and`stackratio > 1`

means dots will have gaps between them.

The `side`

aesthetic allows you to adjust the positioning
and direction of the dots:

`"top"`

,`"right"`

, or`"topright"`

: draw the dots on the top or on the right, depending on`orientation`

`"bottom"`

,`"left"`

, or`"bottomleft"`

: draw the dots on the bottom or on the left, depending on`orientation`

`"topleft"`

: draw the dots on top or on the left, depending on`orientation`

`"bottomright"`

: draw the dots on the bottom or on the right, depending on`orientation`

`"both"`

: draw the dots mirrored, as in a “beeswarm” plot.

When `orientation = "horizontal"`

, this yields:

```
set.seed(1234)
x = rnorm(100)
side_plot = function(...) {
expand.grid(
x = x,
side = c("topright", "both", "bottomleft"),
stringsAsFactors = FALSE
) %>%
ggplot(aes(side = side, ...)) +
geom_dots() +
facet_grid(~ side, labeller = "label_both") +
labs(x = NULL, y = NULL) +
theme(panel.border = element_rect(color = "gray75", fill = NA))
}
side_plot(x = x) +
labs(title = "Horizontal geom_dots() with different values of side") +
scale_y_continuous(breaks = NULL)
```

When `orientation = "vertical"`

, this yields:

The `layout`

parameter allows you to adjust the algorithm
used to place dots:

`"bin"`

(default): places dots on the off-axis at the midpoint of their bins as in the classic Wilkinson dotplot. This maintains the alignment of rows and columns in the dotplot. This layout is slightly different from the classic Wilkinson algorithm in that: (1) it nudges bins slightly to avoid overlapping bins and (2) if the input data are symmetrical it will return a symmetrical layout.`"weave"`

: uses the same basic binning approach of “bin”, but places dots in the off-axis at their actual positions (modulo overlaps, which are nudged out of the way). This maintains the alignment of rows but does not align dots within columns.`"hex"`

: uses the same basic binning approach of “bin”, but alternates placing dots`+binwidth/4`

or`-binwidth/4`

in the off-axis from the bin center. This allows hexagonal packing by setting a`stackratio`

less than`1`

(something like`0.9`

tends to work).- “swarm”: uses the
`"compactswarm"`

layout from`beeswarm::beeswarm()`

. Does not maintain alignment of rows or columns, but can be more compact and neat looking, especially for sample data (as opposed to quantile dotplots of theoretical distributions, which may look better with`"bin"`

,`"weave"`

, or`"hex"`

).

When `side`

is `"top"`

, these layouts look like
this:

```
layout_plot = function(layout, side, ...) {
data.frame(
x = x
) %>%
ggplot(aes(x = x)) +
geom_dots(layout = layout, side = side, stackratio = if (layout == "hex") 0.9 else 1) +
labs(
subtitle = paste0("layout = ", deparse(layout), if (layout == "hex") " with stackratio = 0.9"),
x = NULL,
y = NULL
) +
scale_y_continuous(breaks = NULL) +
theme(panel.border = element_rect(color = "gray75", fill = NA))
}
(layout_plot("bin", side = "top") +
layout_plot("hex", side = "top")) /
(layout_plot("weave", side = "top") +
layout_plot("swarm", side = "top")) +
plot_annotation(title = 'geom_dots() layouts with side = "top"')
```

When `side`

is `"both"`

, these layouts look
like this:

```
(layout_plot("bin", side = "both") +
layout_plot("hex", side = "both")) /
(layout_plot("weave", side = "both") +
layout_plot("swarm", side = "both")) +
plot_annotation(title = 'geom_dots() layouts with side = "both"')
```

Thus, it is possible to create beeswarm plots by using
`geom_dots()`

with `side = "both"`

:

```
set.seed(1234)
abc_df = data.frame(
value = rnorm(300, mean = c(1,2,3), sd = c(1,2,2)),
abc = c("a", "b", "c")
)
abc_df %>%
ggplot(aes(x = abc, y = value)) +
geom_dots(side = "both") +
ggtitle('geom_dots(side = "both")')
```

`side = "both"`

also tends to work well with the
`"hex"`

and `"swarm"`

layouts for more
classic-looking “beeswarm” plots:

```
abc_df %>%
ggplot(aes(x = abc, y = value)) +
geom_dots(side = "both", layout = "hex", stackratio = 0.92) +
ggtitle('geom_dots(side = "both", layout = "hex")')
```

The combination of `binwidth = unit(1.5, "mm")`

and
`overflow = "compress"`

(see the section on large samples,
below) can be used to set the dot size to a specific size while
guaranteeing the layout stays within the bounds of the geom. This
combination is used by two shortcut geoms, `geom_swarm()`

and
`geom_weave()`

, which use the `"swarm"`

and
`"weave"`

layouts respectively. These also use
`side = "both"`

, and are intended to make it easy to create
good-looking beeswarm plots *without* manually tweaking
settings:

```
set.seed(1234)
swarm_data = data.frame(
y = rnorm(300, c(1,4)),
g = c("a","b")
)
swarm_plot = swarm_data %>%
ggplot(aes(x = g, y = y)) +
geom_swarm(linewidth = 0, alpha = 0.75) +
labs(title = "geom_swarm()")
weave_plot = swarm_data %>%
ggplot(aes(x = g, y = y)) +
geom_weave(linewidth = 0, alpha = 0.75) +
labs(title = "geom_weave()")
swarm_plot + weave_plot
```

`color`

, `fill`

, `shape`

,
and `linewidth`

Aesthetics like `color`

, `fill`

,
`shape`

, and `linewidth`

can be varied over the
dots. For example, we can vary the `fill`

aesthetic to create
two subgroups, and use `position = "dodge"`

to dodge entire
“swarms” at once so the subgroups do not overlap. We’ll also set
`linewidth = 0`

so that the default gray outline is not
drawn:

```
set.seed(12345)
abcc_df = data.frame(
value = rnorm(300, mean = c(1,2,3,4), sd = c(1,2,2,1)),
abc = c("a", "b", "c", "c"),
hi = c("h", "h", "h", "i")
)
abcc_df %>%
ggplot(aes(y = value, x = abc, fill = hi)) +
geom_weave(position = "dodge", linewidth = 0, alpha = 0.75) +
scale_fill_brewer(palette = "Dark2") +
ggtitle(
'geom_weave(position = "dodge")',
'aes(fill = hi, shape = hi)'
)
```

By default, if you assign a discrete variable to `fill`

,
color`,`

shape`, etc it will also be used in the`

group`
aesthetic to determine dot groups, which are laid out separate (and can
be dodged separately, as above).

If you override this behavior by setting `group`

to
`NA`

(or to some other variable you want to group dot layouts
by), `geom_dotsinterval()`

will leave dots in data order
within the layout but allow aesthetics to vary across them.

For example:

```
abcc_df %>%
ggplot(aes(y = value, x = abc, fill = hi, group = NA)) +
geom_dots(linewidth = 0) +
scale_color_brewer(palette = "Dark2") +
ggtitle(
'geom_dots()',
'aes(fill = hi, group = NA)'
)
```

By default, dot positions within bins for the `"bin"`

layout are determined by their data values (e.g. by the `y`

values in the above chart). You can override this by passing a variable
to the `order`

aesthetic, which will set the sort order
within bins. This can be used to create “stacked” dotplots by setting
`order`

to a discrete variable:

Continuous variables can also be varied within groups. Since
continuous variables will not automatically set the `group`

aesthetic, we can simply assign them to the desired aesthetic we want to
vary:

On very large samples, the dots may become smaller than desired. To
avoid this, you can set a desired dot size / bin width using the
`binwidth`

argument. To set a specific bin width, pass a
1-element vector; to set a minimum bin width, pass a 2-element vector,
where the first element is the min and the second the max. The bin width
can be in data units (if `numeric`

) or in plotting units
(using `grid::unit()`

).

For example, we could set the minimum dot size to
`unit(1.5, "mm")`

, which is the default size of points in
`ggplot2::geom_point()`

. We’ll also set
`overflow = "compress"`

, which allows dots to overlap if
necessary to maintain the specified dot size (rather than having the
tallest stacks of dots leave the top of the screen):

The dotplot above on a sample of size 2000 is quite noisy. When applied to large samples where you do not care too much about individual dot positions, you may want to apply some smoothing to make the layout more appealing.

`geom_dots()`

supports a handful of *smoothers*
which can be applied using the `smooth =`

parameter. These
all correspond to functions that start with `smooth_`

, like
`smooth_bounded()`

, `smooth_unbounded()`

, and
`smooth_discrete()`

, and can be applied either by passing the
suffix as a string (e.g. `smooth = "bounded"`

) or by passing
the function itself, to set specific options on it (e.g.
`smooth = smooth_bonuded(adjust = 0.5)`

). For continuous
distributions with unbounded support, `smooth_unbounded()`

is
a good choice; it applies a kernel density estimator the assumes
infinite bounds (see `density_unbounded()`

):

```
ggplot() +
geom_dots(aes(x), smooth = "unbounded") +
labs(
title = 'geom_dots() with 2000 dots',
subtitle = 'smooth = "unbounded"',
x = NULL
) +
scale_y_continuous(breaks = NULL)
```

Note that dot positions in the resulting plot will no longer be as
accurate as before. With a large sample this may be an acceptable
compromise. With a small sample, I **do not** recommend
using this technique.

On bounded distributions, you should use
`smooth_bounded()`

, providing the bounds of the distribution.
Otherwise, the dotplot will be smoothed incorrectly. For example, on a
Beta(0.5, 0.5) distribution, which is bounded between 0 and 1, we should
use `smooth = smooth_bounded(bounds = c(0, 1))`

:

```
set.seed(1234)
x = rbeta(2000, 0.5, 0.5)
ggplot(data.frame(x), aes(x)) +
geom_dots(aes(y = "bounded"), smooth = smooth_bounded(bounds = c(0, 1))) +
geom_dots(aes(y = "unbounded"), smooth = "unbounded") +
geom_vline(xintercept = c(0, 1), alpha = 0.25) +
scale_x_continuous(breaks = c(0, 0.5, 1)) +
labs(
title = "geom_dots(smooth = ...) on x ~ Beta(0.5, 0.5)",
y = "smooth ="
)
```

Notice how `smooth = "unbounded"`

incorrectly smooths data
points to be outside the range of the data when the data are
bounded.

The dots family includes a variety of features to make visualizing discrete and categorical distributions easier. Dotplot smoothing can be particularly useful in for these distributions, particularly when bin counts are very high. For example, these distributions are hard to visualize under the default settings, because the dots become very small:

```
set.seed(1234)
abcd_df = data.frame(
x = sample(c("a", "b", "c", "d"), 1000, replace = TRUE, prob = c(0.27, 0.6, 0.03, 0.005)),
g = c("a","b")
)
abcd_df %>%
ggplot(aes(x = x)) +
geom_dots() +
scale_y_continuous(breaks = NULL) +
labs(
title = "geom_dots()",
subtitle = "on a large discrete sample"
)
```

The automatic bin width algorithm selects a dot size that is very small in order to ensure the tallest bin fits in the plot, but this means the dots are hard to see.

Using the `smooth_discrete()`

smoother, we can spread the
dots in each bin out into rectangular shapes:

```
abcd_df %>%
ggplot(aes(x = x)) +
geom_dots(smooth = "discrete") +
scale_y_continuous(breaks = NULL) +
labs(
title = 'geom_dots(smooth = "discrete")',
subtitle = "on a large discrete sample"
)
```

More regular bar-like shapes can be achieved by using
`layout = "bar"`

, so long as you override the default
`ggplot2`

behavior of grouping data by all discrete
variables. This allows the layout to be calculated taking all groups
into account:

```
abcd_df %>%
ggplot(aes(x = x, fill = g, order = g)) +
geom_dots(layout = "bar", group = NA, color = NA) +
scale_y_continuous(breaks = NULL) +
labs(
title = 'geom_dots(aes(fill = g), layout = "bar", group = NA)',
subtitle = "on a large discrete sample"
)
```

`smooth_discrete()`

applies a kernel density smoother
whose default bandwidth is less than the distances between bins. We can
use the `kernel`

argument (passed to
`density_bounded()`

; the same kernels from
`stats::density()`

are available) to change the shape of the
bins.

For example, using the `"epanechnikov"`

(parabolic) kernel
along with `side = "both"`

, we can create lozenge-like
shapes. We’ll abbreviate the kernel `"ep"`

to save typing out
`"epanechnikov"`

(partial matching is allowed):

Like the `stat_slabinterval()`

family,
`stat_dotsinterval()`

and `stat_dots()`

support
using both sample data (via `x`

and `y`

aesthetics) or analytical distributions (via the `xdist`

and
`ydist`

aesthetics). For analytical distributions, these
stats accept specifications for distributions in one of two ways:

**Using distribution names as character vectors**: this format uses aesthetics as follows:`xdist`

,`ydist`

, or`dist`

: the name of the distribution, following R’s naming scheme. This is a string which should have`"p"`

,`"q"`

, and`"d"`

functions defined for it: e.g., “norm” is a valid distribution name because the`pnorm()`

,`qnorm()`

, and`dnorm()`

functions define the CDF, quantile function, and density function of the Normal distribution.`args`

or`arg1`

, …`arg9`

: arguments for the distribution. If you use`args`

, it should be a list column where each element is a list containing arguments for the distribution functions; alternatively, you can pass the arguments directly using`arg1`

, …`arg9`

.

**Using distribution vectors from the distributional package or**: this format uses aesthetics as follows:`posterior::rvar()`

objects`xdist`

,`ydist`

, or`dist`

: a distribution vector or`posterior::rvar()`

produced by functions such as`distributional::dist_normal()`

,`distributional::dist_beta()`

,`posterior::rvar_rng()`

, etc.

For example, here are a variety of distributions:

```
dist_df = tibble(
dist = c(dist_normal(1,0.25), dist_beta(3,3), dist_gamma(5,5)),
dist_name = format(dist)
)
dist_df %>%
ggplot(aes(y = dist_name, xdist = dist)) +
stat_dotsinterval() +
ggtitle(
"stat_dotsinterval()",
"aes(y = dist_name, xdist = dist)"
)
```

Analytical distributions are shown by default using 100 quantiles,
sometimes referred to as a *quantile dotplot*, which can help
people make better decisions under uncertainty (Kay 2016, Fernandes 2018). This
can be changed using the `quantiles`

argument. For example,
we can plot the same distributions again using 1000 quantiles. We’ll
also make use of `point_interval`

to plot the mode and
highest-density continuous intervals (instead of the default median and
quantile intervals; see `point_interval()`

).

We’ll also highlight some intervals by coloring the dots. Like with
the `stat_slabinterval()`

family, computed variables from the
interval sub-geometry (`level`

and `.width`

) are
available to the dots/slab sub-geometry, and correspond to the smallest
interval containing that dot. We can use these to color dots according
to the interval containing them (we’ll also use the `"weave"`

layout since it maintains x positions better than the `"bin"`

layout):

```
dist_df %>%
ggplot(aes(y = dist_name, xdist = dist, slab_fill = after_stat(level))) +
stat_dotsinterval(quantiles = 1000, point_interval = mode_hdci, layout = "weave", slab_color = NA) +
scale_color_manual(values = scales::brewer_pal()(3)[-1], aesthetics = "slab_fill") +
ggtitle(
"stat_dotsinterval(quantiles = 1000, point_interval = mode_hdci)",
"aes(y = dist_name, xdist = dist, slab_fill = after_stat(level))"
)
```

When summarizing sample distributions with
`stat_dots()`

/`stat_dotsinterval()`

(e.g. samples
from Bayesian posteriors), one can also use the `quantiles`

argument, though it is not on by default.

While varying discrete aesthetics works similarly with
`stat_dotsinterval()`

/`stat_dots()`

as it does
with `geom_dotsinterval()`

/`geom_dots()`

, varying
continuous aesthetics within dot groups typically requires mapping the
continuous aesthetic *after* the stats are computed. This is
because the stat (at least for analytical distributions) must first
generate the quantiles before properties of those quantiles can be
mapped to aesthetics.

Thus, because it relies upon generated variables from the stat, you
can use the `after_stat()`

or `stage()`

functions
from `ggplot2`

to map those variables. For example:

```
dist_df %>%
ggplot(aes(y = dist_name, xdist = dist, slab_color = after_stat(x))) +
stat_dotsinterval(slab_shape = 19, quantiles = 500) +
scale_color_distiller(aesthetics = "slab_color", guide = "colorbar2") +
ggtitle(
"stat_dotsinterval(slab_shape = 19, quantiles = 500)",
'aes(slab_color = after_stat(x)) +\nscale_color_distiller(aesthetics = "slab_color", guide = "colorbar2")'
)
```

This example also demonstrates the use of sub-geometry scales: the
`slab_`

-prefixed aesthetics `slab_color`

and
`slab_shape`

must be used to target the color and shape of
the slab (“slab” here refers to the stack of dots) when using
`geom_dotsinterval()`

and `stat_dotsinterval()`

to
disambiguate between the point/interval and the dot stack. When using
`stat_dots()`

/`geom_dots()`

this is not
necessary.

Also note the use of `scale_color_distiller()`

, a base
ggplot2 color scale, with the `slab_color`

aesthetic by
setting the `aesthetics`

and `guide`

properties
(the latter is necessary because the default
`guide = "colorbar"`

will not work with non-standard color
aesthetics).

Another potentially useful application of post-stat aesthetic
computation is to apply thresholds on a dotplot, coloring points on one
side of a line differently. However, the default dotplot layout,
`"bin"`

, can cause dots to be on the wrong side of a cutoff
when coloring dots within dotplots. Thus it can be useful when plotting
thresholds to use the `"weave"`

or `"swarm"`

layouts, which tend to position dots closer to their true `x`

positions, rather than at bin centers:

```
ab_df = tibble(
ab = c("a", "b"),
mean = c(5, 7),
sd = c(1, 1.5)
)
ab_df %>%
ggplot(aes(y = ab, xdist = dist_normal(mean, sd), fill = after_stat(x < 6))) +
stat_dots(position = "dodge", color = NA, layout = "weave") +
labs(
title = 'stat_dots(layout = "weave")',
subtitle = "aes(fill = after_stat(x < 6))"
) +
geom_vline(xintercept = 6, alpha = 0.25) +
scale_x_continuous(breaks = 2:10)
```

Sometimes you may want to include multiple different types of slabs
in the same plot in order to take advantage of the features each slab
type provides. For example, people often combine densities with dotplots
to show the underlying datapoints that go into a density estimate,
creating so-called *rain cloud* plots.

To use multiple slab geometries together, you can use the
`side`

parameter to change which side of the interval a slab
is drawn on and set the `scale`

parameter to something around
`0.5`

(by default it is `0.9`

) so that the two
slabs do not overlap. We’ll also scale the halfeye slab thickness by
`n`

(the number of observations in each group) so that the
area of each slab represents sample size (and looks similar to the total
area of its corresponding dotplot).

We’ll use a subsample of of the data to show how it might look on a reasonably-sized dataset.

```
set.seed(12345) # for reproducibility
data.frame(
abc = c("a", "b", "b", "c"),
value = rnorm(200, c(1, 8, 8, 3), c(1, 1.5, 1.5, 1))
) %>%
ggplot(aes(y = abc, x = value, fill = abc)) +
stat_slab(aes(thickness = after_stat(pdf*n)), scale = 0.7) +
stat_dotsinterval(side = "bottom", scale = 0.7, slab_linewidth = NA) +
scale_fill_brewer(palette = "Set2") +
ggtitle(paste0(
'stat_slab(aes(thickness = after_stat(pdf*n)), scale = 0.7) +\n',
'stat_dotsinterval(side = "bottom", scale = 0.7, slab_linewidth = NA)'
),
'aes(fill = abc)'
)
```

To demonstrate another useful plot type, the *logit dotplot*
(courtesy Ladislas
Nalborczyk), we’ll fit a logistic regression to some data on the sex
and body mass of Gentoo penguins.

First, we’ll demo varying the `side`

aesthetic to create
two dotplots that are “facing” each other:
`scale_side_mirrored()`

will set the `side`

aesthetic to `"top"`

or `"bottom"`

if two
categories are assigned to `side`

“. We also adjust the
`scale`

so that the dots don’t overlap:

```
gentoo = penguins %>%
filter(species == "Gentoo", !is.na(sex))
gentoo %>%
ggplot(aes(x = body_mass_g, y = sex, side = sex)) +
geom_dots(scale = 0.5) +
scale_side_mirrored(guide = "none") +
ggtitle(
"geom_dots(scale = 0.5)",
'aes(side = sex) + scale_side_mirrored()'
)
```

This can also be accomplished by setting side directly and omitting
`scale_side_mirrored()`

; e.g. via
`aes(side = ifelse(sex == "male", "bottom", "top"))`

.

Now we fit a logistic regression predicting sex based on body mass:

```
##
## Call: glm(formula = sex == "male" ~ body_mass_g, family = binomial,
## data = gentoo)
##
## Coefficients:
## (Intercept) body_mass_g
## -55.03337 0.01089
##
## Degrees of Freedom: 118 Total (i.e. Null); 117 Residual
## Null Deviance: 164.9
## Residual Deviance: 45.1 AIC: 49.1
```

Then we can overlay a fit line as a `stat_lineribbon()`

(see `vignette("lineribbon")`

) on top of the mirrored
dotplots to create a *logit dotplot*:

```
# construct a prediction grid for the fit line
prediction_grid = with(gentoo,
data.frame(body_mass_g = seq(min(body_mass_g), max(body_mass_g), length.out = 100))
)
prediction_grid %>%
bind_cols(predict(m, ., se.fit = TRUE)) %>%
mutate(
# distribution describing uncertainty in log odds
log_odds = dist_normal(fit, se.fit),
# inverse-logit transform the log odds to get
# distribution describing uncertainty in Pr(sex == "male")
p_male = dist_transformed(log_odds, plogis, qlogis)
) %>%
ggplot(aes(x = body_mass_g)) +
geom_dots(
aes(y = as.numeric(sex == "male"), side = sex),
scale = 0.4,
data = gentoo
) +
stat_lineribbon(
aes(ydist = p_male), alpha = 1/4, fill = "#08306b"
) +
scale_side_mirrored(guide = "none") +
coord_cartesian(ylim = c(0, 1)) +
labs(
title = "logit dotplot: stat_dots() with stat_lineribbon()",
subtitle = 'aes(side = sex) + scale_side_mirrored()',
x = "Body mass (g) of Gentoo penguins",
y = "Pr(sex = male)"
)
```