`R/smooth.R`

`smooth_density.Rd`

Smooths `x`

values using a density estimator, returning new `x`

of the same
length. Can be used with a dotplot (e.g. `geom_dots`

`(smooth = ...)`

) to create
"density dotplots".
Supports automatic partial function application.

```
smooth_bounded(
x,
density = "bounded",
bounds = c(NA, NA),
bounder = "cooke",
trim = FALSE,
...
)
smooth_unbounded(x, density = "unbounded", trim = FALSE, ...)
```

- x
a numeric vector

- density
Density estimator to use for smoothing. One of:

A function which takes a numeric vector and returns a list with elements

`x`

(giving grid points for the density estimator) and`y`

(the corresponding densities). ggdist provides a family of functions following this format, including`density_unbounded()`

and`density_bounded()`

.A string giving the suffix of a function name that starts with

`"density_"`

; e.g.`"bounded"`

for`[density_bounded()]`

.

- bounds
length-2 vector of min and max bounds. If a bound is

`NA`

, then that bound is estimated from the data using the method specified by`bounder`

.- bounder
Method to use to find missing (

`NA`

)`bounds`

. A function that takes a numeric vector of values and returns a length-2 vector of the estimated lower and upper bound of the distribution. Can also be a string giving the suffix of the name of such a function that starts with`"bounder_"`

. Useful values include:`"cdf"`

: Use the CDF of the the minimum and maximum order statistics of the sample to estimate the bounds. See`bounder_cdf()`

.`"cooke"`

: Use the method from Cooke (1979); i.e. method 2.3 from Loh (1984). See`bounder_cooke()`

.`"range"`

: Use the range of`x`

(i.e the`min`

or`max`

). See`bounder_range()`

.

- trim
Should the density estimate be trimmed to the bounds of the data?

- ...
Arguments passed to the density estimator specified by

`density`

.

A numeric vector of `length(x)`

, where each entry is a smoothed version of
the corresponding entry in `x`

.

If `x`

is missing, returns a partial application of itself. See automatic-partial-functions.

Applies a kernel density estimator (KDE) to `x`

, then uses weighted quantiles
of the KDE to generate a new set of `x`

values with smoothed values. Plotted
using a dotplot (e.g. `geom_dots(smooth = "bounded")`

or
`geom_dots(smooth = smooth_bounded(...)`

), these values create a variation on
a "density dotplot" (Zvinca 2018).

Such plots are recommended **only** in very
large sample sizes where precise positions of individual values are not
particularly meaningful. In small samples, normal dotplots should generally
be used.

Two variants are supplied by default:

`smooth_bounded()`

, which uses`density_bounded()`

. Passes the`bounds`

arguments to the estimator.`smooth_unbounded()`

, which uses`density_unbounded()`

.

It is generally recommended to pick the smooth based on the known bounds of
your data, e.g. by using `smooth_bounded()`

with the `bounds`

parameter if
there are finite bounds, or `smooth_unbounded()`

if both bounds are infinite.

Zvinca, Daniel. "In the pursuit of diversity in data visualization. Jittering data to access details." https://www.linkedin.com/pulse/pursuit-diversity-data-visualization-jittering-access-daniel-zvinca/.

Other dotplot smooths:
`smooth_discrete()`

,
`smooth_none()`

```
library(ggplot2)
set.seed(1234)
x = rnorm(1000)
# basic dotplot is noisy
ggplot(data.frame(x), aes(x)) +
geom_dots()
# density dotplot is smoother, but does move points (most noticeable
# in areas of low density)
ggplot(data.frame(x), aes(x)) +
geom_dots(smooth = "unbounded")
# you can adjust the kernel and bandwidth...
ggplot(data.frame(x), aes(x)) +
geom_dots(smooth = smooth_unbounded(kernel = "triangular", adjust = 0.5))
# for bounded data, you should use the bounded smoother
x_beta = rbeta(1000, 0.5, 0.5)
ggplot(data.frame(x_beta), aes(x_beta)) +
geom_dots(smooth = smooth_bounded(bounds = c(0, 1)))
```