`R/smooth.R`

`smooth_discrete.Rd`

**Note:** Better-looking bar dotplots are typically easier to achieve using
`layout = "bar"`

with the `geom_dotsinterval()`

family instead of
`smooth = "bar"`

or `smooth = "discrete"`

.

Smooths `x`

values where `x`

is presumed to be discrete, returning a new `x`

of the same length. Both `smooth_discrete()`

and `smooth_bar()`

use the
`resolution()`

of the data to apply smoothing around unique values in the
dataset; `smooth_discrete()`

uses a kernel density estimator and `smooth_bar()`

places values in an evenly-spaced grid. Can be used with a dotplot
(e.g. `geom_dots`

`(smooth = ...)`

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

```
smooth_discrete(
x,
kernel = c("rectangular", "gaussian", "epanechnikov", "triangular", "biweight",
"cosine", "optcosine"),
width = 0.7,
...
)
smooth_bar(x, width = 0.7, ...)
```

- x
a numeric vector

- kernel
string: the smoothing kernel to be used. This must partially match one of

`"gaussian"`

,`"rectangular"`

,`"triangular"`

,`"epanechnikov"`

,`"biweight"`

,`"cosine"`

, or`"optcosine"`

. See`stats::density()`

.- width
approximate width of the bars as a fraction of data

`resolution()`

.- ...
additional parameters;

`smooth_discrete()`

passes these to`smooth_unbounded()`

and thereby to`density_unbounded()`

;`smooth_bar()`

ignores them.

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.

`smooth_discrete()`

applies a kernel density estimator (default: rectangular)
to `x`

. It automatically sets the bandwidth to be such that the kernel's
width (for each kernel type) is approximately `width`

times the `resolution()`

of the data. This means it essentially creates smoothed bins around each
unique value. It calls down to `smooth_unbounded()`

.

`smooth_bar()`

generates an evenly-spaced grid of values spanning `+/- width/2`

around each unique value in `x`

.

Other dotplot smooths:
`smooth_density`

,
`smooth_none()`

```
library(ggplot2)
set.seed(1234)
x = rpois(1000, 2)
# automatic binwidth in basic dotplot on large counts in discrete
# distributions is very small
ggplot(data.frame(x), aes(x)) +
geom_dots()
# NOTE: It is now recommended to use layout = "bar" instead of
# smooth = "discrete" or smooth = "bar"; the latter are retained because
# they can sometimes be useful in combination with other layouts for
# more specialized (but finicky) applications.
ggplot(data.frame(x), aes(x)) +
geom_dots(layout = "bar")
# smooth_discrete() constructs wider bins of dots
ggplot(data.frame(x), aes(x)) +
geom_dots(smooth = "discrete")
# smooth_bar() is an alternative approach to rectangular layouts
ggplot(data.frame(x), aes(x)) +
geom_dots(smooth = "bar")
# adjust the shape by changing the kernel or the width. epanechnikov
# works well with side = "both"
ggplot(data.frame(x), aes(x)) +
geom_dots(smooth = smooth_discrete(kernel = "epanechnikov", width = 0.8), side = "both")
```