# Contours of a 2D density estimate

Source:`R/geom-density2d.R`

, `R/stat-density-2d.R`

`geom_density_2d.Rd`

Perform a 2D kernel density estimation using `MASS::kde2d()`

and
display the results with contours. This can be useful for dealing with
overplotting. This is a 2D version of `geom_density()`

. `geom_density_2d()`

draws contour lines, and `geom_density_2d_filled()`

draws filled contour
bands.

## Usage

```
geom_density_2d(
mapping = NULL,
data = NULL,
stat = "density_2d",
position = "identity",
...,
contour_var = "density",
lineend = "butt",
linejoin = "round",
linemitre = 10,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
geom_density_2d_filled(
mapping = NULL,
data = NULL,
stat = "density_2d_filled",
position = "identity",
...,
contour_var = "density",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
stat_density_2d(
mapping = NULL,
data = NULL,
geom = "density_2d",
position = "identity",
...,
contour = TRUE,
contour_var = "density",
n = 100,
h = NULL,
adjust = c(1, 1),
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
stat_density_2d_filled(
mapping = NULL,
data = NULL,
geom = "density_2d_filled",
position = "identity",
...,
contour = TRUE,
contour_var = "density",
n = 100,
h = NULL,
adjust = c(1, 1),
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
```

## Arguments

- 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)`

).- position
Position adjustment, either as a string naming the adjustment (e.g.

`"jitter"`

to use`position_jitter`

), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.- ...
Arguments passed on to

`geom_contour`

`binwidth`

The width of the contour bins. Overridden by

`bins`

.`bins`

Number of contour bins. Overridden by

`breaks`

.`breaks`

One of:

Numeric vector to set the contour breaks

A function that takes the range of the data and binwidth as input and returns breaks as output. A function can be created from a formula (e.g. ~ fullseq(.x, .y)).

Overrides

`binwidth`

and`bins`

. By default, this is a vector of length ten with`pretty()`

breaks.

- contour_var
Character string identifying the variable to contour by. Can be one of

`"density"`

,`"ndensity"`

, or`"count"`

. See the section on computed variables for details.- lineend
Line end style (round, butt, square).

- linejoin
Line join style (round, mitre, bevel).

- linemitre
Line mitre limit (number greater than 1).

- na.rm
If

`FALSE`

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

, missing values are silently removed.- show.legend
logical. Should this layer be included in the legends?

`NA`

, the default, includes if any aesthetics are mapped.`FALSE`

never includes, and`TRUE`

always includes. It can also be a named logical vector to finely select the aesthetics to display.- 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()`

.- geom, stat
Use to override the default connection between

`geom_density_2d()`

and`stat_density_2d()`

.- contour
If

`TRUE`

, contour the results of the 2d density estimation.- n
Number of grid points in each direction.

- h
Bandwidth (vector of length two). If

`NULL`

, estimated using`MASS::bandwidth.nrd()`

.- adjust
A multiplicative bandwidth adjustment to be used if 'h' is 'NULL'. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator. For example,

`adjust = 1/2`

means use half of the default bandwidth.

## Aesthetics

`geom_density_2d()`

understands the following aesthetics (required aesthetics are in bold):

Learn more about setting these aesthetics in `vignette("ggplot2-specs")`

.

`geom_density_2d_filled()`

understands the following aesthetics (required aesthetics are in bold):

Learn more about setting these aesthetics in `vignette("ggplot2-specs")`

.

## Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation. `stat_density_2d()`

and `stat_density_2d_filled()`

compute different variables depending on whether contouring is turned on or off. With contouring off (`contour = FALSE`

), both stats behave the same, and the following variables are provided:

`after_stat(density)`

The density estimate.`after_stat(ndensity)`

Density estimate, scaled to a maximum of 1.`after_stat(count)`

Density estimate * number of observations in group.`after_stat(n)`

Number of observations in each group.

With contouring on (`contour = TRUE`

), either `stat_contour()`

or
`stat_contour_filled()`

(for contour lines or contour bands,
respectively) is run after the density estimate has been obtained,
and the computed variables are determined by these stats.
Contours are calculated for one of the three types of density estimates
obtained before contouring, `density`

, `ndensity`

, and `count`

. Which
of those should be used is determined by the `contour_var`

parameter.

## Dropped variables

`z`

After density estimation, the z values of individual data points are no longer available.

If contouring is enabled, then similarly `density`

, `ndensity`

, and `count`

are no longer available after the contouring pass.

## See also

`geom_contour()`

, `geom_contour_filled()`

for information about
how contours are drawn; `geom_bin2d()`

for another way of dealing with
overplotting.

## Examples

```
m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
geom_point() +
xlim(0.5, 6) +
ylim(40, 110)
# contour lines
m + geom_density_2d()
# \donttest{
# contour bands
m + geom_density_2d_filled(alpha = 0.5)
# contour bands and contour lines
m + geom_density_2d_filled(alpha = 0.5) +
geom_density_2d(linewidth = 0.25, colour = "black")
set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
d <- ggplot(dsmall, aes(x, y))
# If you map an aesthetic to a categorical variable, you will get a
# set of contours for each value of that variable
d + geom_density_2d(aes(colour = cut))
# If you draw filled contours across multiple facets, the same bins are
# used across all facets
d + geom_density_2d_filled() + facet_wrap(vars(cut))
# If you want to make sure the peak intensity is the same in each facet,
# use `contour_var = "ndensity"`.
d + geom_density_2d_filled(contour_var = "ndensity") + facet_wrap(vars(cut))
# If you want to scale intensity by the number of observations in each group,
# use `contour_var = "count"`.
d + geom_density_2d_filled(contour_var = "count") + facet_wrap(vars(cut))
# If we turn contouring off, we can use other geoms, such as tiles:
d + stat_density_2d(
geom = "raster",
aes(fill = after_stat(density)),
contour = FALSE
) + scale_fill_viridis_c()
# Or points:
d + stat_density_2d(geom = "point", aes(size = after_stat(density)), n = 20, contour = FALSE)
# }
```