geom_pointdensity {ggpointdensity} | R Documentation |
A cross between a scatter plot and a 2D density plot
Description
geom_pointdensity()
visualizes overlapping data points on a 2D
coordinate system. It combines the benefits of
geom_point()
,
geom_density2d()
, and
geom_bin2d()
by coloring individual points based
on the density of neighboring points. This approach highlights the overall
data distribution while preserving the visibility of individual outliers,
making it ideal for data exploration.
Usage
geom_pointdensity(
mapping = NULL,
data = NULL,
stat = "pointdensity",
position = "identity",
...,
method = c("auto", "kde2d", "neighbors"),
method.args = list(),
adjust = 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) ).
|
stat |
The statistical transformation to use on the data for this layer.
When using a geom_*() function to construct a layer, the stat
argument can be used the override the default coupling between geoms and
stats. The stat argument accepts the following:
A Stat ggproto subclass, for example StatCount .
A string naming the stat. To give the stat as a string, strip the
function name of the stat_ prefix. For example, to use stat_count() ,
give the stat as "count" .
For more information and other ways to specify the stat, see the
layer stat documentation.
|
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The position argument accepts the following:
The result of calling a position function, such as position_jitter() .
This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a
string, strip the function name of the position_ prefix. For example,
to use position_jitter() , give the position as "jitter" .
For more information and other ways to specify the position, see the
layer position documentation.
|
... |
Other arguments passed on to layer() 's params argument. These
arguments broadly fall into one of 4 categories below. Notably, further
arguments to the position argument, or aesthetics that are required
can not be passed through ... . Unknown arguments that are not part
of the 4 categories below are ignored.
Static aesthetics that are not mapped to a scale, but are at a fixed
value and apply to the layer as a whole. For example, colour = "red"
or linewidth = 3 . The geom's documentation has an Aesthetics
section that lists the available options. The 'required' aesthetics
cannot be passed on to the params . Please note that while passing
unmapped aesthetics as vectors is technically possible, the order and
required length is not guaranteed to be parallel to the input data.
When constructing a layer using
a stat_*() function, the ... argument can be used to pass on
parameters to the geom part of the layer. An example of this is
stat_density(geom = "area", outline.type = "both") . The geom's
documentation lists which parameters it can accept.
Inversely, when constructing a layer using a
geom_*() function, the ... argument can be used to pass on parameters
to the stat part of the layer. An example of this is
geom_area(stat = "density", adjust = 0.5) . The stat's documentation
lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through
... . This can be one of the functions described as
key glyphs, to change the display of the layer in the legend.
|
method |
Density estimation method. Options are "auto" , "neighbors" ,
or "kde2d" .
-
"auto" (default): Selects the appropriate method based on the number of
points. "neighbors" is faster for small datasets, while "kde2d" is more
efficient for large datasets.
-
"neighbors" : Determines an appropriate radius and counts the number of
points within this radius for each point.
-
"kde2d" : Uses 2D kernel density estimation via MASS::kde2d() .
Additional arguments can be provided through method.args .
|
method.args |
List of additional arguments passed on to the density
estimation function defined by method (e.g. MASS::kde2d() ).
|
adjust |
Multiplicative bandwidth adjustment for density estimation. A
value less than 1 (e.g., adjust = 0.1 ) yields a smoother density
estimate, while a value greater than 1 (e.g., adjust = 5 ) increases the
level of visible detail.
|
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() .
|
Aesthetics
geom_point()
understands the following aesthetics (required aesthetics are in bold):
Learn more about setting these aesthetics in vignette("ggplot2-specs")
.
Author(s)
Lukas PM Kremer & Simon Anders
See Also
You can find examples and demo plots at
https://github.com/LKremer/ggpointdensity
Examples
library(ggpointdensity)
library(ggplot2)
library(dplyr)
# generate some toy data
dat <- bind_rows(
tibble(x = rnorm(7000, sd = 1),
y = rnorm(7000, sd = 10),
group = "foo"),
tibble(x = rnorm(3000, mean = 1, sd = .5),
y = rnorm(3000, mean = 7, sd = 5),
group = "bar"))
# plot it with geom_pointdensity()
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity()
# adjust the smoothing bandwidth,
# i.e. the radius around the points
# in which neighbors are counted
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity(adjust = .1)
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity(adjust = 4)
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity(adjust = 4) +
scale_colour_continuous(low = "red", high = "black")
# I recommend the viridis package
# for a more useful color scale
library(viridis)
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis()
# Of course you can combine the geom with standard
# ggplot2 features such as facets...
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
facet_wrap(~ group)
# ... or point shape and size:
dat_subset <- sample_frac(dat, .1) #' smaller data set
ggplot(data = dat_subset, mapping = aes(x = x, y = y)) +
geom_pointdensity(size = 3, shape = 17) +
scale_color_viridis()
# Zooming into the axis works as well, keep in mind
# that xlim() and ylim() affect the density since they
# remove data points.
# It may be better to use coord_cartesian() instead.
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
xlim(c(-1, 3)) + ylim(c(-5, 15))
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
coord_cartesian(xlim = c(-1, 3), ylim = c(-5, 15))
[Package
ggpointdensity version 0.2.0
Index]