stat_dens2d_filter: Filter observations by local 2D density

Description

stat_dens2d_filter Filters-out/filters-in observations in regions of a plot panel with high density of observations, based on the values mapped to both x and y aesthetics. stat_dens2d_filter_g does the filtering by group instead of by panel. This second stat is useful for highlighting observations, while the first one tends to be most useful when the aim is to prevent clashes among text labels.

Usage

stat_dens2d_filter(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  keep.fraction = 0.1,
  keep.number = Inf,
  keep.sparse = TRUE,
  invert.selection = FALSE,
  na.rm = TRUE,
  show.legend = FALSE,
  inherit.aes = TRUE,
  h = NULL,
  n = NULL,
  ...
)
stat_dens2d_filter_g(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  keep.fraction = 0.1,
  keep.number = Inf,
  keep.sparse = TRUE,
  invert.selection = FALSE,
  na.rm = TRUE,
  show.legend = FALSE,
  inherit.aes = TRUE,
  h = NULL,
  n = NULL,
  ...
)

Arguments

mapping

The aesthetic mapping, usually constructed with aes or aes_. Only needs to be set at the layer level if you are overriding the plot defaults.

data

A layer specific dataset - only needed if you want to override the plot defaults.

geom

The geometric object to use display the data.

position

The position adjustment to use for overlapping points on this layer

keep.fraction

numeric [0..1]. The fraction of the observations (or rows) in data to be retained.

keep.number

integer Set the maximum number of observations to retain, effective only if obeying keep.fraction would result in a larger number.

keep.sparse

logical If TRUE, the default, observations from the more sparse regions are retained, if FALSE those from the densest regions.

invert.selection

logical If TRUE, the complement of the selected rows are returned.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

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.

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.

vector of bandwidths for x and y directions. Defaults to normal reference bandwidth (see bandwidth.nrd). A scalar value will be taken to apply to both directions.

Number of grid points in each direction. Can be scalar or a length-2 integer vector

...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Value

A plot layer instance. Using as output data a subset of the rows in input data retained based on a 2D-density-based filtering criterion.

Examples

Run this code

# NOT RUN {
library(ggrepel)

random_string <- function(len = 6) {
paste(sample(letters, len, replace = TRUE), collapse = "")
}

# Make random data.
set.seed(1001)
d <- tibble::tibble(
  x = rnorm(100),
  y = rnorm(100),
  group = rep(c("A", "B"), c(50, 50)),
  lab = replicate(100, { random_string() })
)

# filter (and here highlight) 1/10 observations in sparsest regions
ggplot(data = d, aes(x, y)) +
  geom_point() +
  stat_dens2d_filter(colour = "red")

# filter observations not in the sparsest regions
ggplot(data = d, aes(x, y)) +
  geom_point() +
  stat_dens2d_filter(colour = "blue", invert.selection = TRUE)

# filter observations in dense regions of the plot
ggplot(data = d, aes(x, y)) +
  geom_point() +
  stat_dens2d_filter(colour = "blue", keep.sparse = FALSE)

# filter 1/2 the observations
ggplot(data = d, aes(x, y)) +
  geom_point() +
  stat_dens2d_filter(colour = "red", keep.fraction = 0.5)

# filter 1/2 the observations but cap their number to maximum 12 observations
ggplot(data = d, aes(x, y)) +
  geom_point() +
  stat_dens2d_filter(colour = "red",
                     keep.fraction = 0.5,
                     keep.number = 12)

# density filtering done jointly across groups
ggplot(data = d, aes(x, y, colour = group)) +
  geom_point() +
  stat_dens2d_filter(shape = 1, size = 3, keep.fraction = 1/4)

# density filtering done independently for each group
ggplot(data = d, aes(x, y, colour = group)) +
  geom_point() +
  stat_dens2d_filter_g(shape = 1, size = 3, keep.fraction = 1/4)

# density filtering done jointly across groups by overriding grouping
ggplot(data = d, aes(x, y, colour = group)) +
  geom_point() +
  stat_dens2d_filter_g(colour = "black",
                       shape = 1, size = 3, keep.fraction = 1/4)

# label observations
ggplot(data = d, aes(x, y, label = lab, colour = group)) +
  geom_point() +
  stat_dens2d_filter(geom = "text")

# repulsive labels with ggrepel::geom_text_repel()
ggplot(data = d, aes(x, y, label = lab, colour = group)) +
  geom_point() +
  stat_dens2d_filter(geom = "text_repel")

# }

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