stat_dens1d_filter: Filter observations by local 1D density

Description

stat_dens1d_filter Filters-out/filters-in observations in regions of a plot panel with high density of observations, based on the values mapped to one of x and y aesthetics. stat_dens1d_filter_g does the same 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_dens1d_filter(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  ...,
  keep.fraction = 0.1,
  keep.number = Inf,
  keep.sparse = TRUE,
  invert.selection = FALSE,
  bw = "SJ",
  kernel = "gaussian",
  adjust = 1,
  n = 512,
  orientation = "x",
  na.rm = TRUE,
  show.legend = FALSE,
  inherit.aes = TRUE
)
stat_dens1d_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,
  bw = "SJ",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  orientation = "x",
  ...
)

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

...

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

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.

numeric or character The smoothing bandwidth to be used. If numeric, the standard deviation of the smoothing kernel. If character, a rule to choose the bandwidth, as listed in bw.nrd.

kernel

character See density for details.

adjust

numeric A multiplicative bandwidth adjustment. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator through an argument passed to bw. The larger the value passed to adjust the stronger the smoothing, hence decreasing sensitivity to local changes in density.

numeric Number of equally spaced points at which the density is to be estimated for applying the cut point. See density for details.

orientation

character The aesthetic along which density is computed. Given explicitly by setting orientation to either "x" or "y".

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.

Value

A plot layer instance. Using as output data a subset of the rows in input data retained based on a 1D 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() })
)
d$xg <- d$x
d$xg[51:100] <- d$xg[51:100] + 1

# highlight the 1/10 of observations in sparsest regions of the plot
ggplot(data = d, aes(x, y)) +
  geom_point() +
  geom_rug(sides = "b") +
  stat_dens1d_filter(colour = "red") +
  stat_dens1d_filter(geom = "rug", colour = "red", sides = "b")

# highlight the 1/4 of observations in densest regions of the plot
ggplot(data = d, aes(x, y)) +
  geom_point() +
  geom_rug(sides = "b") +
  stat_dens1d_filter(colour = "blue",
                     keep.fraction = 1/4, keep.sparse = FALSE) +
  stat_dens1d_filter(geom = "rug", colour = "blue",
                     keep.fraction = 1/4, keep.sparse = FALSE,
                     sides = "b")

# switching axes
ggplot(data = d, aes(x, y)) +
  geom_point() +
  geom_rug(sides = "l") +
  stat_dens1d_filter(colour = "red", orientation = "y") +
  stat_dens1d_filter(geom = "rug", colour = "red", orientation = "y",
                     sides = "l")

# highlight 1/10 plus 1/10 observations in high and low density regions
ggplot(data = d, aes(x, y)) +
  geom_point() +
  geom_rug(sides = "b") +
  stat_dens1d_filter(colour = "red") +
  stat_dens1d_filter(geom = "rug", colour = "red", sides = "b") +
  stat_dens1d_filter(colour = "blue", keep.sparse = FALSE) +
  stat_dens1d_filter(geom = "rug",
                     colour = "blue", keep.sparse = FALSE, sides = "b")

# selecting the 1/10 observations in sparsest regions and their complement
ggplot(data = d, aes(x, y)) +
  stat_dens1d_filter(colour = "red") +
  stat_dens1d_filter(geom = "rug", colour = "red", sides = "b") +
  stat_dens1d_filter(colour = "blue", invert.selection = TRUE) +
  stat_dens1d_filter(geom = "rug",
                     colour = "blue", invert.selection = TRUE, sides = "b")

# density filtering done jointly across groups
ggplot(data = d, aes(xg, y, colour = group)) +
  geom_point() +
  geom_rug(sides = "b", colour = "black") +
  stat_dens1d_filter(shape = 1, size = 3, keep.fraction = 1/4, adjust = 2)

# density filtering done independently for each group
ggplot(data = d, aes(xg, y, colour = group)) +
  geom_point() +
  geom_rug(sides = "b") +
  stat_dens1d_filter_g(shape = 1, size = 3, keep.fraction = 1/4, adjust = 2)

# density filtering done jointly across groups by overriding grouping
ggplot(data = d, aes(xg, y, colour = group)) +
  geom_point() +
  geom_rug(sides = "b") +
  stat_dens1d_filter_g(colour = "black",
                       shape = 1, size = 3, keep.fraction = 1/4, adjust = 2)

# label observations
ggplot(data = d, aes(x, y, label = lab, colour = group)) +
  geom_point() +
  stat_dens1d_filter(geom = "text", hjust = "outward")

# repulsive labels with ggrepel::geom_text_repel()
ggplot(data = d, aes(x, y, label = lab, colour = group)) +
  geom_point() +
  stat_dens1d_filter(geom = "text_repel")

# }

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