R/stat-dens1d-filter.r
In aphalo/ggpextra: Grammar Extensions to 'ggplot2'
Defines functions
stat_dens1d_filter_g
stat_dens1d_filter
Documented in
stat_dens1d_filter
stat_dens1d_filter_g
#' @title Filter observations by local 1D density
#'
#' @description \code{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 \code{x} and \code{y} aesthetics.
#' \code{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. By default the data are handled all together, but it is also
#' possible to control labeling separately in each tail.
#'
#' @details The 1D density of observations of \emph{x} or \emph{y} is computed
#' with function \code{\link[stats]{density}} and used to select observations,
#' passing to the geom a subset of the rows in its \code{data} input. The
#' default is to select observations in sparse regions of the plot, but the
#' selection can be inverted so that only observations in the densest regions
#' are returned. Specific observations can be protected from being deselected
#' and "kept" by passing a suitable argument to \code{keep.these}. Logical and
#' integer vectors work as indexes to rows in \code{data}, while a values in a
#' character vector are compared to the character values mapped to the
#' \code{label} aesthetic. A function passed as argument to keep.these will
#' receive as argument the values in the variable mapped to \code{label} and
#' should return a character, logical or numeric vector as described above. If
#' no variable has been mapped to \code{label}, row names are used in its
#' place.
#'
#' How many rows are retained in addition to those in \code{keep.these} is
#' controlled with arguments passed to \code{keep.number} and
#' \code{keep.fraction}. \code{keep.number} sets the maximum number of
#' observations selected, whenever \code{keep.fraction} results in fewer
#' observations selected, it is obeyed. If `xintercept` is a finite value
#' within the \emph{x} range of the data and \code{pool.along}
#' is passed \code{"none"} the data as are split into two groups
#' and \code{keep.number} and \code{keep.fraction} are applied separately to
#' each tail with density still computed jointly from all observations. If the
#' length of \code{keep.number} and \code{keep.fraction} is one, this value
#' is used for both tails, if their length is two, the first value is use
#' for the left tail and the second value for the right tail.
#'
#' Computation of density and of the default bandwidth require at least
#' two observations with different values. If data do not fulfill this
#' condition, they are kept only if \code{keep.fraction = 1}. This is correct
#' behavior for a single observation, but can be surprising in the case of
#' multiple observations.
#'
#' Parameters \code{keep.these} and \code{exclude.these} make it possible to
#' force inclusion or exclusion of observations after the density is computed.
#' In case of conflict, \code{exclude.these} overrides \code{keep.these}.
#'
#' @note Which points are kept and which not depends on how dense and flexible
#' is the density curve estimate. This depends on the values passed as
#' arguments to parameters \code{n}, \code{bw} and \code{kernel}. It is
#' also important to be aware that both \code{geom_text()} and
#' \code{geom_text_repel()} can avoid over plotting by discarding labels at
#' the plot rendering stage, i.e., what is plotted may differ from what is
#' returned by this statistic.
#'
#' @param mapping The aesthetic mapping, usually constructed with
#' \code{\link[ggplot2]{aes}} or \code{\link[ggplot2]{aes_}}. Only needs to be
#' set at the layer level if you are overriding the plot defaults.
#' @param data A layer specific dataset - only needed if you want to override
#' the plot defaults.
#' @param geom The geometric object to use display the data.
#' @param keep.fraction numeric vector of length 1 or 2 [0..1]. The fraction of
#' the observations (or rows) in \code{data} to be retained.
#' @param keep.number integer vector of length 1 or 2. Set the maximum number of
#' observations to retain, effective only if obeying \code{keep.fraction}
#' would result in a larger number.
#' @param keep.sparse logical If \code{TRUE}, the default, observations from the
#' more sparse regions are retained, if \code{FALSE} those from the densest
#' regions.
#' @param keep.these,exclude.these character vector, integer vector, logical
#' vector or function that takes one or more variables in data selected by
#' \code{these.target}. Negative integers behave as in R's extraction methods.
#' The rows from \code{data} indicated by \code{keep.these} and
#' \code{exclude.these} are kept or excluded irrespective of the local
#' density.
#' @param these.target character, numeric or logical selecting one or more
#' column(s) of \code{data}. If \code{TRUE} the whole \code{data} object is
#' passed.
#' @param pool.along character, one of \code{"none"} or \code{"x"},
#' indicating if selection should be done pooling the observations along the
#' \emph{x} aesthetic, or separately on either side of \code{xintercept}.
#' @param xintercept numeric The split point for the data filtering. If
#' \code{NA} the data are not split.
#' @param invert.selection logical If \code{TRUE}, the complement of the
#' selected rows are returned.
#' @param bw 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 \code{\link[stats]{bw.nrd}}.
#' @param 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 \code{bw}. The larger the value
#' passed to \code{adjust} the stronger the smoothing, hence decreasing
#' sensitivity to local changes in density.
#' @param kernel character See \code{\link{density}} for details.
#' @param n numeric Number of equally spaced points at which the density is to
#' be estimated for applying the cut point. See \code{\link{density}} for
#' details.
#' @param return.density logical vector of lenght 1. If \code{TRUE} add columns
#' \code{"density"} and \code{"keep.obs"} to the returned data frame.
#' @param orientation character The aesthetic along which density is computed.
#' Given explicitly by setting orientation to either \code{"x"} or \code{"y"}.
#' @param position The position adjustment to use for overlapping points on this
#' layer
#' @param show.legend logical. Should this layer be included in the legends?
#' \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE}
#' never includes, and \code{TRUE} always includes.
#' @param inherit.aes If \code{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. \code{\link[ggplot2]{borders}}.
#' @param ... other arguments passed on to \code{\link[ggplot2]{layer}}. This
#' can include aesthetics whose values you want to set, not map. See
#' \code{\link[ggplot2]{layer}} for more details.
#' @param na.rm a logical value indicating whether \code{NA} values should be
#' stripped before the computation proceeds.
#'
#' @return A plot layer instance. Using as output \code{data} a subset of the
#' rows in input \code{data} retained based on a 1D filtering criterion.
#'
#' @seealso \code{\link[stats]{density}} used internally.
#'
#' @family statistics returning a subset of data
#'
#' @examples
#'
#' 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")
#'
#' # looking under the hood with gginnards::geom_debug()
#' gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
#' if (gginnards.installed) {
#' library(gginnards)
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' stat_dens1d_filter(geom = "debug")
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' stat_dens1d_filter(geom = "debug", return.density = TRUE)
#'
#' }
#'
#' @export
#'
stat_dens1d_filter <-
function(mapping = NULL,
data = NULL,
geom = "point",
position = "identity",
...,
keep.fraction = 0.10,
keep.number = Inf,
keep.sparse = TRUE,
keep.these = FALSE,
exclude.these = FALSE,
these.target = "label",
pool.along = c("x", "none"),
xintercept = 0,
invert.selection = FALSE,
bw = "SJ",
kernel = "gaussian",
adjust = 1,
n = 512,
return.density = FALSE,
orientation = c("x", "y"),
na.rm = TRUE,
show.legend = FALSE,
inherit.aes = TRUE) {
pool.along <- rlang::arg_match(pool.along)
orientation <- rlang::arg_match(orientation)
if (any(is.na(keep.fraction) | keep.fraction < 0 | keep.fraction > 1)) {
stop("Out of range or missing value for 'keep.fraction': ", keep.fraction)
}
if (any(is.na(keep.number) | keep.number < 0)) {
stop("Out of range or missing value for 'keep.number': ", keep.number)
}
ggplot2::layer(
stat = StatDens1dFilter, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
keep.fraction = keep.fraction,
keep.number = keep.number,
keep.sparse = keep.sparse,
keep.these = keep.these,
exclude.these = exclude.these,
these.target = these.target,
pool.along = pool.along,
xintercept = xintercept,
invert.selection = invert.selection,
bw = bw,
adjust = adjust,
kernel = kernel,
n = n,
return.density = return.density,
orientation = orientation,
...)
)
}
#' @rdname stat_dens1d_filter
#'
#' @export
#'
stat_dens1d_filter_g <-
function(mapping = NULL, data = NULL,
geom = "point", position = "identity",
keep.fraction = 0.10,
keep.number = Inf,
keep.sparse = TRUE,
keep.these = FALSE,
exclude.these = FALSE,
these.target = "label",
pool.along = c("x", "none"),
xintercept = 0,
invert.selection = FALSE,
na.rm = TRUE, show.legend = FALSE,
inherit.aes = TRUE,
bw = "SJ",
adjust = 1,
kernel = "gaussian",
n = 512,
return.density = FALSE,
orientation = c("x", "y"),
...) {
pool.along <- rlang::arg_match(pool.along)
orientation <- rlang::arg_match(orientation)
if (any(is.na(keep.fraction) | keep.fraction < 0 | keep.fraction > 1)) {
stop("Out of range or missing value for 'keep.fraction': ", keep.fraction)
}
if (any(is.na(keep.number) | keep.number < 0)) {
stop("Out of range or missing value for 'keep.number': ", keep.number)
}
ggplot2::layer(
stat = StatDens1dFilterG, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
keep.fraction = keep.fraction,
keep.number = keep.number,
keep.sparse = keep.sparse,
keep.these = keep.these,
exclude.these = exclude.these,
these.target = these.target,
pool.along = pool.along,
xintercept = xintercept,
invert.selection = invert.selection,
bw = bw,
kernel = kernel,
adjust = adjust,
n = n,
return.density = return.density,
orientation = orientation,
...)
)
}
#' @rdname ggpp-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatDens1dFilter <-
ggplot2::ggproto(
"StatDens1dFilter",
ggplot2::Stat,
compute_panel =
# dens1d_flt_compute_fun,
# code duplicated below to ensure it is all seen by 'covr'
function(data,
scales,
keep.fraction,
keep.number,
keep.sparse,
keep.these,
exclude.these,
these.target,
pool.along,
xintercept,
invert.selection,
bw,
kernel,
adjust,
n,
orientation,
return.density) {
force(data)
keep.these <- these2logical(these = keep.these,
data = data,
these.target = these.target)
exclude.these <- these2logical(these = exclude.these,
data = data,
these.target = these.target)
# discard redundant splits and make list of logical vectors
if (pool.along != "x" &&
xintercept < max(data[[orientation]]) &&
xintercept > min(data[[orientation]])) {
selectors <-list(low.tail = data[[orientation]] <= xintercept,
high.tail = data[[orientation]] > xintercept)
if (length(keep.fraction) != 2L) {
keep.fraction <- rep_len(keep.fraction, length.out = 2)
}
if (length(keep.number) != 2L) {
if (length(keep.number) == 1L) {
keep.number <- keep.number %/% 2
}
keep.number <- rep_len(keep.number, length.out = 2)
}
num.rows <- sapply(selectors, sum) # selectors are logical
} else {
keep.fraction <- keep.fraction[[1]] # can be a vector or a list
keep.number <- keep.number[[1]]
num.rows <- nrow(data)
selectors <- list(all = rep.int(TRUE, times = num.rows))
}
# vectorized
too.large.frac <- num.rows * keep.fraction > keep.number
keep.fraction[too.large.frac] <-
keep.number[too.large.frac] / num.rows[too.large.frac]
# density on a grid
# data with fewer than 2 rows is as a special case as density() fails
if (length(unique(data[[orientation]])) >= 2L) {
dens <-
stats::density(data[[orientation]],
bw = bw, kernel = kernel, adjust = adjust, n = n,
from = scales[[orientation]]$dimension()[1],
to = scales[[orientation]]$dimension()[2])
# estimate density at each observations coordinates
fdens <- stats::splinefun(dens$x, dens$y) # y contains estimate of density
dens <- fdens(data[[orientation]])
} else {
if (nrow(data) > 1L) {
message("Density not computed, too few distinct values in '", orientation, "'")
}
dens <- rep_len(1, nrow(data))
}
# we construct one logical vector by adding observations/label to be kept
# we may have a list of 1 or 2 logical vectors
keep <- logical(nrow(data))
for (i in seq_along(selectors)) {
if (keep.fraction[i] == 1 ||
(length(selectors[[i]]) < 2L && keep.fraction[i] >= 0.5)) {
keep[ selectors[[i]] ] <- TRUE
} else if (keep.fraction[i] != 0 && length(selectors[[i]]) >= 2L) {
if (keep.sparse) {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] < stats::quantile(dens[ selectors[[i]] ],
keep.fraction[i],
names = FALSE,
type = 8)
} else {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] >= stats::quantile(dens[ selectors[[i]] ],
1 - keep.fraction[i],
names = FALSE,
type = 8)
}
}
}
keep <- (keep | keep.these) & !exclude.these
if (invert.selection){
keep <- !keep
}
if (return.density) {
data[["keep.obs"]] <- keep
data[["density"]] <- dens
}
data[keep, ]
},
required_aes = "x|y"
)
#' @rdname ggpp-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatDens1dFilterG <-
ggplot2::ggproto(
"StatDens1dFilterG",
ggplot2::Stat,
compute_group =
# dens1d_flt_compute_fun,
# code duplicated above to ensure it is all seen by 'covr'
function(data,
scales,
keep.fraction,
keep.number,
keep.sparse,
keep.these,
exclude.these,
these.target,
pool.along,
xintercept,
invert.selection,
bw,
kernel,
adjust,
n,
orientation,
return.density) {
force(data)
keep.these <- these2logical(these = keep.these,
data = data,
these.target = these.target)
exclude.these <- these2logical(these = exclude.these,
data = data,
these.target = these.target)
# discard redundant splits and make list of logical vectors
if (pool.along != "x" &&
xintercept < max(data[[orientation]]) &&
xintercept > min(data[[orientation]])) {
selectors <-list(low.tail = data[[orientation]] <= xintercept,
high.tail = data[[orientation]] > xintercept)
if (length(keep.fraction) != 2L) {
keep.fraction <- rep_len(keep.fraction, length.out = 2)
}
if (length(keep.number) != 2L) {
if (length(keep.number) == 1L) {
keep.number <- keep.number %/% 2
}
keep.number <- rep_len(keep.number, length.out = 2)
}
num.rows <- sapply(selectors, sum) # selectors are logical
} else {
keep.fraction <- keep.fraction[[1]] # can be a vector or a list
keep.number <- keep.number[[1]]
num.rows <- nrow(data)
selectors <- list(all = rep.int(TRUE, times = num.rows))
}
# vectorized
too.large.frac <- num.rows * keep.fraction > keep.number
keep.fraction[too.large.frac] <-
keep.number[too.large.frac] / num.rows[too.large.frac]
# density on a grid
# data with fewer than 2 rows is as a special case as density() fails
if (length(unique(data[[orientation]])) >= 2L) {
dens <-
stats::density(data[[orientation]],
bw = bw, kernel = kernel, adjust = adjust, n = n,
from = scales[[orientation]]$dimension()[1],
to = scales[[orientation]]$dimension()[2])
# estimate density at each observations coordinates
fdens <- stats::splinefun(dens$x, dens$y) # y contains estimate of density
dens <- fdens(data[[orientation]])
} else {
if (nrow(data) > 1L) {
message("Density not computed, too few distinct values in '", orientation, "'")
}
dens <- rep_len(1, nrow(data))
}
# we construct one logical vector by adding observations/label to be kept
# we may have a list of 1 or 2 logical vectors
keep <- logical(nrow(data))
for (i in seq_along(selectors)) {
if (keep.fraction[i] == 1) {
keep[ selectors[[i]] ] <- TRUE
} else if (keep.fraction[i] != 0 && length(selectors[[i]]) >= 2L) {
if (keep.sparse) {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] < stats::quantile(dens[ selectors[[i]] ],
keep.fraction[i],
names = FALSE,
type = 8)
} else {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] >= stats::quantile(dens[ selectors[[i]] ],
1 - keep.fraction[i],
names = FALSE,
type = 8)
}
}
}
keep <- (keep | keep.these) & !exclude.these
if (invert.selection){
keep <- !keep
}
if (return.density) {
data[["keep.obs"]] <- keep
data[["density"]] <- dens
}
data[keep, ]
},
required_aes = "x|y"
)
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Defines functions stat_dens1d_filter_g stat_dens1d_filter
Documented in stat_dens1d_filter stat_dens1d_filter_g
#' @title Filter observations by local 1D density
#'
#' @description \code{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 \code{x} and \code{y} aesthetics.
#' \code{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. By default the data are handled all together, but it is also
#' possible to control labeling separately in each tail.
#'
#' @details The 1D density of observations of \emph{x} or \emph{y} is computed
#' with function \code{\link[stats]{density}} and used to select observations,
#' passing to the geom a subset of the rows in its \code{data} input. The
#' default is to select observations in sparse regions of the plot, but the
#' selection can be inverted so that only observations in the densest regions
#' are returned. Specific observations can be protected from being deselected
#' and "kept" by passing a suitable argument to \code{keep.these}. Logical and
#' integer vectors work as indexes to rows in \code{data}, while a values in a
#' character vector are compared to the character values mapped to the
#' \code{label} aesthetic. A function passed as argument to keep.these will
#' receive as argument the values in the variable mapped to \code{label} and
#' should return a character, logical or numeric vector as described above. If
#' no variable has been mapped to \code{label}, row names are used in its
#' place.
#'
#' How many rows are retained in addition to those in \code{keep.these} is
#' controlled with arguments passed to \code{keep.number} and
#' \code{keep.fraction}. \code{keep.number} sets the maximum number of
#' observations selected, whenever \code{keep.fraction} results in fewer
#' observations selected, it is obeyed. If `xintercept` is a finite value
#' within the \emph{x} range of the data and \code{pool.along}
#' is passed \code{"none"} the data as are split into two groups
#' and \code{keep.number} and \code{keep.fraction} are applied separately to
#' each tail with density still computed jointly from all observations. If the
#' length of \code{keep.number} and \code{keep.fraction} is one, this value
#' is used for both tails, if their length is two, the first value is use
#' for the left tail and the second value for the right tail.
#'
#' Computation of density and of the default bandwidth require at least
#' two observations with different values. If data do not fulfill this
#' condition, they are kept only if \code{keep.fraction = 1}. This is correct
#' behavior for a single observation, but can be surprising in the case of
#' multiple observations.
#'
#' Parameters \code{keep.these} and \code{exclude.these} make it possible to
#' force inclusion or exclusion of observations after the density is computed.
#' In case of conflict, \code{exclude.these} overrides \code{keep.these}.
#'
#' @note Which points are kept and which not depends on how dense and flexible
#' is the density curve estimate. This depends on the values passed as
#' arguments to parameters \code{n}, \code{bw} and \code{kernel}. It is
#' also important to be aware that both \code{geom_text()} and
#' \code{geom_text_repel()} can avoid over plotting by discarding labels at
#' the plot rendering stage, i.e., what is plotted may differ from what is
#' returned by this statistic.
#'
#' @param mapping The aesthetic mapping, usually constructed with
#' \code{\link[ggplot2]{aes}} or \code{\link[ggplot2]{aes_}}. Only needs to be
#' set at the layer level if you are overriding the plot defaults.
#' @param data A layer specific dataset - only needed if you want to override
#' the plot defaults.
#' @param geom The geometric object to use display the data.
#' @param keep.fraction numeric vector of length 1 or 2 [0..1]. The fraction of
#' the observations (or rows) in \code{data} to be retained.
#' @param keep.number integer vector of length 1 or 2. Set the maximum number of
#' observations to retain, effective only if obeying \code{keep.fraction}
#' would result in a larger number.
#' @param keep.sparse logical If \code{TRUE}, the default, observations from the
#' more sparse regions are retained, if \code{FALSE} those from the densest
#' regions.
#' @param keep.these,exclude.these character vector, integer vector, logical
#' vector or function that takes one or more variables in data selected by
#' \code{these.target}. Negative integers behave as in R's extraction methods.
#' The rows from \code{data} indicated by \code{keep.these} and
#' \code{exclude.these} are kept or excluded irrespective of the local
#' density.
#' @param these.target character, numeric or logical selecting one or more
#' column(s) of \code{data}. If \code{TRUE} the whole \code{data} object is
#' passed.
#' @param pool.along character, one of \code{"none"} or \code{"x"},
#' indicating if selection should be done pooling the observations along the
#' \emph{x} aesthetic, or separately on either side of \code{xintercept}.
#' @param xintercept numeric The split point for the data filtering. If
#' \code{NA} the data are not split.
#' @param invert.selection logical If \code{TRUE}, the complement of the
#' selected rows are returned.
#' @param bw 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 \code{\link[stats]{bw.nrd}}.
#' @param 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 \code{bw}. The larger the value
#' passed to \code{adjust} the stronger the smoothing, hence decreasing
#' sensitivity to local changes in density.
#' @param kernel character See \code{\link{density}} for details.
#' @param n numeric Number of equally spaced points at which the density is to
#' be estimated for applying the cut point. See \code{\link{density}} for
#' details.
#' @param return.density logical vector of lenght 1. If \code{TRUE} add columns
#' \code{"density"} and \code{"keep.obs"} to the returned data frame.
#' @param orientation character The aesthetic along which density is computed.
#' Given explicitly by setting orientation to either \code{"x"} or \code{"y"}.
#' @param position The position adjustment to use for overlapping points on this
#' layer
#' @param show.legend logical. Should this layer be included in the legends?
#' \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE}
#' never includes, and \code{TRUE} always includes.
#' @param inherit.aes If \code{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. \code{\link[ggplot2]{borders}}.
#' @param ... other arguments passed on to \code{\link[ggplot2]{layer}}. This
#' can include aesthetics whose values you want to set, not map. See
#' \code{\link[ggplot2]{layer}} for more details.
#' @param na.rm a logical value indicating whether \code{NA} values should be
#' stripped before the computation proceeds.
#'
#' @return A plot layer instance. Using as output \code{data} a subset of the
#' rows in input \code{data} retained based on a 1D filtering criterion.
#'
#' @seealso \code{\link[stats]{density}} used internally.
#'
#' @family statistics returning a subset of data
#'
#' @examples
#'
#' 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")
#'
#' # looking under the hood with gginnards::geom_debug()
#' gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
#' if (gginnards.installed) {
#' library(gginnards)
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' stat_dens1d_filter(geom = "debug")
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' stat_dens1d_filter(geom = "debug", return.density = TRUE)
#'
#' }
#'
#' @export
#'
stat_dens1d_filter <-
function(mapping = NULL,
data = NULL,
geom = "point",
position = "identity",
...,
keep.fraction = 0.10,
keep.number = Inf,
keep.sparse = TRUE,
keep.these = FALSE,
exclude.these = FALSE,
these.target = "label",
pool.along = c("x", "none"),
xintercept = 0,
invert.selection = FALSE,
bw = "SJ",
kernel = "gaussian",
adjust = 1,
n = 512,
return.density = FALSE,
orientation = c("x", "y"),
na.rm = TRUE,
show.legend = FALSE,
inherit.aes = TRUE) {
pool.along <- rlang::arg_match(pool.along)
orientation <- rlang::arg_match(orientation)
if (any(is.na(keep.fraction) | keep.fraction < 0 | keep.fraction > 1)) {
stop("Out of range or missing value for 'keep.fraction': ", keep.fraction)
}
if (any(is.na(keep.number) | keep.number < 0)) {
stop("Out of range or missing value for 'keep.number': ", keep.number)
}
ggplot2::layer(
stat = StatDens1dFilter, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
keep.fraction = keep.fraction,
keep.number = keep.number,
keep.sparse = keep.sparse,
keep.these = keep.these,
exclude.these = exclude.these,
these.target = these.target,
pool.along = pool.along,
xintercept = xintercept,
invert.selection = invert.selection,
bw = bw,
adjust = adjust,
kernel = kernel,
n = n,
return.density = return.density,
orientation = orientation,
...)
)
}
#' @rdname stat_dens1d_filter
#'
#' @export
#'
stat_dens1d_filter_g <-
function(mapping = NULL, data = NULL,
geom = "point", position = "identity",
keep.fraction = 0.10,
keep.number = Inf,
keep.sparse = TRUE,
keep.these = FALSE,
exclude.these = FALSE,
these.target = "label",
pool.along = c("x", "none"),
xintercept = 0,
invert.selection = FALSE,
na.rm = TRUE, show.legend = FALSE,
inherit.aes = TRUE,
bw = "SJ",
adjust = 1,
kernel = "gaussian",
n = 512,
return.density = FALSE,
orientation = c("x", "y"),
...) {
pool.along <- rlang::arg_match(pool.along)
orientation <- rlang::arg_match(orientation)
if (any(is.na(keep.fraction) | keep.fraction < 0 | keep.fraction > 1)) {
stop("Out of range or missing value for 'keep.fraction': ", keep.fraction)
}
if (any(is.na(keep.number) | keep.number < 0)) {
stop("Out of range or missing value for 'keep.number': ", keep.number)
}
ggplot2::layer(
stat = StatDens1dFilterG, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
keep.fraction = keep.fraction,
keep.number = keep.number,
keep.sparse = keep.sparse,
keep.these = keep.these,
exclude.these = exclude.these,
these.target = these.target,
pool.along = pool.along,
xintercept = xintercept,
invert.selection = invert.selection,
bw = bw,
kernel = kernel,
adjust = adjust,
n = n,
return.density = return.density,
orientation = orientation,
...)
)
}
#' @rdname ggpp-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatDens1dFilter <-
ggplot2::ggproto(
"StatDens1dFilter",
ggplot2::Stat,
compute_panel =
# dens1d_flt_compute_fun,
# code duplicated below to ensure it is all seen by 'covr'
function(data,
scales,
keep.fraction,
keep.number,
keep.sparse,
keep.these,
exclude.these,
these.target,
pool.along,
xintercept,
invert.selection,
bw,
kernel,
adjust,
n,
orientation,
return.density) {
force(data)
keep.these <- these2logical(these = keep.these,
data = data,
these.target = these.target)
exclude.these <- these2logical(these = exclude.these,
data = data,
these.target = these.target)
# discard redundant splits and make list of logical vectors
if (pool.along != "x" &&
xintercept < max(data[[orientation]]) &&
xintercept > min(data[[orientation]])) {
selectors <-list(low.tail = data[[orientation]] <= xintercept,
high.tail = data[[orientation]] > xintercept)
if (length(keep.fraction) != 2L) {
keep.fraction <- rep_len(keep.fraction, length.out = 2)
}
if (length(keep.number) != 2L) {
if (length(keep.number) == 1L) {
keep.number <- keep.number %/% 2
}
keep.number <- rep_len(keep.number, length.out = 2)
}
num.rows <- sapply(selectors, sum) # selectors are logical
} else {
keep.fraction <- keep.fraction[[1]] # can be a vector or a list
keep.number <- keep.number[[1]]
num.rows <- nrow(data)
selectors <- list(all = rep.int(TRUE, times = num.rows))
}
# vectorized
too.large.frac <- num.rows * keep.fraction > keep.number
keep.fraction[too.large.frac] <-
keep.number[too.large.frac] / num.rows[too.large.frac]
# density on a grid
# data with fewer than 2 rows is as a special case as density() fails
if (length(unique(data[[orientation]])) >= 2L) {
dens <-
stats::density(data[[orientation]],
bw = bw, kernel = kernel, adjust = adjust, n = n,
from = scales[[orientation]]$dimension()[1],
to = scales[[orientation]]$dimension()[2])
# estimate density at each observations coordinates
fdens <- stats::splinefun(dens$x, dens$y) # y contains estimate of density
dens <- fdens(data[[orientation]])
} else {
if (nrow(data) > 1L) {
message("Density not computed, too few distinct values in '", orientation, "'")
}
dens <- rep_len(1, nrow(data))
}
# we construct one logical vector by adding observations/label to be kept
# we may have a list of 1 or 2 logical vectors
keep <- logical(nrow(data))
for (i in seq_along(selectors)) {
if (keep.fraction[i] == 1 ||
(length(selectors[[i]]) < 2L && keep.fraction[i] >= 0.5)) {
keep[ selectors[[i]] ] <- TRUE
} else if (keep.fraction[i] != 0 && length(selectors[[i]]) >= 2L) {
if (keep.sparse) {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] < stats::quantile(dens[ selectors[[i]] ],
keep.fraction[i],
names = FALSE,
type = 8)
} else {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] >= stats::quantile(dens[ selectors[[i]] ],
1 - keep.fraction[i],
names = FALSE,
type = 8)
}
}
}
keep <- (keep | keep.these) & !exclude.these
if (invert.selection){
keep <- !keep
}
if (return.density) {
data[["keep.obs"]] <- keep
data[["density"]] <- dens
}
data[keep, ]
},
required_aes = "x|y"
)
#' @rdname ggpp-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatDens1dFilterG <-
ggplot2::ggproto(
"StatDens1dFilterG",
ggplot2::Stat,
compute_group =
# dens1d_flt_compute_fun,
# code duplicated above to ensure it is all seen by 'covr'
function(data,
scales,
keep.fraction,
keep.number,
keep.sparse,
keep.these,
exclude.these,
these.target,
pool.along,
xintercept,
invert.selection,
bw,
kernel,
adjust,
n,
orientation,
return.density) {
force(data)
keep.these <- these2logical(these = keep.these,
data = data,
these.target = these.target)
exclude.these <- these2logical(these = exclude.these,
data = data,
these.target = these.target)
# discard redundant splits and make list of logical vectors
if (pool.along != "x" &&
xintercept < max(data[[orientation]]) &&
xintercept > min(data[[orientation]])) {
selectors <-list(low.tail = data[[orientation]] <= xintercept,
high.tail = data[[orientation]] > xintercept)
if (length(keep.fraction) != 2L) {
keep.fraction <- rep_len(keep.fraction, length.out = 2)
}
if (length(keep.number) != 2L) {
if (length(keep.number) == 1L) {
keep.number <- keep.number %/% 2
}
keep.number <- rep_len(keep.number, length.out = 2)
}
num.rows <- sapply(selectors, sum) # selectors are logical
} else {
keep.fraction <- keep.fraction[[1]] # can be a vector or a list
keep.number <- keep.number[[1]]
num.rows <- nrow(data)
selectors <- list(all = rep.int(TRUE, times = num.rows))
}
# vectorized
too.large.frac <- num.rows * keep.fraction > keep.number
keep.fraction[too.large.frac] <-
keep.number[too.large.frac] / num.rows[too.large.frac]
# density on a grid
# data with fewer than 2 rows is as a special case as density() fails
if (length(unique(data[[orientation]])) >= 2L) {
dens <-
stats::density(data[[orientation]],
bw = bw, kernel = kernel, adjust = adjust, n = n,
from = scales[[orientation]]$dimension()[1],
to = scales[[orientation]]$dimension()[2])
# estimate density at each observations coordinates
fdens <- stats::splinefun(dens$x, dens$y) # y contains estimate of density
dens <- fdens(data[[orientation]])
} else {
if (nrow(data) > 1L) {
message("Density not computed, too few distinct values in '", orientation, "'")
}
dens <- rep_len(1, nrow(data))
}
# we construct one logical vector by adding observations/label to be kept
# we may have a list of 1 or 2 logical vectors
keep <- logical(nrow(data))
for (i in seq_along(selectors)) {
if (keep.fraction[i] == 1) {
keep[ selectors[[i]] ] <- TRUE
} else if (keep.fraction[i] != 0 && length(selectors[[i]]) >= 2L) {
if (keep.sparse) {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] < stats::quantile(dens[ selectors[[i]] ],
keep.fraction[i],
names = FALSE,
type = 8)
} else {
keep[ selectors[[i]] ] <-
dens[ selectors[[i]] ] >= stats::quantile(dens[ selectors[[i]] ],
1 - keep.fraction[i],
names = FALSE,
type = 8)
}
}
}
keep <- (keep | keep.these) & !exclude.these
if (invert.selection){
keep <- !keep
}
if (return.density) {
data[["keep.obs"]] <- keep
data[["density"]] <- dens
}
data[keep, ]
},
required_aes = "x|y"
)
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