R/stat-dens2d-labels.r
In aphalo/ggpp: Grammar Extensions to 'ggplot2'
Defines functions
these2logical
stat_dens2d_labels
Documented in
stat_dens2d_labels
these2logical
#' @title Replace labels in data based on 2D density
#'
#' @description \code{stat_dens2d_labels()} Sets values mapped to the
#' \code{label} aesthetic to \code{""} or a user provided character string
#' based on the local density in regions of a plot panel. Its main use is
#' together with repulsive geoms from package \code{\link[ggrepel]{ggrepel}}.
#' If there is no mapping to \code{label} in \code{data}, the mapping is set
#' to \code{rownames(data)}, with a message.
#'
#' @details \code{stat_dens2d_labels()} is designed to work together with
#' geometries from package 'ggrepel'. To avoid text labels being plotted over
#' unlabelled points all the rows in data need to be retained but
#' labels replaced with the empty character string, \code{""}. Function
#' \code{\link{stat_dens2d_filter}} cannot be used with the repulsive geoms
#' from 'ggrepel' because it drops observations.
#'
#' \code{stat_dens2d_labels()} can be useful also in other situations, as the
#' substitution character string can be set by the user by passing an argument
#' to \code{label.fill}. If this argument is \code{NULL} the unselected rows
#' are filtered out identically as by \code{stat_dens2d_filter}.
#'
#' The local density of observations in 2D (\emph{x} and \emph{y}) is computed
#' with function \code{\link[MASS]{kde2d}} and used to select observations,
#' passing to the geom all the rows in its \code{data} input but with with the
#' text of labels replaced in those "not kept". 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 having the label replaced by
#' passing a suitable argument to \code{keep.these}. Logical and integer
#' vectors function as indexes to rows in \code{data}, while a character
#' vector is compared to values in the variable mapped to the \code{label}
#' aesthetic. A function passed as argument to \code{keep.these} will receive
#' as its first argument the values in the variable mapped to \code{label} and
#' should return a character, logical or numeric vector as described above.
#'
#' How many labels are retained intact 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.
#'
#' 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 a grid is used
#' and how flexible the density surface estimate is. 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 overplotting 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 [0..1]. The fraction of the observations (or
#' rows) in \code{data} to be retained.
#' @param keep.number integer 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,yintercept numeric The split points for the data filtering.
#' @param invert.selection logical If \code{TRUE}, the complement of the
#' selected rows are returned.
#' @param h 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.
#' @param n Number of grid points in each direction. Can be scalar or a length-2
#' integer vector
#' @param label.fill character vector of length 1, a function or \code{NULL}.
#' @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 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 NA values should be stripped
#' before the computation proceeds.
#'
#' @return A plot layer instance. Using as output \code{data} the input
#' \code{data} after value substitution based on a 2D the filtering criterion.
#'
#' @seealso \code{\link{stat_dens2d_filter}} and \code{\link[MASS]{kde2d}} used
#' internally. Parameters \code{n}, \code{h} in this statistic correspond to
#' the parameters with the same name in this imported function. Limits are set
#' to the limits of the plot scales.
#'
#' @family statistics returning a subset of data
#'
#' @export
#'
#' @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() })
#' )
#'
#' # using defaults
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels()
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(keep.these = "zoujdg")
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(keep.these = function(x) {grepl("^z", x)})
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_s",
#' position = position_nudge_center(x = 0.1, y = 0.1,
#' center_x = mean,
#' center_y = mean),
#' vjust = "outward_mean", hjust = "outward_mean") +
#' expand_limits(x = c(-4, 4.5))
#'
#' ggrepel.installed <- requireNamespace("ggrepel", quietly = TRUE)
#' if (ggrepel.installed) {
#' library(ggrepel)
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_repel")
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_repel", label.fill = NA)
#'
#' # we keep labels starting with "a" across the whole plot, but all in sparse
#' # regions. To achieve this we pass as argument to label.fill a fucntion
#' # instead of a character string.
#' label.fun <- function(x) {ifelse(grepl("^a", x), x, "")}
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_repel", label.fill = label.fun)
#' }
#' # Using geom_debug() we can see that all 100 rows in \code{d} are
#' # returned. But only those labelled in the previous example still contain
#' # the original labels.
#'
#' gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
#' if (gginnards.installed) {
#' library(gginnards)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug")
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", return.density = TRUE)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", label.fill = NULL)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", label.fill = FALSE, return.density = TRUE)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", label.fill = NULL, return.density = TRUE)
#'
#' ggplot(data = d, aes(x, y)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug")
#' }
#'
stat_dens2d_labels <-
function(mapping = NULL,
data = NULL,
geom = "text",
position = "identity",
...,
keep.fraction = 0.10,
keep.number = Inf,
keep.sparse = TRUE,
keep.these = FALSE,
exclude.these = FALSE,
these.target = "label",
pool.along = c("xy", "x", "y", "none"),
xintercept = 0,
yintercept = 0,
invert.selection = FALSE,
h = NULL,
n = NULL,
label.fill = "",
return.density = FALSE,
na.rm = TRUE,
show.legend = FALSE,
inherit.aes = TRUE) {
pool.along <- rlang::arg_match(pool.along)
if (length(label.fill) > 1L) {
stop("Length for 'label.fill' is not 1: ", label.fill)
}
if (is.numeric(label.fill)) {
stop("'label.fill' should not be a 'numeric' value: ", label.fill)
}
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)
}
max.expected.length <- c(none = 4L, x = 2L, y = 2L, xy = 1L)[pool.along]
if (length(keep.fraction) > max.expected.length) {
if (max.expected.length == 4L) {
stop("Length of 'keep.fraction' should not exceed 4")
} else {
warning("'keep.fraction' is too long, did you forget to set 'pool.along'?")
}
}
if (length(keep.number) > max.expected.length) {
if (max.expected.length == 4L) {
stop("Length of 'keep.number' should not exceed 4")
} else {
warning("'keep.number' is too long, did you forget to set 'pool.along'?")
}
}
ggplot2::layer(
stat = StatDens2dLabels, 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,
yintercept = yintercept,
invert.selection = invert.selection,
h = h,
n = n,
label.fill = label.fill,
return.density = return.density,
...)
)
}
#' @rdname ggpp-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatDens2dLabels <-
ggplot2::ggproto(
"StatDens2dLabels",
ggplot2::Stat,
compute_panel =
function(data,
scales,
keep.fraction,
keep.number,
keep.sparse,
keep.these,
exclude.these,
these.target,
pool.along,
xintercept,
yintercept,
invert.selection,
h,
n,
label.fill,
return.density) {
force(data)
if (!exists("label", data) && !is.null(label.fill)) {
data[["label"]] <- rownames(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
if (pool.along != "xy") {
if (pool.along == "y" &&
!(xintercept < max(data[["x"]]) &&
xintercept > min(data[["x"]]))) {
pool.along <- "xy"
} else if (pool.along == "x" &&
!(yintercept < max(data[["y"]]) &&
yintercept > min(data[["y"]]))) {
pool.along <- "xy"
} else if (pool.along == "none") {
if (!(xintercept < max(data[["x"]]) &&
xintercept > min(data[["x"]])) &&
!(yintercept < max(data[["y"]]) &&
yintercept > min(data[["y"]]))) {
pool.along <- "xy"
} else if (!(xintercept < max(data[["x"]]) &&
xintercept > min(data[["x"]]))) {
pool.along <- "x"
} else if (!(yintercept < max(data[["y"]]) &&
yintercept > min(data[["y"]]))) {
pool.along <- "y"
}
}
}
# make list of logical vectors
if (pool.along == "y") {
selectors <-list(q12 = data[["x"]] <= xintercept,
q34 = data[["x"]] > 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 if (pool.along == "x") {
selectors <-list(q23 = data[["y"]] <= yintercept,
q41 = data[["y"]] > yintercept)
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 if (pool.along == "none") {
selectors <-list(q1 = data[["y"]] >= yintercept & data[["x"]] >= xintercept,
q2 = data[["y"]] < yintercept & data[["x"]] >= xintercept,
q3 = data[["y"]] < yintercept & data[["x"]] < xintercept,
q4 = data[["y"]] > yintercept & data[["x"]] < xintercept)
if (length(keep.fraction) != 4L) {
keep.fraction <- rep_len(keep.fraction, length.out = 4)
}
if (length(keep.number) != 4L) {
if (length(keep.number) == 1L) {
keep.number <- keep.number %/% 4
}
keep.number <- rep_len(keep.number, length.out = 4)
}
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]
# estimate 2D density
# data with fewer than 2 distinct values preventgs density() estimation
if (length(unique(data$x)) >= 2L &&
length(unique(data$y)) >= 2L) {
if (is.null(h)) {
h <- c(MASS::bandwidth.nrd(data$x), MASS::bandwidth.nrd(data$y))
}
if (is.null(n)) {
n <- trunc(sqrt(nrow(data))) * 8L
}
kk <- MASS::kde2d(
data[["x"]], data[["y"]], h = h, n = n,
lims = c(scales$x$dimension(), scales$y$dimension()))
dimnames(kk[["z"]]) <- list(kk[["x"]], kk[["y"]])
# compute 2D density at each observation's coordinates
kx <- cut(data$x, kk$x, labels = FALSE, include.lowest = TRUE)
ky <- cut(data$y, kk$y, labels = FALSE, include.lowest = TRUE)
kz <- sapply(seq_along(kx), function(i) kk$z[kx[i], ky[i]])
} else {
kz <- rep_len(1, nrow(data))
}
# we construct one logical vector by adding observations/label to be kept
# we may have a list of 1, 2, or 4 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]] ] <-
kz[ selectors[[i]] ] < stats::quantile(kz[ selectors[[i]] ],
keep.fraction[i],
names = FALSE,
type = 8)
} else {
keep[ selectors[[i]] ] <-
kz[ selectors[[i]] ] >= stats::quantile(kz[ 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"]] <- kz
}
if (is.null(label.fill)) {
data <- data[keep, ]
} else if (is.function(label.fill)) {
data[["label"]][!keep] <- label.fill(data[["label"]][!keep])
} else if (is.na(label.fill)) {
# NA_logical_, the default NA, cannot always be assigned to character
label.fill <- NA_character_
data[["label"]][!keep] <- label.fill
} else if (is.character(label.fill)) {
data[["label"]][!keep] <- label.fill
} else if (is.logical(label.fill)) {
if (label.fill) {
data[["label"]][!keep] <- ""
} # if FALSE data is not modified
} else {
stop("'label.fill' is : ", mode(label.fill),
" instead of 'character' or 'function'.")
}
data
},
required_aes = c("x", "y")
)
# Utils for stats that subset data
#' Convert keep.these argument into logical vector
#'
#' @param these character vector, integer vector, logical vector or
#' function that takes the variable mapped to the \code{label} aesthetic as
#' first argument and returns a character vector or a logical vector. These
#' rows from \code{data} are selected irrespective of the local density.
#' @param data data.frame The plot layer's data set.
#' @param these.target character The name of the variable (or aesthetic) in
#' which to select the target passed through \code{these}.
#'
#' @keywords internal
#'
these2logical <- function(these,
data,
these.target = "label") {
if (length(these)) {
if (is.character(these) || is.function(these)) {
if (is.character(these.target) && any(these.target == "label") &&
!exists("label", where = data, mode = "character", inherits = FALSE)) {
data$label <- rownames(data)
}
if (is.character(these.target)) {
orig.num.targets <- length(unique(these.target))
these.target <- intersect(these.target, colnames(data))
if (length(these.target) == 0L) {
stop("Variables in 'these.target' not in 'data'")
} else if (orig.num.targets > length(these.target)) {
warning("Some variables in 'these.target' not in 'data'")
}
}
}
if (is.function(these)) {
these <- these(data[ , these.target, drop = TRUE]) # any vector
}
if (is.character(these)) {
stopifnot(is.character(data[[these.target[1]]]))
these <- data[[these.target[1]]] %in% these # logical vector
}
if (is.numeric(these)) { # positional indices
temp <- rep_len(FALSE, length.out = nrow(data))
temp[these] <- TRUE
these <- temp
}
if (is.logical(these)) { # logical indices, if short recycle
if (length(these) >= 1L && length(these) < nrow(data)) {
these <- rep_len(these, length.out = nrow(data))
} else if (length(these) > nrow(data)) {
stop("Logical vector 'keep.these' or 'exclude.these' longer than data")
}
}
if (anyNA(these)) {
warning("Discarding 'NA's in 'keep.these' or 'exclude-these'")
these[is.na(these)] <- FALSE
}
} else { # replace NULL and vectors with length zero with FALSE
these <- rep_len(FALSE, length.out = nrow(data))
}
these
}
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Defines functions these2logical stat_dens2d_labels
Documented in stat_dens2d_labels these2logical
#' @title Replace labels in data based on 2D density
#'
#' @description \code{stat_dens2d_labels()} Sets values mapped to the
#' \code{label} aesthetic to \code{""} or a user provided character string
#' based on the local density in regions of a plot panel. Its main use is
#' together with repulsive geoms from package \code{\link[ggrepel]{ggrepel}}.
#' If there is no mapping to \code{label} in \code{data}, the mapping is set
#' to \code{rownames(data)}, with a message.
#'
#' @details \code{stat_dens2d_labels()} is designed to work together with
#' geometries from package 'ggrepel'. To avoid text labels being plotted over
#' unlabelled points all the rows in data need to be retained but
#' labels replaced with the empty character string, \code{""}. Function
#' \code{\link{stat_dens2d_filter}} cannot be used with the repulsive geoms
#' from 'ggrepel' because it drops observations.
#'
#' \code{stat_dens2d_labels()} can be useful also in other situations, as the
#' substitution character string can be set by the user by passing an argument
#' to \code{label.fill}. If this argument is \code{NULL} the unselected rows
#' are filtered out identically as by \code{stat_dens2d_filter}.
#'
#' The local density of observations in 2D (\emph{x} and \emph{y}) is computed
#' with function \code{\link[MASS]{kde2d}} and used to select observations,
#' passing to the geom all the rows in its \code{data} input but with with the
#' text of labels replaced in those "not kept". 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 having the label replaced by
#' passing a suitable argument to \code{keep.these}. Logical and integer
#' vectors function as indexes to rows in \code{data}, while a character
#' vector is compared to values in the variable mapped to the \code{label}
#' aesthetic. A function passed as argument to \code{keep.these} will receive
#' as its first argument the values in the variable mapped to \code{label} and
#' should return a character, logical or numeric vector as described above.
#'
#' How many labels are retained intact 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.
#'
#' 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 a grid is used
#' and how flexible the density surface estimate is. 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 overplotting 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 [0..1]. The fraction of the observations (or
#' rows) in \code{data} to be retained.
#' @param keep.number integer 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,yintercept numeric The split points for the data filtering.
#' @param invert.selection logical If \code{TRUE}, the complement of the
#' selected rows are returned.
#' @param h 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.
#' @param n Number of grid points in each direction. Can be scalar or a length-2
#' integer vector
#' @param label.fill character vector of length 1, a function or \code{NULL}.
#' @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 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 NA values should be stripped
#' before the computation proceeds.
#'
#' @return A plot layer instance. Using as output \code{data} the input
#' \code{data} after value substitution based on a 2D the filtering criterion.
#'
#' @seealso \code{\link{stat_dens2d_filter}} and \code{\link[MASS]{kde2d}} used
#' internally. Parameters \code{n}, \code{h} in this statistic correspond to
#' the parameters with the same name in this imported function. Limits are set
#' to the limits of the plot scales.
#'
#' @family statistics returning a subset of data
#'
#' @export
#'
#' @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() })
#' )
#'
#' # using defaults
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels()
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(keep.these = "zoujdg")
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(keep.these = function(x) {grepl("^z", x)})
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_s",
#' position = position_nudge_center(x = 0.1, y = 0.1,
#' center_x = mean,
#' center_y = mean),
#' vjust = "outward_mean", hjust = "outward_mean") +
#' expand_limits(x = c(-4, 4.5))
#'
#' ggrepel.installed <- requireNamespace("ggrepel", quietly = TRUE)
#' if (ggrepel.installed) {
#' library(ggrepel)
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_repel")
#'
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_repel", label.fill = NA)
#'
#' # we keep labels starting with "a" across the whole plot, but all in sparse
#' # regions. To achieve this we pass as argument to label.fill a fucntion
#' # instead of a character string.
#' label.fun <- function(x) {ifelse(grepl("^a", x), x, "")}
#' ggplot(data = d, aes(x, y, label = lab, colour = group)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "text_repel", label.fill = label.fun)
#' }
#' # Using geom_debug() we can see that all 100 rows in \code{d} are
#' # returned. But only those labelled in the previous example still contain
#' # the original labels.
#'
#' gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
#' if (gginnards.installed) {
#' library(gginnards)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug")
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", return.density = TRUE)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", label.fill = NULL)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", label.fill = FALSE, return.density = TRUE)
#'
#' ggplot(data = d, aes(x, y, label = lab)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug", label.fill = NULL, return.density = TRUE)
#'
#' ggplot(data = d, aes(x, y)) +
#' geom_point() +
#' stat_dens2d_labels(geom = "debug")
#' }
#'
stat_dens2d_labels <-
function(mapping = NULL,
data = NULL,
geom = "text",
position = "identity",
...,
keep.fraction = 0.10,
keep.number = Inf,
keep.sparse = TRUE,
keep.these = FALSE,
exclude.these = FALSE,
these.target = "label",
pool.along = c("xy", "x", "y", "none"),
xintercept = 0,
yintercept = 0,
invert.selection = FALSE,
h = NULL,
n = NULL,
label.fill = "",
return.density = FALSE,
na.rm = TRUE,
show.legend = FALSE,
inherit.aes = TRUE) {
pool.along <- rlang::arg_match(pool.along)
if (length(label.fill) > 1L) {
stop("Length for 'label.fill' is not 1: ", label.fill)
}
if (is.numeric(label.fill)) {
stop("'label.fill' should not be a 'numeric' value: ", label.fill)
}
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)
}
max.expected.length <- c(none = 4L, x = 2L, y = 2L, xy = 1L)[pool.along]
if (length(keep.fraction) > max.expected.length) {
if (max.expected.length == 4L) {
stop("Length of 'keep.fraction' should not exceed 4")
} else {
warning("'keep.fraction' is too long, did you forget to set 'pool.along'?")
}
}
if (length(keep.number) > max.expected.length) {
if (max.expected.length == 4L) {
stop("Length of 'keep.number' should not exceed 4")
} else {
warning("'keep.number' is too long, did you forget to set 'pool.along'?")
}
}
ggplot2::layer(
stat = StatDens2dLabels, 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,
yintercept = yintercept,
invert.selection = invert.selection,
h = h,
n = n,
label.fill = label.fill,
return.density = return.density,
...)
)
}
#' @rdname ggpp-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatDens2dLabels <-
ggplot2::ggproto(
"StatDens2dLabels",
ggplot2::Stat,
compute_panel =
function(data,
scales,
keep.fraction,
keep.number,
keep.sparse,
keep.these,
exclude.these,
these.target,
pool.along,
xintercept,
yintercept,
invert.selection,
h,
n,
label.fill,
return.density) {
force(data)
if (!exists("label", data) && !is.null(label.fill)) {
data[["label"]] <- rownames(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
if (pool.along != "xy") {
if (pool.along == "y" &&
!(xintercept < max(data[["x"]]) &&
xintercept > min(data[["x"]]))) {
pool.along <- "xy"
} else if (pool.along == "x" &&
!(yintercept < max(data[["y"]]) &&
yintercept > min(data[["y"]]))) {
pool.along <- "xy"
} else if (pool.along == "none") {
if (!(xintercept < max(data[["x"]]) &&
xintercept > min(data[["x"]])) &&
!(yintercept < max(data[["y"]]) &&
yintercept > min(data[["y"]]))) {
pool.along <- "xy"
} else if (!(xintercept < max(data[["x"]]) &&
xintercept > min(data[["x"]]))) {
pool.along <- "x"
} else if (!(yintercept < max(data[["y"]]) &&
yintercept > min(data[["y"]]))) {
pool.along <- "y"
}
}
}
# make list of logical vectors
if (pool.along == "y") {
selectors <-list(q12 = data[["x"]] <= xintercept,
q34 = data[["x"]] > 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 if (pool.along == "x") {
selectors <-list(q23 = data[["y"]] <= yintercept,
q41 = data[["y"]] > yintercept)
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 if (pool.along == "none") {
selectors <-list(q1 = data[["y"]] >= yintercept & data[["x"]] >= xintercept,
q2 = data[["y"]] < yintercept & data[["x"]] >= xintercept,
q3 = data[["y"]] < yintercept & data[["x"]] < xintercept,
q4 = data[["y"]] > yintercept & data[["x"]] < xintercept)
if (length(keep.fraction) != 4L) {
keep.fraction <- rep_len(keep.fraction, length.out = 4)
}
if (length(keep.number) != 4L) {
if (length(keep.number) == 1L) {
keep.number <- keep.number %/% 4
}
keep.number <- rep_len(keep.number, length.out = 4)
}
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]
# estimate 2D density
# data with fewer than 2 distinct values preventgs density() estimation
if (length(unique(data$x)) >= 2L &&
length(unique(data$y)) >= 2L) {
if (is.null(h)) {
h <- c(MASS::bandwidth.nrd(data$x), MASS::bandwidth.nrd(data$y))
}
if (is.null(n)) {
n <- trunc(sqrt(nrow(data))) * 8L
}
kk <- MASS::kde2d(
data[["x"]], data[["y"]], h = h, n = n,
lims = c(scales$x$dimension(), scales$y$dimension()))
dimnames(kk[["z"]]) <- list(kk[["x"]], kk[["y"]])
# compute 2D density at each observation's coordinates
kx <- cut(data$x, kk$x, labels = FALSE, include.lowest = TRUE)
ky <- cut(data$y, kk$y, labels = FALSE, include.lowest = TRUE)
kz <- sapply(seq_along(kx), function(i) kk$z[kx[i], ky[i]])
} else {
kz <- rep_len(1, nrow(data))
}
# we construct one logical vector by adding observations/label to be kept
# we may have a list of 1, 2, or 4 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]] ] <-
kz[ selectors[[i]] ] < stats::quantile(kz[ selectors[[i]] ],
keep.fraction[i],
names = FALSE,
type = 8)
} else {
keep[ selectors[[i]] ] <-
kz[ selectors[[i]] ] >= stats::quantile(kz[ 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"]] <- kz
}
if (is.null(label.fill)) {
data <- data[keep, ]
} else if (is.function(label.fill)) {
data[["label"]][!keep] <- label.fill(data[["label"]][!keep])
} else if (is.na(label.fill)) {
# NA_logical_, the default NA, cannot always be assigned to character
label.fill <- NA_character_
data[["label"]][!keep] <- label.fill
} else if (is.character(label.fill)) {
data[["label"]][!keep] <- label.fill
} else if (is.logical(label.fill)) {
if (label.fill) {
data[["label"]][!keep] <- ""
} # if FALSE data is not modified
} else {
stop("'label.fill' is : ", mode(label.fill),
" instead of 'character' or 'function'.")
}
data
},
required_aes = c("x", "y")
)
# Utils for stats that subset data
#' Convert keep.these argument into logical vector
#'
#' @param these character vector, integer vector, logical vector or
#' function that takes the variable mapped to the \code{label} aesthetic as
#' first argument and returns a character vector or a logical vector. These
#' rows from \code{data} are selected irrespective of the local density.
#' @param data data.frame The plot layer's data set.
#' @param these.target character The name of the variable (or aesthetic) in
#' which to select the target passed through \code{these}.
#'
#' @keywords internal
#'
these2logical <- function(these,
data,
these.target = "label") {
if (length(these)) {
if (is.character(these) || is.function(these)) {
if (is.character(these.target) && any(these.target == "label") &&
!exists("label", where = data, mode = "character", inherits = FALSE)) {
data$label <- rownames(data)
}
if (is.character(these.target)) {
orig.num.targets <- length(unique(these.target))
these.target <- intersect(these.target, colnames(data))
if (length(these.target) == 0L) {
stop("Variables in 'these.target' not in 'data'")
} else if (orig.num.targets > length(these.target)) {
warning("Some variables in 'these.target' not in 'data'")
}
}
}
if (is.function(these)) {
these <- these(data[ , these.target, drop = TRUE]) # any vector
}
if (is.character(these)) {
stopifnot(is.character(data[[these.target[1]]]))
these <- data[[these.target[1]]] %in% these # logical vector
}
if (is.numeric(these)) { # positional indices
temp <- rep_len(FALSE, length.out = nrow(data))
temp[these] <- TRUE
these <- temp
}
if (is.logical(these)) { # logical indices, if short recycle
if (length(these) >= 1L && length(these) < nrow(data)) {
these <- rep_len(these, length.out = nrow(data))
} else if (length(these) > nrow(data)) {
stop("Logical vector 'keep.these' or 'exclude.these' longer than data")
}
}
if (anyNA(these)) {
warning("Discarding 'NA's in 'keep.these' or 'exclude-these'")
these[is.na(these)] <- FALSE
}
} else { # replace NULL and vectors with length zero with FALSE
these <- rep_len(FALSE, length.out = nrow(data))
}
these
}
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