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#' Bin and summarise in 2d (rectangle & hexagons)
#'
#' \code{stat_summary_2d} is a 2d variation of \code{\link{stat_summary}}.
#' \code{stat_summary_hex} is a hexagonal variation of
#' \code{\link{stat_summary_2d}}. The data are divided into bins defined
#' by \code{x} and \code{y}, and then the values of \code{z} in each cell is
#' are summarised with \code{fun}.
#'
#' @section Aesthetics:
#' \itemize{
#' \item \code{x}: horizontal position
#' \item \code{y}: vertical position
#' \item \code{z}: value passed to the summary function
#' }
#' @section Computed variables:
#' \describe{
#' \item{x,y}{Location}
#' \item{value}{Value of summary statistic.}
#' }
#' @seealso \code{\link{stat_summary_hex}} for hexagonal summarization.
#' \code{\link{stat_bin2d}} for the binning options.
#' @inheritParams layer
#' @inheritParams geom_point
#' @inheritParams stat_bin_2d
#' @param drop drop if the output of \code{fun} is \code{NA}.
#' @param fun function for summary.
#' @param fun.args A list of extra arguments to pass to \code{fun}
#' @export
#' @examples
#' d <- ggplot(diamonds, aes(carat, depth, z = price))
#' d + stat_summary_2d()
#'
#' # Specifying function
#' d + stat_summary_2d(fun = function(x) sum(x^2))
#' d + stat_summary_2d(fun = var)
#' d + stat_summary_2d(fun = "quantile", fun.args = list(probs = 0.1))
#'
#' if (requireNamespace("hexbin")) {
#' d + stat_summary_hex()
#' }
stat_summary_2d <- function(mapping = NULL, data = NULL,
geom = "tile", position = "identity",
...,
bins = 30,
binwidth = NULL,
drop = TRUE,
fun = "mean",
fun.args = list(),
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = StatSummary2d,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
bins = bins,
binwidth = binwidth,
drop = drop,
fun = fun,
fun.args = fun.args,
na.rm = na.rm,
...
)
)
}
#' @export
#' @rdname stat_summary_2d
#' @usage NULL
stat_summary2d <- function(...) {
message("Please use stat_summary_2d() instead")
stat_summary_2d(...)
}
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatSummary2d <- ggproto("StatSummary2d", Stat,
default_aes = aes(fill = ..value..),
required_aes = c("x", "y", "z"),
compute_group = function(data, scales, binwidth = NULL, bins = 30,
breaks = NULL, origin = NULL, drop = TRUE,
fun = "mean", fun.args = list()) {
origin <- dual_param(origin, list(NULL, NULL))
binwidth <- dual_param(binwidth, list(NULL, NULL))
breaks <- dual_param(breaks, list(NULL, NULL))
bins <- dual_param(bins, list(x = 30, y = 30))
xbreaks <- bin2d_breaks(scales$x, breaks$x, origin$x, binwidth$x, bins$x)
ybreaks <- bin2d_breaks(scales$y, breaks$y, origin$y, binwidth$y, bins$y)
xbin <- cut(data$x, xbreaks, include.lowest = TRUE, labels = FALSE)
ybin <- cut(data$y, ybreaks, include.lowest = TRUE, labels = FALSE)
f <- function(x) {
do.call(fun, c(list(quote(x)), fun.args))
}
out <- tapply_df(data$z, list(xbin = xbin, ybin = ybin), f, drop = drop)
xdim <- bin_loc(xbreaks, out$xbin)
out$x <- xdim$mid
out$width <- xdim$length
ydim <- bin_loc(ybreaks, out$ybin)
out$y <- ydim$mid
out$height <- ydim$length
out
}
)
# Adaptation of tapply that returns a data frame instead of a matrix
tapply_df <- function(x, index, fun, ..., drop = TRUE) {
labels <- lapply(index, ulevels)
out <- expand.grid(labels, KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE)
grps <- split(x, index)
names(grps) <- NULL
out$value <- unlist(lapply(grps, fun, ...))
if (drop) {
n <- vapply(grps, length, integer(1))
out <- out[n > 0, , drop = FALSE]
}
out
}