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#' @section Stats:
#'
#' All \code{stat_*} functions (like \code{stat_bin}) return a layer that
#' contains a \code{Stat*} object (like \code{StatBin}). The \code{Stat*}
#' object is responsible for rendering the data in the plot.
#'
#' Each of the \code{Stat*} objects is a \code{\link{ggproto}} object, descended
#' from the top-level \code{Stat}, and each implements various methods and
#' fields. To create a new type of Stat object, you typically will want to
#' implement one or more of the following:
#'
#' \itemize{
#' \item Override one of :
#' \code{compute_layer(self, data, scales, ...)},
#' \code{compute_panel(self, data, scales, ...)}, or
#' \code{compute_group(self, data, scales, ...)}.
#'
#' \code{compute_layer()} is called once per layer, \code{compute_panel_()}
#' is called once per panel, and \code{compute_group()} is called once per
#' group. All must return a data frame.
#'
#' It's usually best to start by overriding \code{compute_group}: if
#' you find substantial performance optimisations, override higher up.
#' You'll need to read the source code of the default methods to see
#' what else you should be doing.
#'
#' \code{data} is a data frame containing the variables named according
#' to the aesthetics that they're mapped to. \code{scales} is a list
#' containing the \code{x} and \code{y} scales. There functions are called
#' before the facets are trained, so they are global scales, not local
#' to the individual panels.\code{...} contains the parameters returned by
#' \code{setup_params()}.
#' \item \code{finish_layer(data, params)}: called once for each layer. Used
#' to modify the data after scales has been applied, but before the data is
#' handed of to the geom for rendering. The default is to not modify the
#' data. Use this hook if the stat needs access to the actual aesthetic
#' values rather than the values that are mapped to the aesthetic.
#' \item \code{setup_params(data, params)}: called once for each layer.
#' Used to setup defaults that need to complete dataset, and to inform
#' the user of important choices. Should return list of parameters.
#' \item \code{setup_data(data, params)}: called once for each layer,
#' after \code{setp_params()}. Should return modified \code{data}.
#' Default methods removes all rows containing a missing value in
#' required aesthetics (with a warning if \code{!na.rm}).
#' \item \code{required_aes}: A character vector of aesthetics needed to
#' render the geom.
#' \item \code{default_aes}: A list (generated by \code{\link{aes}()} of
#' default values for aesthetics.
#' }
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
Stat <- ggproto("Stat",
# Should the values produced by the statistic also be transformed
# in the second pass when recently added statistics are trained to
# the scales
retransform = TRUE,
default_aes = aes(),
required_aes = character(),
non_missing_aes = character(),
setup_params = function(data, params) {
params
},
setup_data = function(data, params) {
data
},
compute_layer = function(self, data, params, layout) {
check_required_aesthetics(
self$required_aes,
c(names(data), names(params)),
snake_class(self)
)
data <- remove_missing(data, params$na.rm,
c(self$required_aes, self$non_missing_aes),
snake_class(self),
finite = TRUE
)
# Trim off extra parameters
params <- params[intersect(names(params), self$parameters())]
args <- c(list(data = quote(data), scales = quote(scales)), params)
plyr::ddply(data, "PANEL", function(data) {
scales <- layout$get_scales(data$PANEL[1])
tryCatch(do.call(self$compute_panel, args), error = function(e) {
warning("Computation failed in `", snake_class(self), "()`:\n",
e$message, call. = FALSE)
data.frame()
})
})
},
compute_panel = function(self, data, scales, ...) {
if (empty(data)) return(data.frame())
groups <- split(data, data$group)
stats <- lapply(groups, function(group) {
self$compute_group(data = group, scales = scales, ...)
})
stats <- mapply(function(new, old) {
if (empty(new)) return(data.frame())
unique <- uniquecols(old)
missing <- !(names(unique) %in% names(new))
cbind(
new,
unique[rep(1, nrow(new)), missing,drop = FALSE]
)
}, stats, groups, SIMPLIFY = FALSE)
do.call(plyr::rbind.fill, stats)
},
compute_group = function(self, data, scales) {
stop("Not implemented", call. = FALSE)
},
finish_layer = function(self, data, params) {
data
},
# See discussion at Geom$parameters()
extra_params = "na.rm",
parameters = function(self, extra = FALSE) {
# Look first in compute_panel. If it contains ... then look in compute_group
panel_args <- names(ggproto_formals(self$compute_panel))
group_args <- names(ggproto_formals(self$compute_group))
args <- if ("..." %in% panel_args) group_args else panel_args
# Remove arguments of defaults
args <- setdiff(args, names(ggproto_formals(Stat$compute_group)))
if (extra) {
args <- union(args, self$extra_params)
}
args
},
aesthetics = function(self) {
c(union(self$required_aes, names(self$default_aes)), "group")
}
)