Wrap computed variables in after_stat()

Author: teunbrand

R/geom-dotplot.r

@@ -18,16 +18,19 @@
 #'
 #' @eval rd_aesthetics("geom", "dotplot")
 #' @section Computed variables:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval].
 #' \describe{
-#'   \item{x}{center of each bin, if binaxis is "x"}
-#'   \item{y}{center of each bin, if binaxis is "x"}
-#'   \item{binwidth}{max width of each bin if method is "dotdensity";
-#'     width of each bin if method is "histodot"}
-#'   \item{count}{number of points in bin}
-#'   \item{ncount}{count, scaled to maximum of 1}
-#'   \item{density}{density of points in bin, scaled to integrate to 1,
-#'     if method is "histodot"}
-#'   \item{ndensity}{density, scaled to maximum of 1, if method is "histodot"}
+#'   \item{`after_stat(x)`}{center of each bin, if binaxis is "x"}
+#'   \item{`after_stat(y)`}{center of each bin, if binaxis is "x"}
+#'   \item{`after_stat(binwidth)`}{max width of each bin if method is
+#'     "dotdensity"; width of each bin if method is "histodot"}
+#'   \item{`after_stat(count)`}{number of points in bin}
+#'   \item{`after_stat(ncount)`}{count, scaled to maximum of 1}
+#'   \item{`after_stat(density)`}{density of points in bin, scaled to integrate
+#'     to 1, if method is "histodot"}
+#'   \item{`after_stat(ndensity)`}{density, scaled to maximum of 1, if method is
+#'     "histodot"}
 #' }
 #'
 #' @inheritParams layer

R/stat-bin.r

@@ -28,12 +28,15 @@
 #' @param pad If `TRUE`, adds empty bins at either end of x. This ensures
 #'   frequency polygons touch 0. Defaults to `FALSE`.
 #' @section Computed variables:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval].
 #' \describe{
-#'   \item{`count`}{number of points in bin}
-#'   \item{`density`}{density of points in bin, scaled to integrate to 1}
-#'   \item{`ncount`}{count, scaled to maximum of 1}
-#'   \item{`ndensity`}{density, scaled to maximum of 1}
-#'   \item{`width`}{widths of bins}
+#'   \item{`after_stat(count)`}{number of points in bin}
+#'   \item{`after_stat(density)`}{density of points in bin, scaled to integrate
+#'     to 1}
+#'   \item{`after_stat(ncount)`}{count, scaled to maximum of 1}
+#'   \item{`after_stat(ndensity)`}{density, scaled to maximum of 1}
+#'   \item{`after_stat(width)`}{widths of bins}
 #' }
 #'
 #' @section Dropped variables:

R/stat-bin2d.r

@@ -6,11 +6,14 @@
 #' @export
 #' @rdname geom_bin_2d
 #' @section Computed variables:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval].
 #' \describe{
-#'   \item{count}{number of points in bin}
-#'   \item{density}{density of points in bin, scaled to integrate to 1}
-#'   \item{ncount}{count, scaled to maximum of 1}
-#'   \item{ndensity}{density, scaled to maximum of 1}
+#'   \item{`after_stat(count)`}{number of points in bin}
+#'   \item{`after_stat(density)`}{density of points in bin, scaled to integrate
+#'     to 1}
+#'   \item{`after_stat(ncount)`}{count, scaled to maximum of 1}
+#'   \item{`after_stat(ndensity)`}{density, scaled to maximum of 1}
 #' }
 stat_bin_2d <- function(mapping = NULL, data = NULL,
                         geom = "tile", position = "identity",

R/stat-binhex.r

@@ -2,11 +2,14 @@
 #' @rdname geom_hex
 #' @inheritParams stat_bin_2d
 #' @section Computed variables:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval].
 #' \describe{
-#'   \item{count}{number of points in bin}
-#'   \item{density}{density of points in bin, scaled to integrate to 1}
-#'   \item{ncount}{count, scaled to maximum of 1}
-#'   \item{ndensity}{density, scaled to maximum of 1}
+#'   \item{`after_stat(count)`}{number of points in bin}
+#'   \item{`after_stat(density)`}{density of points in bin, scaled to integrate
+#'     to 1}
+#'   \item{`after_stat(ncount)`}{count, scaled to maximum of 1}
+#'   \item{`after_stat(ndensity)`}{density, scaled to maximum of 1}
 #' }
 stat_bin_hex <- function(mapping = NULL, data = NULL,
                          geom = "hex", position = "identity",

R/stat-boxplot.r

@@ -2,16 +2,25 @@
 #' @param coef Length of the whiskers as multiple of IQR. Defaults to 1.5.
 #' @inheritParams stat_identity
 #' @section Computed variables:
-#' `stat_boxplot()` provides the following variables, some of which depend on the orientation:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval]. `stat_boxplot()` provides the following
+#' variables, some of which depend on the orientation:
 #' \describe{
-#'   \item{width}{width of boxplot}
-#'   \item{ymin *or* xmin}{lower whisker = smallest observation greater than or equal to lower hinge - 1.5 * IQR}
-#'   \item{lower *or* xlower}{lower hinge, 25% quantile}
-#'   \item{notchlower}{lower edge of notch = median - 1.58 * IQR / sqrt(n)}
-#'   \item{middle *or* xmiddle}{median, 50% quantile}
-#'   \item{notchupper}{upper edge of notch = median + 1.58 * IQR / sqrt(n)}
-#'   \item{upper *or* xupper}{upper hinge, 75% quantile}
-#'   \item{ymax *or* xmax}{upper whisker = largest observation less than or equal to upper hinge + 1.5 * IQR}
+#'   \item{`after_stat(width)`}{width of boxplot}
+#'   \item{`after_stat(ymin)` *or* `after_stat(xmin)`}{lower whisker = smallest
+#'     observation greater than or equal to lower hinge - 1.5 * IQR}
+#'   \item{`after_stat(lower)` *or* `after_stat(xlower)`}{lower hinge, 25%
+#'     quantile}
+#'   \item{`after_stat(notchlower)`}{lower edge of notch =
+#'     median - 1.58 * IQR / sqrt(n)}
+#'   \item{`after_stat(middle)` *or* `after_stat(xmiddle)`}{median,
+#'     50% quantile}
+#'   \item{`after_stat(notchupper)`}{upper edge of notch =
+#'     median + 1.58 * IQR / sqrt(n)}
+#'   \item{`after_stat(upper)` *or* `after_stat(xupper)`}{upper hinge, 75%
+#'     quantile}
+#'   \item{`after_stat(ymax)` *or* `after_stat(xmax)`}{upper whisker = largest
+#'     observation less than or equal to upper hinge + 1.5 * IQR}
 #' }
 #' @export
 stat_boxplot <- function(mapping = NULL, data = NULL,

R/stat-contour.r

@@ -4,19 +4,22 @@
 #' @eval rd_aesthetics("stat", "contour")
 #' @eval rd_aesthetics("stat", "contour_filled")
 #' @section Computed variables:
-#' The computed variables differ somewhat for contour lines (computed by
-#' `stat_contour()`) and contour bands (filled contours, computed by `stat_contour_filled()`).
-#' The variables `nlevel` and `piece` are available for both, whereas `level_low`, `level_high`,
-#' and `level_mid` are only available for bands. The variable `level` is a numeric or a factor
-#' depending on whether lines or bands are calculated.
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval]. The computed variables differ somewhat for
+#' contour lines (computed by `stat_contour()`) and contour bands (filled
+#' contours, computed by `stat_contour_filled()`). The variables `nlevel` and
+#' `piece` are available for both, whereas `level_low`, `level_high`, and
+#' `level_mid` are only available for bands. The variable `level` is a numeric
+#' or a factor depending on whether lines or bands are calculated.
 #' \describe{
-#'  \item{`level`}{Height of contour. For contour lines, this is numeric vector that
-#'    represents bin boundaries. For contour bands, this is an ordered factor that
-#'    represents bin ranges.}
-#'  \item{`level_low`, `level_high`, `level_mid`}{(contour bands only) Lower and upper
-#'    bin boundaries for each band, as well the mid point between the boundaries.}
-#'  \item{`nlevel`}{Height of contour, scaled to maximum of 1.}
-#'  \item{`piece`}{Contour piece (an integer).}
+#'  \item{`after_stat(level)`}{Height of contour. For contour lines, this is
+#'    numeric vector that represents bin boundaries. For contour bands, this is
+#'    an ordered factor that represents bin ranges.}
+#'  \item{`after_stat(level_low)`, `after_stat(level_high)`,
+#'    `after_stat(level_mid)`}{(contour bands only) Lower and upper bin
+#'    boundaries for each band, as well the mid point between the boundaries.}
+#'  \item{`after_stat(nlevel)`}{Height of contour, scaled to maximum of 1.}
+#'  \item{`after_stat(piece)`}{Contour piece (an integer).}
 #' }
 #'
 #' @section Dropped variables:

R/stat-count.r

@@ -1,7 +1,9 @@
 #' @section Computed variables:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval].
 #' \describe{
-#'   \item{count}{number of points in bin}
-#'   \item{prop}{groupwise proportion}
+#'   \item{`after_stat(count)`}{number of points in bin}
+#'   \item{`after_stat(prop)`}{groupwise proportion}
 #' }
 #' @seealso [stat_bin()], which bins data in ranges and counts the
 #'   cases in each range. It differs from `stat_count()`, which counts the

R/stat-density-2d.r

@@ -14,15 +14,17 @@
 #'    using the a bandwidth estimator. For example, `adjust = 1/2` means
 #'    use half of the default bandwidth.
 #' @section Computed variables:
-#' `stat_density_2d()` and `stat_density_2d_filled()` compute different
-#' variables depending on whether contouring is turned on or off. With
-#' contouring off (`contour = FALSE`), both stats behave the same, and the
-#' following variables are provided:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval]. `stat_density_2d()` and
+#' `stat_density_2d_filled()` compute different variables depending on whether
+#' contouring is turned on or off. With contouring off (`contour = FALSE`), both
+#' stats behave the same, and the following variables are provided:
 #' \describe{
-#'   \item{`density`}{The density estimate.}
-#'   \item{`ndensity`}{Density estimate, scaled to a maximum of 1.}
-#'   \item{`count`}{Density estimate * number of observations in group.}
-#'   \item{`n`}{Number of observations in each group.}
+#'   \item{`after_stat(density)`}{The density estimate.}
+#'   \item{`after_stat(ndensity)`}{Density estimate, scaled to a maximum of 1.}
+#'   \item{`after_stat(count)`}{Density estimate * number of observations in
+#'     group.}
+#'   \item{`after_stat(n)`}{Number of observations in each group.}
 #' }
 #'
 #' With contouring on (`contour = TRUE`), either [stat_contour()] or

R/stat-density.r

@@ -21,13 +21,15 @@
 #'   reflecting tails outside `bounds` around their closest edge. Data points
 #'   outside of bounds are removed with a warning.
 #' @section Computed variables:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval].
 #' \describe{
-#'   \item{density}{density estimate}
-#'   \item{count}{density * number of points - useful for stacked density
-#'      plots}
-#'   \item{scaled}{density estimate, scaled to maximum of 1}
-#'   \item{n}{number of points}
-#'   \item{ndensity}{alias for `scaled`, to mirror the syntax of
+#'   \item{`after_stat(density)`}{density estimate}
+#'   \item{`after_stat(count)`}{density * number of points - useful for stacked
+#'     density plots}
+#'   \item{`after_stat(scaled)`}{density estimate, scaled to maximum of 1}
+#'   \item{`after_stat(n)`}{number of points}
+#'   \item{`after_stat(ndensity)`}{alias for `scaled`, to mirror the syntax of
 #'    [`stat_bin()`]}
 #' }
 #' @export

R/stat-ecdf.r

@@ -21,8 +21,10 @@
 #' @param pad If `TRUE`, pad the ecdf with additional points (-Inf, 0)
 #'   and (Inf, 1)
 #' @section Computed variables:
+#' These are calculated by the 'stat' part of layers and can be accessed with
+#' [delayed evaluation][aes_eval].
 #' \describe{
-#'   \item{y}{cumulative density corresponding x}
+#'   \item{`after_stat(y)`}{cumulative density corresponding x}
 #' }
 #' @export
 #' @examples