Use helper

Author: teunbrand

R/geom-dotplot.r

@@ -17,21 +17,17 @@
 #' to match the number of dots.
 #'
 #' @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{`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"}
-#' }
+#' @eval rd_computed_vars(
+#'   x = 'center of each bin, if `binaxis` is `"x"`.',
+#'   y = 'center of each bin, if `binaxis` is `"x"`.',
+#'   binwidth = 'maximum width of each bin if method is `"dotdensity"`;
+#'   width of each bin if method is `"histodot"`.',
+#'   count   = "number of points in bin.",
+#'   ncount  = "count, scaled to a maximum of 1.",
+#'   density = 'density of points in bin, scaled to integrate to 1, if method
+#'   is `"histodot"`.',
+#'   ndensity = 'density, scaled to maximum of 1, if method is `"histodot"`.'
+#' )
 #'
 #' @inheritParams layer
 #' @inheritParams geom_point

R/stat-bin.r

@@ -27,17 +27,13 @@
 #'   or left edges of bins are included in the bin.
 #' @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{`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}
-#' }
+#' @eval rd_computed_vars(
+#'   count    = "number of points in bin.",
+#'   density  = "density of points in bin, scaled to integrate to 1.",
+#'   ncount   = "count, scaled to a maximum of 1.",
+#'   ndensity = "density, scaled to a maximum of 1.",
+#'   width    = "widths of bins."
+#' )
 #'
 #' @section Dropped variables:
 #' \describe{

R/stat-bin2d.r

@@ -5,16 +5,12 @@
 #' @param drop if `TRUE` removes all cells with 0 counts.
 #' @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{`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}
-#' }
+#' @eval rd_computed_vars(
+#'   count    = "number of points in bin.",
+#'   density  = "density of points in bin, scaled to integrate to 1.",
+#'   ncount   = "count, scaled to maximum of 1.",
+#'   ndensity = "density, scaled to a maximum of 1."
+#' )
 stat_bin_2d <- function(mapping = NULL, data = NULL,
                         geom = "tile", position = "identity",
                         ...,

R/stat-binhex.r

@@ -1,16 +1,12 @@
 #' @export
 #' @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{`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}
-#' }
+#' @eval rd_computed_vars(
+#'   count    = "number of points in bin.",
+#'   density  = "density of points in bin, scaled to integrate to 1.",
+#'   ncount   = "count, scaled to maximum of 1.",
+#'   ndensity = "density, scaled to maximum of 1."
+#' )
 stat_bin_hex <- function(mapping = NULL, data = NULL,
                          geom = "hex", position = "identity",
                          ...,

R/stat-boxplot.r

@@ -1,28 +1,21 @@
 #' @rdname geom_boxplot
 #' @param coef Length of the whiskers as multiple of IQR. Defaults to 1.5.
 #' @inheritParams stat_identity
-#' @section Computed variables:
-#' 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{`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
+#' @eval rd_computed_vars(
+#'   .details = "`stat_boxplot()` provides the following variables, some of
+#'   which depend on the orientation:",
+#'   width = "width of boxplot.",
+#'   "ymin|xmin" = "lower whisker = smallest observation greater than or equal
+#'   to lower hinger - 1.5 * IQR.",
+#'   "lower|xlower" = "lower hinge, 25% quantile.",
+#'   notchlower = "lower edge of notch = median - 1.58 * IQR / sqrt(n).",
+#'   "middle|xmiddle" = "median, 50% quantile.",
+#'   notchupper = "upper edge of notch = median + 1.58 * IQR / sqrt(n).",
+#'   "upper|xupper" = "upper hinge, 75% quantile.",
+#'   "ymax|xmax" = "upper whisker = largest observation less than or equal to
+#'   upper hinger + 1.5 * IQR."
+#' )
 stat_boxplot <- function(mapping = NULL, data = NULL,
                          geom = "boxplot", position = "dodge2",
                          ...,

R/stat-contour.r

@@ -3,24 +3,21 @@
 #' @export
 #' @eval rd_aesthetics("stat", "contour")
 #' @eval rd_aesthetics("stat", "contour_filled")
-#' @section Computed variables:
-#' 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{`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).}
-#' }
+#' @eval rd_computed_vars(
+#'   .details = "The computed variables differ somewhat for contour lines
+#'   (compbuted 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.",
+#'   level = "Height of contour. For contour lines, this is a numeric vector
+#'   that represents bin boundaries. For contour bands, this is an ordered
+#'   factor that represents bin ranges.",
+#'   "level_low,level_high,level_mid" = "(contour bands only) Lower and upper
+#'   bin boundaries for each band, as well as the mid point between boundaries.",
+#'   nlevel = "Height of contour, scaled to a maximum of 1.",
+#'   piece = "Contour piece (an integer)."
+#' )
 #'
 #' @section Dropped variables:
 #' \describe{

R/stat-count.r

@@ -1,10 +1,7 @@
-#' @section Computed variables:
-#' These are calculated by the 'stat' part of layers and can be accessed with
-#' [delayed evaluation][aes_eval].
-#' \describe{
-#'   \item{`after_stat(count)`}{number of points in bin}
-#'   \item{`after_stat(prop)`}{groupwise proportion}
-#' }
+#' @eval rd_computed_vars(
+#'   count = "number of points in bin.",
+#'   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
 #'   number of cases at each `x` position (without binning into ranges).

R/stat-density-2d.r

@@ -13,20 +13,18 @@
 #'    'NULL'. This makes it possible to adjust the bandwidth while still
 #'    using the a bandwidth estimator. For example, `adjust = 1/2` means
 #'    use half of the default bandwidth.
-#' @section Computed variables:
-#' 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{`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.}
-#' }
+#' @eval rd_computed_vars(
+#'   .details = "`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:",
+#'   density  = "The density estimate.",
+#'   ndensity = "Density estimate, scaled to a maximum of 1.",
+#'   count    = "Density estimate * number of observations in group.",
+#'   n        = "Number of observations in each group."
+#' )
 #'
+#' @section Computed variables:
 #' With contouring on (`contour = TRUE`), either [stat_contour()] or
 #' [stat_contour_filled()] (for contour lines or contour bands,
 #' respectively) is run after the density estimate has been obtained,

R/stat-density.r

@@ -20,18 +20,13 @@
 #'   finite, boundary effect of default density estimation will be corrected by
 #'   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{`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()`]}
-#' }
+#' @eval rd_computed_vars(
+#'  density  = "density estimate.",
+#'  count    = "density * number of points - useful for stacked density plots.",
+#'  scaled   = "density estimate, scaled to maximum of 1.",
+#'  n        = "number of points.",
+#'  ndensity = "alias for `scaled`, to mirror the syntax of [`stat_bin()`]."
+#' )
 #' @export
 #' @rdname geom_density
 stat_density <- function(mapping = NULL, data = NULL,

R/stat-ecdf.r

@@ -20,12 +20,9 @@
 #'   of points to interpolate with.
 #' @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{`after_stat(y)`}{cumulative density corresponding x}
-#' }
+#' @eval rd_computed_vars(
+#'   y = "Cumulative density corresponding to `x`."
+#' )
 #' @export
 #' @examples
 #' set.seed(1)