Expose argument n from stats::density in the interface of stat_density.

NEWS.md

@@ -1,5 +1,6 @@
 # ggplot2 2.1.0.9000 
 
+
 * `position_stack()` and `position_fill()` now sorts the stacking order so it 
   matches the order of the grouping. Use level reordering to alter the stacking 
   order. The default legend and stacking order is now also in line. The default 
@@ -28,6 +29,10 @@
 
 * `stat_ecdf()` respects `pad` argument (#1646).
 
+* `stat_density` now makes argument `n` of the unterlying function
+  `stats::density` ("number of equally spaced points at which the
+  density is to be estimated") accessible. (@hbuschme)
+
 * `x` and `y` scales are now symmetric regarding the list of
   aesthetics they accept: `xmin_final`, `xmax_final`, `xlower`,
   `xmiddle` and `xupper` are now valid `x` aesthetics.

R/stat-density.r

@@ -4,6 +4,9 @@
 #'   \code{\link{density}} for details
 #' @param kernel kernel used for density estimation, see
 #'   \code{\link{density}} for details
+#' @param n number of equally spaced points at which the density is to be
+#'   estimated, should be a power of two, see \code{\link{density}} for
+#'   details
 #' @param trim This parameter only matters if you are displaying multiple
 #'   densities in one plot. If \code{FALSE}, the default, each density is
 #'   computed on the full range of the data. If \code{TRUE}, each density
@@ -25,6 +28,7 @@ stat_density <- function(mapping = NULL, data = NULL,
                          bw = "nrd0",
                          adjust = 1,
                          kernel = "gaussian",
+                         n = 512,
                          trim = FALSE,
                          na.rm = FALSE,
                          show.legend = NA,
@@ -42,6 +46,7 @@ stat_density <- function(mapping = NULL, data = NULL,
       bw = bw,
       adjust = adjust,
       kernel = kernel,
+      n = n,
       trim = trim,
       na.rm = na.rm,
       ...
@@ -58,45 +63,45 @@ StatDensity <- ggproto("StatDensity", Stat,
   default_aes = aes(y = ..density.., fill = NA),
 
   compute_group = function(data, scales, bw = "nrd0", adjust = 1, kernel = "gaussian",
-                           trim = FALSE, na.rm = FALSE) {
+                           n = 512, trim = FALSE, na.rm = FALSE) {
     if (trim) {
       range <- range(data$x, na.rm = TRUE)
     } else {
       range <- scales$x$dimension()
     }
 
     compute_density(data$x, data$weight, from = range[1], to = range[2],
-      bw = bw, adjust = adjust, kernel = kernel)
+      bw = bw, adjust = adjust, kernel = kernel, n = n)
   }
 
 )
 
 compute_density <- function(x, w, from, to, bw = "nrd0", adjust = 1,
-                            kernel = "gaussian") {
-  n <- length(x)
+                            kernel = "gaussian", n = 512) {
+  nx <- length(x)
   if (is.null(w)) {
-    w <- rep(1 / n, n)
+    w <- rep(1 / nx, nx)
   }
 
   # if less than 3 points, spread density evenly over points
-  if (n < 3) {
+  if (nx < 3) {
     return(data.frame(
       x = x,
       density = w / sum(w),
       scaled = w / max(w),
       count = 1,
-      n = n
+      n = nx
     ))
   }
 
   dens <- stats::density(x, weights = w, bw = bw, adjust = adjust,
-    kernel = kernel, from = from, to = to)
+    kernel = kernel, n = n, from = from, to = to)
 
   data.frame(
     x = dens$x,
     density = dens$y,
     scaled =  dens$y / max(dens$y, na.rm = TRUE),
-    count =   dens$y * n,
-    n = n
+    count =   dens$y * nx,
+    n = nx
   )
 }