Make level numeric for filled contours. Fixes #3875.

Author: clauswilke

R/geom-contour.r

@@ -20,7 +20,7 @@
 #' @seealso [geom_density_2d()]: 2d density contours
 #' @export
 #' @examples
-#' #' # Basic plot
+#' # Basic plot
 #' v <- ggplot(faithfuld, aes(waiting, eruptions, z = density))
 #' v + geom_contour()
 #'

R/geom-density2d.r

@@ -40,15 +40,13 @@
 #' # set of contours for each value of that variable
 #' d + geom_density_2d(aes(colour = cut))
 #'
-#' # When coloring by level across multiple facets, it is best to set breaks
-#' # manually to ensure they are consistent across facets
-#' breaks <- seq(0, 0.45, by = 0.05)
-#' d + geom_density_2d_filled(breaks = breaks) +
-#'   facet_grid(. ~ cut)
+#' # Coloring by level across multiple facets
+#' d + geom_density_2d_filled() +
+#'   facet_grid(. ~ cut) + scale_fill_viridis_c()
 #' # If you want to make sure the peak intensity is the same in each facet,
 #' # use `after_stat(nlevel)`. Note: this is a numerical value, not a factor,
 #' # and it represents the upper limit of each contour band.
-#' d + geom_density_2d_filled(aes(fill = after_stat(nlevel)), breaks = breaks) +
+#' d + geom_density_2d_filled(aes(fill = after_stat(nlevel))) +
 #'   facet_grid(. ~ cut) + scale_fill_viridis_c()
 #' # You can also use `after_stat(count)` to show the density scaled by the
 #' # number of observations in the group. This is useful to highlight differences

R/stat-contour.r

@@ -108,10 +108,11 @@ StatContourFilled <- ggproto("StatContourFilled", Stat,
     names(isobands) <- pretty_isoband_levels(names(isobands))
     path_df <- iso_to_polygon(isobands, data$group[1])
 
-    path_df$level <- ordered(path_df$level, levels = names(isobands))
-    path_df$level_low <- breaks[as.numeric(path_df$level)]
-    path_df$level_high <- breaks[as.numeric(path_df$level) + 1]
-    path_df$nlevel <- rescale_max(path_df$level_high)
+    path_df$bin <- ordered(path_df$level, levels = names(isobands))
+    path_df$level_low <- breaks[as.numeric(path_df$bin)]
+    path_df$level_high <- breaks[as.numeric(path_df$bin) + 1]
+    path_df$level <- 0.5*(path_df$level_low + path_df$level_high)
+    path_df$nlevel <- rescale_max(path_df$level)
 
     path_df
   }