update from master

DESCRIPTION

@@ -198,7 +198,6 @@ Collate:
     'theme-defaults.r'
     'theme-elements.r'
     'theme.r'
-    'translate-qplot-base.r'
     'translate-qplot-ggplot.r'
     'translate-qplot-lattice.r'
     'utilities-break.r'
@@ -210,4 +209,4 @@ Collate:
     'zxx.r'
     'zzz.r'
 VignetteBuilder: knitr
-RoxygenNote: 5.0.0
+RoxygenNote: 5.0.1

NEWS.md

@@ -1,7 +1,16 @@
 # ggplot2 2.0.0.9000
 
+* Add access to `bw` argument of `density` in `stat_density`, which makes
+  it easy to get consistent smoothing between facets for example (@jiho)
+
 ## Bug fixes and minor improvements
 
+* `stat_function()` once again works with discrete x axes (#1509).
+
+* All `\donttest{}` examples run.
+
+* Removed a superfluous comma in `theme-defaults.r` code (@jschoeley)
+
 * Fixed a compatibility issue with `ggproto` and R versions prior to 3.1.2.
   (#1444)
 

R/data.R

@@ -135,12 +135,12 @@
 #' }
 "msleep"
 
-#' Terms of 10 presidents from Eisenhower to Bush W.
+#' Terms of 11 presidents from Eisenhower to Obama.
 #'
 #' The names of each president, the start and end date of their term, and
-#' their party of 10 US presidents from Eisenhower to Bush W.
+#' their party of 11 US presidents from Eisenhower to Obama.
 #'
-#' @format A data frame with 10 rows and 4 variables
+#' @format A data frame with 11 rows and 4 variables
 "presidential"
 
 #' Vector field of seal movements.

R/fortify.r

@@ -17,6 +17,9 @@ fortify.data.frame <- function(model, data, ...) model
 fortify.NULL <- function(model, data, ...) waiver()
 #' @export
 fortify.default <- function(model, data, ...) {
-
-  stop("ggplot2 doesn't know how to deal with data of class ", class(model), call. = FALSE)
+  stop(
+    "ggplot2 doesn't know how to deal with data of class ",
+    paste(class(model), collapse = "/"),
+    call. = FALSE
+  )
 }

R/margins.R

@@ -1,10 +1,10 @@
 #' Define margins.
 #'
-#' This is a convenience function that creates a grid unit object of the
+#' This is a convenient function that creates a grid unit object of the
 #' correct length to use for setting margins.
 #'
 #' @export
-#' @param t,b,r,l Dimensions of each margin. (To remember order, think trouble).
+#' @param t,r,b,l Dimensions of each margin. (To remember order, think trouble).
 #' @param unit Default units of dimensions. Defaults to "pt" so it
 #'   can be most easily scaled with the text.
 #' @export

R/plot.r

@@ -90,7 +90,7 @@ ggplot.default <- function(data = NULL, mapping = aes(), ...,
 ggplot.data.frame <- function(data, mapping = aes(), ...,
                               environment = parent.frame()) {
   if (!missing(mapping) && !inherits(mapping, "uneval")) {
-    stop("Mapping should be created with aes or aes_string")
+    stop("Mapping should be created with `aes() or `aes_()`.", call. = FALSE)
   }
 
   p <- structure(list(

R/scale-discrete-.r

@@ -15,10 +15,9 @@
 #' @rdname scale_discrete
 #' @export
 #' @examples
-#' \donttest{
-#' ggplot(diamonds, aes(cut)) + stat_bin()
 #' ggplot(diamonds, aes(cut)) + geom_bar()
 #'
+#' \donttest{
 #' # The discrete position scale is added automatically whenever you
 #' # have a discrete position.
 #'
@@ -29,9 +28,6 @@
 #' d + scale_x_discrete("Cut", labels = c("Fair" = "F","Good" = "G",
 #'   "Very Good" = "VG","Perfect" = "P","Ideal" = "I"))
 #'
-#' d + scale_y_discrete("Clarity")
-#' d + scale_x_discrete("Cut") + scale_y_discrete("Clarity")
-#'
 #' # Use limits to adjust the which levels (and in what order)
 #' # are displayed
 #' d + scale_x_discrete(limits=c("Fair","Ideal"))

R/stat-density.r

@@ -1,4 +1,7 @@
-#' @param adjust see \code{\link{density}} for details
+#' @param bw the smoothing bandwidth to be used, see 
+#'   \code{\link{density}} for details
+#' @param adjust adjustment of the bandwidth, see
+#'   \code{\link{density}} for details
 #' @param kernel kernel used for density estimation, see
 #'   \code{\link{density}} for details
 #' @param trim This parameter only matters if you are displaying multiple
@@ -17,7 +20,7 @@
 #' @export
 #' @rdname geom_density
 stat_density <- function(mapping = NULL, data = NULL, geom = "area",
-                         position = "stack", adjust = 1, kernel = "gaussian",
+                         position = "stack", bw = "nrd0", adjust = 1, kernel = "gaussian",
                          trim = FALSE, na.rm = FALSE,
                          show.legend = NA, inherit.aes = TRUE, ...) {
 
@@ -30,6 +33,7 @@ stat_density <- function(mapping = NULL, data = NULL, geom = "area",
     show.legend = show.legend,
     inherit.aes = inherit.aes,
     params = list(
+      bw = bw,
       adjust = adjust,
       kernel = kernel,
       trim = trim,
@@ -47,7 +51,7 @@ StatDensity <- ggproto("StatDensity", Stat,
   required_aes = "x",
   default_aes = aes(y = ..density.., fill = NA),
 
-  compute_group = function(data, scales, adjust = 1, kernel = "gaussian",
+  compute_group = function(data, scales, bw = "nrd0", adjust = 1, kernel = "gaussian",
                            trim = FALSE, na.rm = FALSE) {
     if (trim) {
       range <- range(data$x, na.rm = TRUE)
@@ -56,7 +60,7 @@ StatDensity <- ggproto("StatDensity", Stat,
     }
 
     compute_density(data$x, data$weight, from = range[1], to = range[2],
-      adjust = adjust, kernel = kernel)
+      bw = bw, adjust = adjust, kernel = kernel)
   }
 
 )

R/stat-function.r

@@ -80,9 +80,17 @@ StatFunction <- ggproto("StatFunction", Stat,
     range <- scales$x$dimension()
     xseq <- seq(range[1], range[2], length.out = n)
 
+    if (scales$x$is_discrete()) {
+      x_trans <- xseq
+    } else {
+      # For continuous scales, need to back transform from transformed range
+      # to original values
+      x_trans <- scales$x$trans$inverse(xseq)
+    }
+
     data.frame(
       x = xseq,
-      y = do.call(fun, c(list(quote(scales$x$trans$inv(xseq))), args))
+      y = do.call(fun, c(list(quote(x_trans)), args))
     )
   }
 )

R/stat-summary.r

@@ -47,27 +47,18 @@
 #'
 #' # You can supply individual functions to summarise the value at
 #' # each x:
-#' d + stat_summary(fun.y = "median", colour = "red", size = 2)
-#' d + stat_summary(fun.y = "mean", colour = "red", size = 2)
+#' d + stat_summary(fun.y = "median", colour = "red", size = 2, geom = "point")
+#' d + stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
 #' d + aes(colour = factor(vs)) + stat_summary(fun.y = mean, geom="line")
 #'
 #' d + stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max,
 #'   colour = "red")
 #'
-#' #' d <- ggplot(diamonds, aes(carat, price))
-#' d + geom_smooth()
-#' d + geom_line(stat = "summary_bin", binwidth = 0.1, fun.y = "mean")
-#'
 #' d <- ggplot(diamonds, aes(cut))
 #' d + geom_bar()
 #' d + stat_summary_bin(aes(y = price), fun.y = "mean", geom = "bar")
-
-#' \donttest{
-#' # A set of useful summary functions is provided from the Hmisc package:
-#' stat_sum_df <- function(fun, geom="crossbar", ...) {
-#'   stat_summary(fun.data=fun, colour="red", geom=geom, width=0.2, ...)
-#' }
 #'
+#' \donttest{
 #' # Don't use ylim to zoom into a summary plot - this throws the
 #' # data away
 #' p <- ggplot(mtcars, aes(cyl, mpg)) +
@@ -77,28 +68,18 @@
 #' # Instead use coord_cartesian
 #' p + coord_cartesian(ylim = c(15, 30))
 #'
+#' # A set of useful summary functions is provided from the Hmisc package:
+#' stat_sum_df <- function(fun, geom="crossbar", ...) {
+#'   stat_summary(fun.data=fun, colour="red", geom=geom, width=0.2, ...)
+#' }
+#' d <- ggplot(mtcars, aes(cyl, mpg)) + geom_point()
 #' # The crossbar geom needs grouping to be specified when used with
 #' # a continuous x axis.
 #' d + stat_sum_df("mean_cl_boot", mapping = aes(group = cyl))
 #' d + stat_sum_df("mean_sdl", mapping = aes(group = cyl))
-#' d + stat_sum_df("mean_sdl", mult = 1, mapping = aes(group = cyl))
+#' d + stat_sum_df("mean_sdl", fun.args = list(mult = 1), mapping = aes(group = cyl))
 #' d + stat_sum_df("median_hilow", mapping = aes(group = cyl))
 #'
-#' # There are lots of different geoms you can use to display the summaries
-#'
-#' d + stat_sum_df("mean_cl_normal", mapping = aes(group = cyl))
-#' d + stat_sum_df("mean_cl_normal", geom = "errorbar")
-#' d + stat_sum_df("mean_cl_normal", geom = "pointrange")
-#' d + stat_sum_df("mean_cl_normal", geom = "smooth")
-#'
-#' # Summaries are more useful with a bigger data set:
-#' mpg2 <- subset(mpg, cyl != 5L)
-#' m <- ggplot(mpg2, aes(x=cyl, y=hwy)) +
-#'         geom_point() +
-#'         stat_summary(fun.data = "mean_sdl", geom = "linerange",
-#'                      colour = "red", size = 2, mult = 1) +
-#'        xlab("cyl")
-#' m
 #' # An example with highly skewed distributions:
 #' if (require("ggplot2movies")) {
 #' set.seed(596)

R/stat-ydensity.r

@@ -18,7 +18,7 @@
 #' @export
 #' @rdname geom_violin
 stat_ydensity <- function(mapping = NULL, data = NULL, geom = "violin",
-                          position = "dodge", adjust = 1, kernel = "gaussian",
+                          position = "dodge", bw = "nrd0", adjust = 1, kernel = "gaussian",
                           trim = TRUE, scale = "area", na.rm = FALSE,
                           show.legend = NA, inherit.aes = TRUE, ...) {
   scale <- match.arg(scale, c("area", "count", "width"))
@@ -32,6 +32,7 @@ stat_ydensity <- function(mapping = NULL, data = NULL, geom = "violin",
     show.legend = show.legend,
     inherit.aes = inherit.aes,
     params = list(
+      bw = bw,
       adjust = adjust,
       kernel = kernel,
       trim = trim,
@@ -51,7 +52,7 @@ StatYdensity <- ggproto("StatYdensity", Stat,
   required_aes = c("x", "y"),
   non_missing_aes = "weight",
 
-  compute_group = function(data, scales, width = NULL, adjust = 1,
+  compute_group = function(data, scales, width = NULL, bw = "nrd0", adjust = 1,
                        kernel = "gaussian", trim = TRUE, na.rm = FALSE) {
     if (nrow(data) < 3) return(data.frame())
 
@@ -61,7 +62,7 @@ StatYdensity <- ggproto("StatYdensity", Stat,
       range <- scales$y$dimension()
     }
     dens <- compute_density(data$y, data$w, from = range[1], to = range[2],
-      adjust = adjust, kernel = kernel)
+      bw = bw, adjust = adjust, kernel = kernel)
 
     dens$y <- dens$x
     dens$x <- mean(range(data$x))
@@ -75,11 +76,11 @@ StatYdensity <- ggproto("StatYdensity", Stat,
     dens
   },
 
-  compute_panel = function(self, data, scales, width = NULL, adjust = 1,
+  compute_panel = function(self, data, scales, width = NULL, bw = "nrd0", adjust = 1,
                            kernel = "gaussian", trim = TRUE, na.rm = FALSE,
                            scale = "area") {
     data <- ggproto_parent(Stat, self)$compute_panel(
-      data, scales, width = width, adjust = adjust, kernel = kernel,
+      data, scales, width = width, bw = bw, adjust = adjust, kernel = kernel,
       trim = trim, na.rm = na.rm
     )
 

R/theme-defaults.r

@@ -86,7 +86,7 @@ theme_grey <- function(base_size = 11, base_family = "") {
                          ),
     axis.title.y =       element_text(
                            angle = 90,
-                           margin = margin(r = 0.8 * half_line, l = 0.8 * half_line / 2),
+                           margin = margin(r = 0.8 * half_line, l = 0.8 * half_line / 2)
                          ),
 
     legend.background =  element_rect(colour = NA),