Allow rlang-style lambda expressions in stat_summary functions (#3569)

Author: dkahle

NEWS.md

@@ -118,6 +118,10 @@
   the limits of the scale and ignore the order of any `breaks` provided. Note
   that this may change the appearance of plots that previously relied on the
   unordered behaviour (#2429, @idno0001).
+  
+* `stat_summary()` and related functions now support rlang-style lambda functions
+  (#3568, @dkahle).
+
 
 # ggplot2 3.2.1
 

R/stat-summary-2d.r

@@ -30,11 +30,13 @@
 #'
 #' # Specifying function
 #' d + stat_summary_2d(fun = function(x) sum(x^2))
+#' d + stat_summary_2d(fun = ~ sum(.x^2))
 #' d + stat_summary_2d(fun = var)
 #' d + stat_summary_2d(fun = "quantile", fun.args = list(probs = 0.1))
 #'
 #' if (requireNamespace("hexbin")) {
 #' d + stat_summary_hex()
+#' d + stat_summary_hex(fun = ~ sum(.x^2))
 #' }
 stat_summary_2d <- function(mapping = NULL, data = NULL,
                             geom = "tile", position = "identity",
@@ -98,6 +100,7 @@ StatSummary2d <- ggproto("StatSummary2d", Stat,
     xbin <- cut(data$x, xbreaks, include.lowest = TRUE, labels = FALSE)
     ybin <- cut(data$y, ybreaks, include.lowest = TRUE, labels = FALSE)
 
+    fun <- as_function(fun)
     f <- function(x) {
       do.call(fun, c(list(quote(x)), fun.args))
     }

R/stat-summary-bin.R

@@ -96,7 +96,7 @@ make_summary_fun <- function(fun.data, fun, fun.max, fun.min, fun.args) {
 
   if (!is.null(fun.data)) {
     # Function that takes complete data frame as input
-    fun.data <- match.fun(fun.data)
+    fun.data <- as_function(fun.data)
     function(df) {
       do.call(fun.data, c(list(quote(df$y)), fun.args))
     }
@@ -105,6 +105,7 @@ make_summary_fun <- function(fun.data, fun, fun.max, fun.min, fun.args) {
 
     call_f <- function(fun, x) {
       if (is.null(fun)) return(NA_real_)
+      fun <- as_function(fun)
       do.call(fun, c(list(quote(x)), fun.args))
     }
 
@@ -116,7 +117,7 @@ make_summary_fun <- function(fun.data, fun, fun.max, fun.min, fun.args) {
       ))
     }
   } else {
-    message("No summary function supplied, defaulting to `mean_se()")
+    message("No summary function supplied, defaulting to `mean_se()`")
     function(df) {
       mean_se(df$y)
     }

R/stat-summary-hex.r

@@ -46,6 +46,7 @@ StatSummaryHex <- ggproto("StatSummaryHex", Stat,
     try_require("hexbin", "stat_summary_hex")
 
     binwidth <- binwidth %||% hex_binwidth(bins, scales)
+    fun <- as_function(fun)
     hexBinSummarise(data$x, data$y, data$z, binwidth,
       fun = fun, fun.args = fun.args, drop = drop)
   }

man/stat_summary_2d.Rd

@@ -0,0 +1,84 @@
+context("stat_summary")
+
+test_that("stat_summary(_bin) work with lambda expressions", {
+  # note: stat_summary and stat_summary_bin both use
+  # make_summary_fun, so this tests both
+
+  dat <- data_frame(
+    x = c(1, 1, 2, 2, 3, 3),
+    y = c(0, 2, 1, 3, 2, 4)
+  )
+
+  p1 <- ggplot(dat, aes(x, y)) +
+    stat_summary(fun.data = mean_se)
+
+
+  # test fun.data
+  p2 <- ggplot(dat, aes(x, y)) +
+    stat_summary(fun.data = ~ {
+      mean <- mean(.x)
+      se <- sqrt(stats::var(.x) / length(.x))
+      data_frame(y = mean, ymin = mean - se, ymax = mean + se)
+    })
+
+  expect_equal(
+    layer_data(p1),
+    layer_data(p2)
+  )
+
+
+  # fun, fun.min, fun.max
+  p3 <- ggplot(dat, aes(x, y)) +
+    stat_summary(
+      fun = ~ mean(.x),
+      fun.min = ~ mean(.x) - sqrt(stats::var(.x) / length(.x)),
+      fun.max = ~ mean(.x) + sqrt(stats::var(.x) / length(.x))
+    )
+
+  expect_equal(
+    layer_data(p1),
+    layer_data(p3)
+  )
+
+})
+
+
+
+
+test_that("stat_summary_(2d|hex) work with lambda expressions", {
+
+  dat <- data_frame(
+    x = c(0, 0, 0, 0, 1, 1, 1, 1),
+    y = c(0, 0, 1, 1, 0, 0, 1, 1),
+    z = c(1, 1, 2, 2, 2, 2, 3, 3)
+  )
+
+
+  # stat_summary_2d
+  p1 <- ggplot(dat, aes(x, y, z = z)) +
+    stat_summary_2d(fun = function(x) mean(x))
+
+  p2 <- ggplot(dat, aes(x, y, z = z)) +
+    stat_summary_2d(fun = ~ mean(.x))
+
+  expect_equal(
+    layer_data(p1),
+    layer_data(p2)
+  )
+
+
+
+  # stat_summary_hex
+  # this plot is a bit funky, but easy to reason through
+  p1 <- ggplot(dat, aes(x, y, z = z)) +
+    stat_summary_hex(fun = function(x) mean(x))
+
+  p2 <- ggplot(dat, aes(x, y, z = z)) +
+    stat_summary_hex(fun = ~ mean(.x))
+
+  expect_equal(
+    layer_data(p1),
+    layer_data(p2)
+  )
+
+})