[GitHub] [arrow] nealrichardson commented on a change in pull request #9875: ARROW-11338: [R] Bindings for quantile and median [WIP]

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nealrichardson commented on a change in pull request #9875:
URL: https://github.com/apache/arrow/pull/9875#discussion_r606352305



##########
File path: r/NAMESPACE
##########
@@ -308,6 +311,8 @@ importFrom(rlang,seq2)
 importFrom(rlang,set_names)
 importFrom(rlang,syms)
 importFrom(rlang,warn)
+importFrom(stats,median)
+importFrom(stats,quantile)

Review comment:
       Does this require that we add `stats` to `Imports:` in DESCRIPTION? 

##########
File path: r/R/compute.R
##########
@@ -80,6 +80,36 @@ collect_arrays_from_dots <- function(dots) {
   ChunkedArray$create(!!!arrays)
 }
 
+#' @export
+quantile.ArrowDatum <- function(x,
+                                probs = seq(0, 1, 0.25),
+                                na.rm = FALSE,
+                                interpolation = c("linear", "lower", "higher", "nearest", "midpoint"),
+                                ...) {
+  if (inherits(x, "Scalar")) x <- Array$create(x)
+  assert_is(probs, c("numeric", "integer"))
+  assert_that(length(probs) > 0)
+  assert_that(all(probs >= 0 & probs <= 1))
+  if (!na.rm && TRUE %in% as.vector(unique(is.na(x)))) {
+    stop("Missing values not allowed if 'na.rm' is FALSE", call. = FALSE)

Review comment:
       Why doesn't this just return an Array of NA, like `median` does?
   
   Is this a missing feature on the C++ side (needing a JIRA)? 

##########
File path: r/R/compute.R
##########
@@ -80,6 +80,36 @@ collect_arrays_from_dots <- function(dots) {
   ChunkedArray$create(!!!arrays)
 }
 
+#' @export
+quantile.ArrowDatum <- function(x,
+                                probs = seq(0, 1, 0.25),
+                                na.rm = FALSE,
+                                interpolation = c("linear", "lower", "higher", "nearest", "midpoint"),
+                                ...) {
+  if (inherits(x, "Scalar")) x <- Array$create(x)
+  assert_is(probs, c("numeric", "integer"))
+  assert_that(length(probs) > 0)
+  assert_that(all(probs >= 0 & probs <= 1))
+  if (!na.rm && TRUE %in% as.vector(unique(is.na(x)))) {

Review comment:
       Compare to `scalar_aggregate()` above in this file:
   
   ```suggestion
     if (!na.rm && x$null_count > 0) {
   ```




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