zero323 commented on a change in pull request #27278:
[SPARK-30569][SQL][PYSPARK][SPARKR] Add percentile_approx DSL functions.
URL: https://github.com/apache/spark/pull/27278#discussion_r368307557
##########
File path: R/pkg/tests/fulltests/test_sparkSQL.R
##########
@@ -1762,6 +1762,28 @@ test_that("column functions", {
collect(select(df, alias(not(df$is_true), "is_false"))),
data.frame(is_false = c(FALSE, TRUE, NA))
)
+
+ # Test percentile_approx
+ actual <- lapply(
+ list(
+ percentile_approx(column("foo"), 0.5),
+ percentile_approx(column("bar"), 0.25, 42L),
+ percentile_approx(column("bar"), c(0.25, 0.5, 0.75)),
+ percentile_approx(column("foo"), c(0.05, 0.95), 100L),
+ percentile_approx("foo", 0.5),
+ percentile_approx("bar", c(0.1, 0.9), 10L)),
+ function(x) SparkR:::callJMethod(x@jc, "toString"))
+
+ expected <- list(
+ "percentile_approx(foo, 0.5, 10000)",
+ "percentile_approx(bar, 0.25, 42)",
+ "percentile_approx(bar, [0.25,0.5,0.75], 10000)",
+ "percentile_approx(foo, [0.05,0.95], 100)",
+ "percentile_approx(foo, 0.5, 10000)",
+ "percentile_approx(bar, [0.1,0.9], 10)"
+ )
+
+ exepct_equal(actual, expected)
Review comment:
This should be sufficient, as the main concern is if data is properly passed
to JVM.
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