Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17674#discussion_r112106399
--- Diff: R/pkg/R/functions.R ---
@@ -3652,3 +3652,56 @@ setMethod("posexplode",
jc <- callJStatic("org.apache.spark.sql.functions",
"posexplode", x@jc)
column(jc)
})
+
+#' create_array
+#'
+#' Creates a new array column. The input columns must all have the same
data type.
+#'
+#' @param x Column to compute on
+#' @param ... other columns
+#'
+#' @family collection_funcs
+#' @rdname create_array
+#' @name create_array
+#' @aliases create_array,Column-method
+#' @export
+#' @examples \dontrun{create_array(df$x, df$y, df$z)}
+#' @note create_array since 2.3.0
+setMethod("create_array",
+ signature(x = "Column"),
+ function(x, ...) {
+ jcols <- lapply(list(x, ...), function (x) {
+ stopifnot(class(x) == "Column")
+ x@jc
+ })
+ jc <- callJStatic("org.apache.spark.sql.functions", "array",
jcols)
+ column(jc)
+ })
+
+#' create_map
+#'
+#' Creates a new map column. The input columns must be grouped as
key-value pairs,
+#' e.g. (key1, value1, key2, value2, ...).
+#' The key columns must all have the same data type, and can't be null.
--- End diff --
`null` in JVM is mapped to NA in R - we haven't documented that
consistently, but would be good to start thinking about the better way to do
that
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