[SPARK-32949][R][SQL] Add timestamp_seconds to SparkR

### What changes were proposed in this pull request?

This PR adds R wrapper for `timestamp_seconds` function.

### Why are the changes needed?

Feature parity.

### Does this PR introduce _any_ user-facing change?

Yes, it adds a new R function.

### How was this patch tested?

New unit tests.

Closes #29822 from zero323/SPARK-32949.

Authored-by: zero323 <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>

Author: zero323

R/pkg/NAMESPACE

@@ -405,6 +405,7 @@ exportMethods("%<=>%",
               "sumDistinct",
               "tan",
               "tanh",
+              "timestamp_seconds",
               "toDegrees",
               "toRadians",
               "to_csv",

R/pkg/R/functions.R

@@ -4407,3 +4407,18 @@ setMethod("current_timestamp",
             jc <- callJStatic("org.apache.spark.sql.functions", "current_timestamp")
             column(jc)
           })
+
+#' @details
+#' \code{timestamp_seconds}: Creates timestamp from the number of seconds since UTC epoch.
+#'
+#' @rdname column_datetime_functions
+#' @aliases timestamp_seconds timestamp_seconds,Column-method
+#' @note timestamp_seconds since 3.1.0
+setMethod("timestamp_seconds",
+    signature(x = "Column"),
+    function(x) {
+        jc <- callJStatic(
+            "org.apache.spark.sql.functions", "timestamp_seconds", x@jc
+        )
+        column(jc)
+    })

R/pkg/R/generics.R

@@ -1357,6 +1357,10 @@ setGeneric("substring_index", function(x, delim, count) { standardGeneric("subst
 #' @name NULL
 setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") })
 
+#' @rdname column_datetime_functions
+#' @name timestamp_seconds
+setGeneric("timestamp_seconds", function(x) { standardGeneric("timestamp_seconds") })
+
 #' @rdname column_collection_functions
 #' @name NULL
 setGeneric("transform_keys", function(x, f) {  standardGeneric("transform_keys") })

R/pkg/tests/fulltests/test_sparkSQL.R

@@ -1424,6 +1424,7 @@ test_that("column functions", {
     date_trunc("quarter", c) + current_date() + current_timestamp()
   c25 <- overlay(c1, c2, c3, c3) + overlay(c1, c2, c3) + overlay(c1, c2, 1) +
     overlay(c1, c2, 3, 4)
+  c26 <- timestamp_seconds(c1)
 
   # Test if base::is.nan() is exposed
   expect_equal(is.nan(c("a", "b")), c(FALSE, FALSE))