Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/9218#discussion_r42884395
--- Diff: R/pkg/R/DataFrame.R ---
@@ -276,6 +276,57 @@ setMethod("names<-",
}
})
+#' @rdname columns
+#' @name colnames
+setMethod("colnames",
+ signature(x = "DataFrame"),
+ function(x) {
+ columns(x)
+ })
+
+#' @rdname columns
+#' @name colnames<-
+setMethod("colnames<-",
+ signature(x = "DataFrame", value = "character"),
+ function(x, value) {
+ sdf <- callJMethod(x@sdf, "toDF", as.list(value))
+ dataFrame(sdf)
+ })
+
+#' coltypes
+#'
+#' Set the column types of a DataFrame.
+#'
+#' @name coltypes
+#' @param x (DataFrame)
+#' @return value (character) A character vector with the target column
types for the given DataFrame
+#' @rdname coltypes
+#' @aliases coltypes
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' path <- "path/to/file.json"
+#' df <- jsonFile(sqlContext, path)
+#' coltypes(df) <- c("string", "integer")
+#'}
+setMethod("coltypes<-",
--- End diff --
So this is a little tricky. In #8984 we are converting the SparkSQL types
to R types. So in that case for consistency we should take in R types here (i.e
character, numeric etc.) and convert them to SparkSQL types
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