Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/9218#discussion_r43698343
--- Diff: R/pkg/R/DataFrame.R ---
@@ -276,6 +276,75 @@ 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)
+ })
+
+rToScalaTypes <- new.env()
+rToScalaTypes[["integer"]] <- "integer" # in R, integer is 32bit
+rToScalaTypes[["numeric"]] <- "double" # in R, numeric == double which
is 64bit
+rToScalaTypes[["double"]] <- "double"
+rToScalaTypes[["character"]] <- "string"
+rToScalaTypes[["logical"]] <- "boolean"
+
+#' 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. Column types can be one of integer, numeric/double,
character, logical, or NA
+#' to keep that column as-is.
+#' @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("character", "integer")
+#' coltypes(df) <- c(NA, "numeric")
+#'}
+setMethod("coltypes<-",
+ signature(x = "DataFrame", value = "character"),
+ function(x, value) {
+ cols <- columns(x)
+ ncols <- length(cols)
+ if (length(value) == 0 || length(value) != ncols) {
--- End diff --
It could do that. I would rather be concise even if only a few more lines
because
- it's already checking for length == 0
- not sure we could create a DataFrame with 0 col from R
- no point to set column data type on a DataFrame having no column
Thought?
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