Github user olarayej commented on a diff in the pull request:
https://github.com/apache/spark/pull/8984#discussion_r41321101
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1881,3 +1881,31 @@ setMethod("as.data.frame",
collect(x)
}
)
+
+#' Returns the column types of a DataFrame.
+#'
+#' @name coltypes
+#' @title Get column types of a DataFrame
+#' @param x (DataFrame)
+#' @return value (character) A character vector with the column types of
the given DataFrame
+#' @rdname coltypes
+setMethod("coltypes",
+ signature(x = "DataFrame"),
+ function(x) {
+ # TODO: This may be moved as a global parameter
+ # These are the supported data types and how they map to
+ # R's data types
+ DATA_TYPES <- c("string"="character",
+ "double"="numeric",
+ "int"="integer",
+ "long"="integer",
+ "boolean"="long"
+ )
+
+ # Get the data types of the DataFrame by invoking dtypes()
function.
+ # Some post-processing is needed.
+ types <- as.character(t(as.data.frame(dtypes(x))[2, ]))
+
+ # Map Spark data types into R's data types
+ as.character(DATA_TYPES[types])
--- End diff --
@felixcheung Yeah, that's a good point. I'm thinking coltypes() should
always have an equivalent R data type for each column. We don't want method
coltypes() to return NA's or throw an unsupported-type error cuz that would
mean that the input DataFrame is inconsistent.
Therefore, it'd be just a matter of putting in DATA_TYPES, the list all
possible values returned by dtypes() (If I'm missing any). I couldn't find that
in the docs. Could you point me to the list?
Finally, I think the check for unsupported data types should be done
instead in the coltypes()<- method and in the DataFrame initialization.
coltypes() assumes the input DataFrame was assigned valid data types, which
makes sense to me.
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