Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/8984#discussion_r41596831
--- 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 --
http://spark.apache.org/docs/latest/sql-programming-guide.html#data-types
is a list that might be helpful.
Also I think it might make sense to try and map them to R types and if we
fail to find a relevant one we fallback to the SparkSQL type
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