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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