Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/9029#discussion_r41592919
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1854,30 +1784,36 @@ setMethod("fillna",
sdf <- if (length(cols) == 0) {
callJMethod(naFunctions, "fill", value)
} else {
- callJMethod(naFunctions, "fill", value, as.list(cols))
+ callJMethod(naFunctions, "fill", value,
listToSeq(as.list(cols)))
}
dataFrame(sdf)
})
-#' This function downloads the contents of a DataFrame into an R's
data.frame.
-#' Since data.frames are held in memory, ensure that you have enough memory
-#' in your system to accommodate the contents.
+#' crosstab
#'
-#' @title Download data from a DataFrame into a data.frame
-#' @param x a DataFrame
-#' @return a data.frame
-#' @rdname as.data.frame
-#' @examples \dontrun{
+#' Computes a pair-wise frequency table of the given columns. Also known
as a contingency
+#' table. The number of distinct values for each column should be less
than 1e4. At most 1e6
+#' non-zero pair frequencies will be returned.
#'
-#' irisDF <- createDataFrame(sqlContext, iris)
-#' df <- as.data.frame(irisDF[irisDF$Species == "setosa", ])
+#' @param col1 name of the first column. Distinct items will make the
first item of each row.
+#' @param col2 name of the second column. Distinct items will make the
column names of the output.
+#' @return a local R data.frame representing the contingency table. The
first column of each row
+#' will be the distinct values of `col1` and the column names will
be the distinct values
+#' of `col2`. The name of the first column will be `$col1_$col2`.
Pairs that have no
+#' occurrences will have zero as their counts.
+#'
+#' @rdname statfunctions
+#' @name crosstab
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- jsonFile(sqlCtx, "/path/to/file.json")
+#' ct = crosstab(df, "title", "gender")
#' }
-setMethod("as.data.frame",
--- End diff --
also for this from a recent PR
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