Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/13798#discussion_r67813477
--- Diff: R/pkg/R/DataFrame.R ---
@@ -606,10 +607,10 @@ setMethod("unpersist",
#'
#' The following options for repartition are possible:
#' \itemize{
-#' \item{"Option 1"} {Return a new SparkDataFrame partitioned by
+#' \item{1.} {Return a new SparkDataFrame partitioned by
#' the given columns into `numPartitions`.}
-#' \item{"Option 2"} {Return a new SparkDataFrame that has exactly
`numPartitions`.}
-#' \item{"Option 3"} {Return a new SparkDataFrame partitioned by the
given column(s),
+#' \item{2.} {Return a new SparkDataFrame that has exactly
`numPartitions`.}
+#' \item{3.} {Return a new SparkDataFrame partitioned by the given
column(s),
#' using `spark.sql.shuffle.partitions` as number of
partitions.}
--- End diff --
For the ordered itemize, what about the following?
```
#' \enumerate{
#' \item Return a new SparkDataFrame partitioned by the given columns into
`numPartitions`.
#' \item Return a new SparkDataFrame that has exactly `numPartitions`.
#' \item Return a new SparkDataFrame partitioned by the given column(s),
#' using `spark.sql.shuffle.partitions` as number of
partitions.
#' }
```
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