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From felixcheung <>
Subject [GitHub] spark pull request #13786: [SPARK-15294][R] Add `pivot` to SparkR
Date Tue, 21 Jun 2016 01:55:22 GMT
Github user felixcheung commented on a diff in the pull request:
    --- Diff: R/pkg/R/group.R ---
    @@ -129,6 +129,48 @@ methods <- c("avg", "max", "mean", "min", "sum")
     # These are not exposed on GroupedData: "kurtosis", "skewness", "stddev", "stddev_samp",
     # "variance", "var_samp", "var_pop"
    +#' Pivot a column of the GroupedData and perform the specified aggregation.
    +#' Pivot a column of the GroupedData and perform the specified aggregation.
    +#' There are two versions of pivot function: one that requires the caller to specify
the list
    +#' of distinct values to pivot on, and one that does not. The latter is more concise
but less
    +#' efficient, because Spark needs to first compute the list of distinct values internally.
    +#' @param x a GroupedData object
    +#' @param colname A column name
    +#' @param values A value or a list/vector of distinct values for the output columns.
    +#' @return GroupedData object
    +#' @rdname pivot
    +#' @name pivot
    +#' @export
    +#' @examples
    +#' \dontrun{
    +#' df <- createDataFrame(data.frame(
    +#'     earnings = c(10000, 10000, 11000, 15000, 12000, 20000, 21000, 22000),
    +#'     course = c("R", "Python", "R", "Python", "R", "Python", "R", "Python"),
    +#'     year = c(2013, 2013, 2014, 2014, 2015, 2015, 2016, 2016)
    +#' ))
    +#' collect(sum(pivot(groupBy(df, "year"), "course"), "earnings"))
    +#' collect(sum(pivot(groupBy(df, "year"), "course", "R"), "earnings"))
    +#' collect(sum(pivot(groupBy(df, "year"), "course", c("Python", "R")), "earnings"))
    +#' collect(sum(pivot(groupBy(df, "year"), "course", list("Python", "R")), "earnings"))
    --- End diff --
    nit: I think maybe we should vary the examples a bit? Perhaps to avoid promoting collect()
if possible, either leave it at `pivot(....)` or set to a variable `group_sum <- sum(pivot(...))`

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