[SPARK-31568][R][DOCS] Add detail about func/key in gapply to documentation

Improve documentation for `gapply` in `SparkR`

Spent a long time this weekend trying to figure out just what exactly `key` is in `gapply`'s `func`. I had assumed it would be a _named_ list, but apparently not -- the examples are working because `schema` is applying the name and the names of the output `data.frame` don't matter.

As near as I can tell the description I've added is correct, namely, that `key` is an unnamed list.

No? Not in code. Only documentation.

Not. Documentation only

Closes #28350 from MichaelChirico/r-gapply-key-doc.

Authored-by: Michael Chirico <[email protected]>
Signed-off-by: HyukjinKwon <[email protected]>

Author: Michael Chirico

R/pkg/R/DataFrame.R

@@ -1657,9 +1657,7 @@ setMethod("dapplyCollect",
 #'
 #' @param cols grouping columns.
 #' @param func a function to be applied to each group partition specified by grouping
-#'             column of the SparkDataFrame. The function \code{func} takes as argument
-#'             a key - grouping columns and a data frame - a local R data.frame.
-#'             The output of \code{func} is a local R data.frame.
+#'             column of the SparkDataFrame. See Details.
 #' @param schema the schema of the resulting SparkDataFrame after the function is applied.
 #'               The schema must match to output of \code{func}. It has to be defined for each
 #'               output column with preferred output column name and corresponding data type.
@@ -1669,29 +1667,43 @@ setMethod("dapplyCollect",
 #' @aliases gapply,SparkDataFrame-method
 #' @rdname gapply
 #' @name gapply
+#' @details
+#' \code{func} is a function of two arguments. The first, usually named \code{key}
+#' (though this is not enforced) corresponds to the grouping key, will be an
+#' unnamed \code{list} of \code{length(cols)} length-one objects corresponding
+#' to the grouping columns' values for the current group.
+#'
+#' The second, herein \code{x}, will be a local \code{\link{data.frame}} with the
+#' columns of the input not in \code{cols} for the rows corresponding to \code{key}.
+#'
+#' The output of \code{func} must be a \code{data.frame} matching \code{schema} --
+#' in particular this means the names of the output \code{data.frame} are irrelevant
+#'
 #' @seealso \link{gapplyCollect}
 #' @examples
 #'
 #' \dontrun{
-#' Computes the arithmetic mean of the second column by grouping
-#' on the first and third columns. Output the grouping values and the average.
+#' # Computes the arithmetic mean of the second column by grouping
+#' # on the first and third columns. Output the grouping values and the average.
 #'
 #' df <- createDataFrame (
 #' list(list(1L, 1, "1", 0.1), list(1L, 2, "1", 0.2), list(3L, 3, "3", 0.3)),
 #'   c("a", "b", "c", "d"))
 #'
-#' Here our output contains three columns, the key which is a combination of two
-#' columns with data types integer and string and the mean which is a double.
+#' # Here our output contains three columns, the key which is a combination of two
+#' # columns with data types integer and string and the mean which is a double.
 #' schema <- structType(structField("a", "integer"), structField("c", "string"),
 #'   structField("avg", "double"))
 #' result <- gapply(
 #'   df,
 #'   c("a", "c"),
 #'   function(key, x) {
+#'     # key will either be list(1L, '1') (for the group where a=1L,c='1') or
+#'     #   list(3L, '3') (for the group where a=3L,c='3')
 #'     y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
 #' }, schema)
 #'
-#' The schema also can be specified in a DDL-formatted string.
+#' # The schema also can be specified in a DDL-formatted string.
 #' schema <- "a INT, c STRING, avg DOUBLE"
 #' result <- gapply(
 #'   df,
@@ -1700,8 +1712,8 @@ setMethod("dapplyCollect",
 #'     y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
 #' }, schema)
 #'
-#' We can also group the data and afterwards call gapply on GroupedData.
-#' For Example:
+#' # We can also group the data and afterwards call gapply on GroupedData.
+#' # For example:
 #' gdf <- group_by(df, "a", "c")
 #' result <- gapply(
 #'   gdf,
@@ -1710,15 +1722,15 @@ setMethod("dapplyCollect",
 #' }, schema)
 #' collect(result)
 #'
-#' Result
-#' ------
-#' a c avg
-#' 3 3 3.0
-#' 1 1 1.5
+#' # Result
+#' # ------
+#' # a c avg
+#' # 3 3 3.0
+#' # 1 1 1.5
 #'
-#' Fits linear models on iris dataset by grouping on the 'Species' column and
-#' using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
-#' and 'Petal_Width' as training features.
+#' # Fits linear models on iris dataset by grouping on the 'Species' column and
+#' # using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
+#' # and 'Petal_Width' as training features.
 #'
 #' df <- createDataFrame (iris)
 #' schema <- structType(structField("(Intercept)", "double"),
@@ -1734,12 +1746,12 @@ setMethod("dapplyCollect",
 #'   }, schema)
 #' collect(df1)
 #'
-#' Result
-#' ---------
-#' Model  (Intercept)  Sepal_Width  Petal_Length  Petal_Width
-#' 1        0.699883    0.3303370    0.9455356    -0.1697527
-#' 2        1.895540    0.3868576    0.9083370    -0.6792238
-#' 3        2.351890    0.6548350    0.2375602     0.2521257
+#' # Result
+#' # ---------
+#' # Model  (Intercept)  Sepal_Width  Petal_Length  Petal_Width
+#' # 1        0.699883    0.3303370    0.9455356    -0.1697527
+#' # 2        1.895540    0.3868576    0.9083370    -0.6792238
+#' # 3        2.351890    0.6548350    0.2375602     0.2521257
 #'
 #'}
 #' @note gapply(SparkDataFrame) since 2.0.0
@@ -1757,20 +1769,30 @@ setMethod("gapply",
 #'
 #' @param cols grouping columns.
 #' @param func a function to be applied to each group partition specified by grouping
-#'             column of the SparkDataFrame. The function \code{func} takes as argument
-#'             a key - grouping columns and a data frame - a local R data.frame.
-#'             The output of \code{func} is a local R data.frame.
+#'             column of the SparkDataFrame. See Details.
 #' @return A data.frame.
 #' @family SparkDataFrame functions
 #' @aliases gapplyCollect,SparkDataFrame-method
 #' @rdname gapplyCollect
 #' @name gapplyCollect
+#' @details
+#' \code{func} is a function of two arguments. The first, usually named \code{key}
+#' (though this is not enforced) corresponds to the grouping key, will be an
+#' unnamed \code{list} of \code{length(cols)} length-one objects corresponding
+#' to the grouping columns' values for the current group.
+#'
+#' The second, herein \code{x}, will be a local \code{\link{data.frame}} with the
+#' columns of the input not in \code{cols} for the rows corresponding to \code{key}.
+#'
+#' The output of \code{func} must be a \code{data.frame} matching \code{schema} --
+#' in particular this means the names of the output \code{data.frame} are irrelevant
+#'
 #' @seealso \link{gapply}
 #' @examples
 #'
 #' \dontrun{
-#' Computes the arithmetic mean of the second column by grouping
-#' on the first and third columns. Output the grouping values and the average.
+#' # Computes the arithmetic mean of the second column by grouping
+#' # on the first and third columns. Output the grouping values and the average.
 #'
 #' df <- createDataFrame (
 #' list(list(1L, 1, "1", 0.1), list(1L, 2, "1", 0.2), list(3L, 3, "3", 0.3)),
@@ -1785,8 +1807,8 @@ setMethod("gapply",
 #'     y
 #'   })
 #'
-#' We can also group the data and afterwards call gapply on GroupedData.
-#' For Example:
+#' # We can also group the data and afterwards call gapply on GroupedData.
+#' # For example:
 #' gdf <- group_by(df, "a", "c")
 #' result <- gapplyCollect(
 #'   gdf,
@@ -1796,15 +1818,15 @@ setMethod("gapply",
 #'     y
 #'   })
 #'
-#' Result
-#' ------
-#' key_a key_c mean_b
-#' 3 3 3.0
-#' 1 1 1.5
+#' # Result
+#' # ------
+#' # key_a key_c mean_b
+#' # 3 3 3.0
+#' # 1 1 1.5
 #'
-#' Fits linear models on iris dataset by grouping on the 'Species' column and
-#' using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
-#' and 'Petal_Width' as training features.
+#' # Fits linear models on iris dataset by grouping on the 'Species' column and
+#' # using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
+#' # and 'Petal_Width' as training features.
 #'
 #' df <- createDataFrame (iris)
 #' result <- gapplyCollect(
@@ -1816,12 +1838,12 @@ setMethod("gapply",
 #'     data.frame(t(coef(m)))
 #'   })
 #'
-#' Result
-#'---------
-#' Model  X.Intercept.  Sepal_Width  Petal_Length  Petal_Width
-#' 1        0.699883    0.3303370    0.9455356    -0.1697527
-#' 2        1.895540    0.3868576    0.9083370    -0.6792238
-#' 3        2.351890    0.6548350    0.2375602     0.2521257
+#' # Result
+#' # ---------
+#' # Model  X.Intercept.  Sepal_Width  Petal_Length  Petal_Width
+#' # 1        0.699883    0.3303370    0.9455356    -0.1697527
+#' # 2        1.895540    0.3868576    0.9083370    -0.6792238
+#' # 3        2.351890    0.6548350    0.2375602     0.2521257
 #'
 #'}
 #' @note gapplyCollect(SparkDataFrame) since 2.0.0