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Groups the SparkDataFrame using the specified columns, applies the R function to each group and collects the result back to R as data.frame.

Usage

gapplyCollect(x, ...)

# S4 method for GroupedData
gapplyCollect(x, func)

# S4 method for SparkDataFrame
gapplyCollect(x, cols, func)

Arguments

x

a SparkDataFrame or GroupedData.

...

additional argument(s) passed to the method.

func

a function to be applied to each group partition specified by grouping column of the SparkDataFrame. See Details.

cols

grouping columns.

Value

A data.frame.

Details

func is a function of two arguments. The first, usually named key (though this is not enforced) corresponds to the grouping key, will be an unnamed list of length(cols) length-one objects corresponding to the grouping columns' values for the current group.

The second, herein x, will be a local data.frame with the columns of the input not in cols for the rows corresponding to key.

The output of func must be a data.frame matching schema -- in particular this means the names of the output data.frame are irrelevant

Note

gapplyCollect(GroupedData) since 2.0.0

gapplyCollect(SparkDataFrame) since 2.0.0

Examples


if (FALSE) {
# 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"))

result <- gapplyCollect(
  df,
  c("a", "c"),
  function(key, x) {
    y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
    colnames(y) <- c("key_a", "key_c", "mean_b")
    y
  })

# We can also group the data and afterwards call gapply on GroupedData.
# For example:
gdf <- group_by(df, "a", "c")
result <- gapplyCollect(
  gdf,
  function(key, x) {
    y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
    colnames(y) <- c("key_a", "key_c", "mean_b")
    y
  })

# 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.

df <- createDataFrame (iris)
result <- gapplyCollect(
  df,
  df$"Species",
  function(key, x) {
    m <- suppressWarnings(lm(Sepal_Length ~
    Sepal_Width + Petal_Length + Petal_Width, x))
    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

}