gapplyCollect

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 class 'GroupedData'
gapplyCollect(x, func)

# S4 method for class '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

Other SparkDataFrame functions: SparkDataFrame-class, agg(), alias(), arrange(), as.data.frame(), attach,SparkDataFrame-method, broadcast(), cache(), checkpoint(), coalesce(), collect(), colnames(), coltypes(), createOrReplaceTempView(), crossJoin(), cube(), dapplyCollect(), dapply(), describe(), dim(), distinct(), dropDuplicates(), dropna(), drop(), dtypes(), exceptAll(), except(), explain(), filter(), first(), gapply(), getNumPartitions(), group_by(), head(), hint(), histogram(), insertInto(), intersectAll(), intersect(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), randomSplit(), rbind(), rename(), repartitionByRange(), repartition(), rollup(), sample(), saveAsTable(), schema(), selectExpr(), select(), showDF(), show(), storageLevel(), str(), subset(), summary(), take(), toJSON(), unionAll(), unionByName(), union(), unpersist(), unpivot(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples


if (FALSE) { # \dontrun{
# 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

} # }