gapplyCollect

Description

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 signature 'GroupedData'
gapplyCollect(x, func)

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

Arguments

Value

A data.frame.

Note

gapplyCollect(GroupedData) since 2.0.0

gapplyCollect(SparkDataFrame) since 2.0.0

See Also

gapply

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, except, explain, filter, first, gapply, getNumPartitions, group_by, head, hint, histogram, insertInto, intersect, isLocal, isStreaming, join, limit, localCheckpoint, merge, mutate, ncol, nrow, persist, printSchema, randomSplit, rbind, registerTempTable, rename, repartition, rollup, sample, saveAsTable, schema, selectExpr, select, showDF, show, storageLevel, str, subset, summary, take, toJSON, unionByName, union, unpersist, withColumn, withWatermark, with, write.df, write.jdbc, write.json, write.orc, write.parquet, write.stream, write.text

Examples

## Not run: 
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


## End(Not run)