SparkR (version 3.1.2)

dapplyCollect: dapplyCollect

Description

Apply a function to each partition of a SparkDataFrame and collect the result back to R as a data.frame.

Usage

dapplyCollect(x, func)
# S4 method for SparkDataFrame,`function`
dapplyCollect(x, func)

Arguments

x

A SparkDataFrame

func

A function to be applied to each partition of the SparkDataFrame. func should have only one parameter, to which a R data.frame corresponds to each partition will be passed. The output of func should be a R data.frame.

See Also

dapply

Other SparkDataFrame functions: SparkDataFrame-class, agg(), alias(), arrange(), as.data.frame(), attach,SparkDataFrame-method, broadcast(), cache(), checkpoint(), coalesce(), collect(), colnames(), coltypes(), createOrReplaceTempView(), crossJoin(), cube(), dapply(), describe(), dim(), distinct(), dropDuplicates(), dropna(), drop(), dtypes(), exceptAll(), except(), explain(), filter(), first(), gapplyCollect(), 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(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples

# NOT RUN {
  df <- createDataFrame(iris)
  ldf <- dapplyCollect(df, function(x) { x })

  # filter and add a column
  df <- createDataFrame(
          list(list(1L, 1, "1"), list(2L, 2, "2"), list(3L, 3, "3")),
          c("a", "b", "c"))
  ldf <- dapplyCollect(
           df,
           function(x) {
             y <- x[x[1] > 1, ]
             y <- cbind(y, y[1] + 1L)
           })
  # the result
  #       a b c d
  #       2 2 2 3
  #       3 3 3 4
# }