dapply

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dapply

Apply a function to each partition of a SparkDataFrame.

Usage
dapply(x, func, schema)

# S4 method for SparkDataFrame,`function`,characterOrstructType dapply(x, func, schema)

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.

schema

The schema of the resulting SparkDataFrame after the function is applied. It must match the output of func. Since Spark 2.3, the DDL-formatted string is also supported for the schema.

Note

dapply since 2.0.0

See Also

dapplyCollect

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(), 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(), unionByName(), union(), unpersist(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Aliases
  • dapply
  • dapply,SparkDataFrame,function,characterOrstructType-method
Examples
# NOT RUN {
  df <- createDataFrame(iris)
  df1 <- dapply(df, function(x) { x }, schema(df))
  collect(df1)

  # filter and add a column
  df <- createDataFrame(
          list(list(1L, 1, "1"), list(2L, 2, "2"), list(3L, 3, "3")),
          c("a", "b", "c"))
  schema <- structType(structField("a", "integer"), structField("b", "double"),
                     structField("c", "string"), structField("d", "integer"))
  df1 <- dapply(
           df,
           function(x) {
             y <- x[x[1] > 1, ]
             y <- cbind(y, y[1] + 1L)
           },
           schema)

  # The schema also can be specified in a DDL-formatted string.
  schema <- "a INT, d DOUBLE, c STRING, d INT"
  df1 <- dapply(
           df,
           function(x) {
             y <- x[x[1] > 1, ]
             y <- cbind(y, y[1] + 1L)
           },
           schema)

  collect(df1)
  # the result
  #       a b c d
  #     1 2 2 2 3
  #     2 3 3 3 4
# }
Documentation reproduced from package SparkR, version 2.4.6, License: Apache License (== 2.0)

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