dapply {SparkR}R Documentation

dapply

Description

Apply a function to each partition of a SparkDataFrame.

Usage

dapply(x, func, schema)

## S4 method for signature '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()

Examples

## Not run: 
##D   df <- createDataFrame(iris)
##D   df1 <- dapply(df, function(x) { x }, schema(df))
##D   collect(df1)
##D 
##D   # filter and add a column
##D   df <- createDataFrame(
##D           list(list(1L, 1, "1"), list(2L, 2, "2"), list(3L, 3, "3")),
##D           c("a", "b", "c"))
##D   schema <- structType(structField("a", "integer"), structField("b", "double"),
##D                      structField("c", "string"), structField("d", "integer"))
##D   df1 <- dapply(
##D            df,
##D            function(x) {
##D              y <- x[x[1] > 1, ]
##D              y <- cbind(y, y[1] + 1L)
##D            },
##D            schema)
##D 
##D   # The schema also can be specified in a DDL-formatted string.
##D   schema <- "a INT, d DOUBLE, c STRING, d INT"
##D   df1 <- dapply(
##D            df,
##D            function(x) {
##D              y <- x[x[1] > 1, ]
##D              y <- cbind(y, y[1] + 1L)
##D            },
##D            schema)
##D 
##D   collect(df1)
##D   # the result
##D   #       a b c d
##D   #     1 2 2 2 3
##D   #     2 3 3 3 4
## End(Not run)

[Package SparkR version 2.4.5 Index]