SparkR (version 2.4.6)

group_by: GroupBy

Description

Groups the SparkDataFrame using the specified columns, so we can run aggregation on them.

Usage

group_by(x, ...)
groupBy(x, ...)
# S4 method for SparkDataFrame
groupBy(x, ...)
# S4 method for SparkDataFrame
group_by(x, ...)

Arguments

x

a SparkDataFrame.

...

character name(s) or Column(s) to group on.

Value

A GroupedData.

See Also

agg, cube, rollup

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(), gapplyCollect(), gapply(), getNumPartitions(), 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 {
  # Compute the average for all numeric columns grouped by department.
  avg(groupBy(df, "department"))

  # Compute the max age and average salary, grouped by department and gender.
  agg(groupBy(df, "department", "gender"), salary="avg", "age" -> "max")
# }