GroupBy

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

Usage

group_by(x, ...)

groupBy(x, ...)

# S4 method for class 'SparkDataFrame'
groupBy(x, ...)

# S4 method for class 'SparkDataFrame'
group_by(x, ...)

Arguments

x

a SparkDataFrame.

...

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

Value

A GroupedData.

Note

groupBy since 1.4.0

group_by since 1.4.0

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(), dapply(), dapplyCollect(), describe(), dim(), distinct(), drop(), dropDuplicates(), dropna(), dtypes(), except(), exceptAll(), explain(), filter(), first(), gapply(), gapplyCollect(), getNumPartitions(), head(), hint(), histogram(), insertInto(), intersect(), intersectAll(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), randomSplit(), rbind(), rename(), repartition(), repartitionByRange(), rollup(), sample(), saveAsTable(), schema(), select(), selectExpr(), show(), showDF(), storageLevel(), str(), subset(), summary(), take(), toJSON(), union(), unionAll(), unionByName(), unpersist(), unpivot(), with(), withColumn(), withWatermark(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples

if (FALSE) { # \dontrun{
  # 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")
} # }