summarize

Aggregates on the entire SparkDataFrame without groups. The resulting SparkDataFrame will also contain the grouping columns.

Compute aggregates by specifying a list of columns

Usage

agg(x, ...)

summarize(x, ...)

# S4 method for GroupedData
agg(x, ...)

# S4 method for GroupedData
summarize(x, ...)

# S4 method for SparkDataFrame
agg(x, ...)

# S4 method for SparkDataFrame
summarize(x, ...)

Arguments

x

a SparkDataFrame or GroupedData.

...

further arguments to be passed to or from other methods.

Value

A SparkDataFrame.

Details

df2 <- agg(df, <column> = <aggFunction>) df2 <- agg(df, newColName = aggFunction(column))

Note

agg since 1.4.0

summarize since 1.4.0

agg since 1.4.0

summarize since 1.4.0

See also

Other SparkDataFrame functions: SparkDataFrame-class, 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(), group_by(), 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) {
 df2 <- agg(df, age = "sum")  # new column name will be created as 'SUM(age#0)'
 df3 <- agg(df, ageSum = sum(df$age)) # Creates a new column named ageSum
 df4 <- summarize(df, ageSum = max(df$age))
}