gapplyCollect {SparkR}R Documentation

gapplyCollect

Description

Groups the SparkDataFrame using the specified columns, applies the R function to each group and collects the result back to R as data.frame.

Usage

gapplyCollect(x, ...)

## S4 method for signature 'GroupedData'
gapplyCollect(x, func)

## S4 method for signature 'SparkDataFrame'
gapplyCollect(x, cols, func)

Arguments

x

a SparkDataFrame or GroupedData.

...

additional argument(s) passed to the method.

func

a function to be applied to each group partition specified by grouping column of the SparkDataFrame. See Details.

cols

grouping columns.

Details

func is a function of two arguments. The first, usually named key (though this is not enforced) corresponds to the grouping key, will be an unnamed list of length(cols) length-one objects corresponding to the grouping columns' values for the current group.

The second, herein x, will be a local data.frame with the columns of the input not in cols for the rows corresponding to key.

The output of func must be a data.frame matching schema – in particular this means the names of the output data.frame are irrelevant

Value

A data.frame.

Note

gapplyCollect(GroupedData) since 2.0.0

gapplyCollect(SparkDataFrame) since 2.0.0

See Also

gapply

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

Examples




## Not run: 
##D # Computes the arithmetic mean of the second column by grouping
##D # on the first and third columns. Output the grouping values and the average.
##D 
##D df <- createDataFrame (
##D list(list(1L, 1, "1", 0.1), list(1L, 2, "1", 0.2), list(3L, 3, "3", 0.3)),
##D   c("a", "b", "c", "d"))
##D 
##D result <- gapplyCollect(
##D   df,
##D   c("a", "c"),
##D   function(key, x) {
##D     y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
##D     colnames(y) <- c("key_a", "key_c", "mean_b")
##D     y
##D   })
##D 
##D # We can also group the data and afterwards call gapply on GroupedData.
##D # For example:
##D gdf <- group_by(df, "a", "c")
##D result <- gapplyCollect(
##D   gdf,
##D   function(key, x) {
##D     y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
##D     colnames(y) <- c("key_a", "key_c", "mean_b")
##D     y
##D   })
##D 
##D # Result
##D # ------
##D # key_a key_c mean_b
##D # 3 3 3.0
##D # 1 1 1.5
##D 
##D # Fits linear models on iris dataset by grouping on the 'Species' column and
##D # using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
##D # and 'Petal_Width' as training features.
##D 
##D df <- createDataFrame (iris)
##D result <- gapplyCollect(
##D   df,
##D   df$"Species",
##D   function(key, x) {
##D     m <- suppressWarnings(lm(Sepal_Length ~
##D     Sepal_Width + Petal_Length + Petal_Width, x))
##D     data.frame(t(coef(m)))
##D   })
##D 
##D # Result
##D # ---------
##D # Model  X.Intercept.  Sepal_Width  Petal_Length  Petal_Width
##D # 1        0.699883    0.3303370    0.9455356    -0.1697527
##D # 2        1.895540    0.3868576    0.9083370    -0.6792238
##D # 3        2.351890    0.6548350    0.2375602     0.2521257
##D 
## End(Not run)




[Package SparkR version 3.2.4 Index]