SparkR (version 2.4.6)

randomSplit: randomSplit

Description

Return a list of randomly split dataframes with the provided weights.

Usage

randomSplit(x, weights, seed)
# S4 method for SparkDataFrame,numeric
randomSplit(x, weights, seed)

Arguments

x

A SparkDataFrame

weights

A vector of weights for splits, will be normalized if they don't sum to 1

seed

A seed to use for random split

See Also

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(), group_by(), head(), hint(), histogram(), insertInto(), intersectAll(), intersect(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), 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 {
sparkR.session()
df <- createDataFrame(data.frame(id = 1:1000))
df_list <- randomSplit(df, c(2, 3, 5), 0)
# df_list contains 3 SparkDataFrames with each having about 200, 300 and 500 rows respectively
sapply(df_list, count)
# }