| randomSplit {SparkR} | R Documentation |
Return a list of randomly split dataframes with the provided weights.
randomSplit(x, weights, seed) ## S4 method for signature 'SparkDataFrame,numeric' randomSplit(x, weights, seed)
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 |
randomSplit since 2.0.0
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, unionAll,
unionByName, union,
unpersist, withColumn,
withWatermark, with,
write.df, write.jdbc,
write.json, write.orc,
write.parquet, write.stream,
write.text
## Not run:
##D sparkR.session()
##D df <- createDataFrame(data.frame(id = 1:1000))
##D df_list <- randomSplit(df, c(2, 3, 5), 0)
##D # df_list contains 3 SparkDataFrames with each having about 200, 300 and 500 rows respectively
##D sapply(df_list, count)
## End(Not run)