| dapplyCollect {SparkR} | R Documentation |
Apply a function to each partition of a SparkDataFrame and collect the result back to R as a data.frame.
dapplyCollect(x, func) ## S4 method for signature 'SparkDataFrame,'function'' dapplyCollect(x, func)
x |
A SparkDataFrame |
func |
A function to be applied to each partition of the SparkDataFrame. func should have only one parameter, to which a R data.frame corresponds to each partition will be passed. The output of func should be a R data.frame. |
dapplyCollect 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,
dapply, describe,
dim, distinct,
dropDuplicates, dropna,
drop, dtypes,
except, explain,
filter, first,
gapplyCollect, gapply,
getNumPartitions, group_by,
head, hint,
histogram, insertInto,
intersect, isLocal,
isStreaming, join,
limit, localCheckpoint,
merge, mutate,
ncol, nrow,
persist, printSchema,
randomSplit, rbind,
registerTempTable, rename,
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
## Not run:
df <- createDataFrame(iris)
ldf <- dapplyCollect(df, function(x) { x })
# filter and add a column
df <- createDataFrame(
list(list(1L, 1, "1"), list(2L, 2, "2"), list(3L, 3, "3")),
c("a", "b", "c"))
ldf <- dapplyCollect(
df,
function(x) {
y <- x[x[1] > 1, ]
y <- cbind(y, y[1] + 1L)
})
# the result
# a b c d
# 2 2 2 3
# 3 3 3 4
## End(Not run)