| dapply {SparkR} | R Documentation |
Apply a function to each partition of a SparkDataFrame.
dapply(x, func, schema) ## S4 method for signature 'SparkDataFrame,'function',structType' dapply(x, func, schema)
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. |
schema |
The schema of the resulting SparkDataFrame after the function is applied. It must match the output of func. |
dapply since 2.0.0
Other SparkDataFrame functions: SparkDataFrame-class,
agg, arrange,
as.data.frame,
attach,SparkDataFrame-method,
cache, checkpoint,
coalesce, collect,
colnames, coltypes,
createOrReplaceTempView,
crossJoin, dapplyCollect,
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,
merge, mutate,
ncol, nrow,
persist, printSchema,
randomSplit, rbind,
registerTempTable, rename,
repartition, sample,
saveAsTable, schema,
selectExpr, select,
showDF, show,
storageLevel, str,
subset, take,
toJSON, union,
unpersist, withColumn,
with, write.df,
write.jdbc, write.json,
write.orc, write.parquet,
write.stream, write.text
## Not run:
##D df <- createDataFrame(iris)
##D df1 <- dapply(df, function(x) { x }, schema(df))
##D collect(df1)
##D
##D # filter and add a column
##D df <- createDataFrame(
##D list(list(1L, 1, "1"), list(2L, 2, "2"), list(3L, 3, "3")),
##D c("a", "b", "c"))
##D schema <- structType(structField("a", "integer"), structField("b", "double"),
##D structField("c", "string"), structField("d", "integer"))
##D df1 <- dapply(
##D df,
##D function(x) {
##D y <- x[x[1] > 1, ]
##D y <- cbind(y, y[1] + 1L)
##D },
##D schema)
##D collect(df1)
##D # the result
##D # a b c d
##D # 1 2 2 2 3
##D # 2 3 3 3 4
## End(Not run)