intersect {SparkR} | R Documentation |
Return a new SparkDataFrame containing rows only in both this SparkDataFrame
and another SparkDataFrame. This is equivalent to INTERSECT
in SQL.
intersect(x, y) ## S4 method for signature 'SparkDataFrame,SparkDataFrame' intersect(x, y)
x |
A SparkDataFrame |
y |
A SparkDataFrame |
A SparkDataFrame containing the result of the intersect.
intersect since 1.4.0
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
, except
,
explain
, filter
,
first
, gapplyCollect
,
gapply
, getNumPartitions
,
group_by
, head
,
hint
, histogram
,
insertInto
, 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:
##D sparkR.session()
##D df1 <- read.json(path)
##D df2 <- read.json(path2)
##D intersectDF <- intersect(df, df2)
## End(Not run)