Merges two data frames
merge.Rd
Merges two data frames
Arguments
- x
the first data frame to be joined.
- y
the second data frame to be joined.
- ...
additional argument(s) passed to the method.
- by
a character vector specifying the join columns. If by is not specified, the common column names in
x
andy
will be used. If by or both by.x and by.y are explicitly set to NULL or of length 0, the Cartesian Product of x and y will be returned.- by.x
a character vector specifying the joining columns for x.
- by.y
a character vector specifying the joining columns for y.
- all
a boolean value setting
all.x
andall.y
if any of them are unset.- all.x
a boolean value indicating whether all the rows in x should be including in the join.
- all.y
a boolean value indicating whether all the rows in y should be including in the join.
- sort
a logical argument indicating whether the resulting columns should be sorted.
- suffixes
a string vector of length 2 used to make colnames of
x
andy
unique. The first element is appended to each colname ofx
. The second element is appended to each colname ofy
.
Details
If all.x and all.y are set to FALSE, a natural join will be returned. If all.x is set to TRUE and all.y is set to FALSE, a left outer join will be returned. If all.x is set to FALSE and all.y is set to TRUE, a right outer join will be returned. If all.x and all.y are set to TRUE, a full outer join will be returned.
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()
,
mutate()
,
ncol()
,
nrow()
,
persist()
,
printSchema()
,
randomSplit()
,
rbind()
,
rename()
,
repartitionByRange()
,
repartition()
,
rollup()
,
sample()
,
saveAsTable()
,
schema()
,
selectExpr()
,
select()
,
showDF()
,
show()
,
storageLevel()
,
str()
,
subset()
,
summary()
,
take()
,
toJSON()
,
unionAll()
,
unionByName()
,
union()
,
unpersist()
,
unpivot()
,
withColumn()
,
withWatermark()
,
with()
,
write.df()
,
write.jdbc()
,
write.json()
,
write.orc()
,
write.parquet()
,
write.stream()
,
write.text()
Examples
if (FALSE) {
sparkR.session()
df1 <- read.json(path)
df2 <- read.json(path2)
merge(df1, df2) # Performs an inner join by common columns
merge(df1, df2, by = "col1") # Performs an inner join based on expression
merge(df1, df2, by.x = "col1", by.y = "col2", all.y = TRUE)
merge(df1, df2, by.x = "col1", by.y = "col2", all.x = TRUE)
merge(df1, df2, by.x = "col1", by.y = "col2", all.x = TRUE, all.y = TRUE)
merge(df1, df2, by.x = "col1", by.y = "col2", all = TRUE, sort = FALSE)
merge(df1, df2, by = "col1", all = TRUE, suffixes = c("-X", "-Y"))
merge(df1, df2, by = NULL) # Performs a Cartesian join
}