SparkR (version 3.1.2)

merge: Merges two data frames

Description

Merges two data frames

Usage

merge(x, y, ...)
# S4 method for SparkDataFrame,SparkDataFrame
merge(
  x,
  y,
  by = intersect(names(x), names(y)),
  by.x = by,
  by.y = by,
  all = FALSE,
  all.x = all,
  all.y = all,
  sort = TRUE,
  suffixes = c("_x", "_y"),
  ...
)

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 and y 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 and all.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 and y unique. The first element is appended to each colname of x. The second element is appended to each colname of y.

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

join crossJoin

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(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples

# NOT RUN {
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
# }