SparkR (version 2.1.2)

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, arrange, as.data.frame, attach, cache, coalesce, collect, colnames, coltypes, createOrReplaceTempView, crossJoin, dapplyCollect, dapply, describe, dim, distinct, dropDuplicates, dropna, drop, dtypes, except, explain, filter, first, gapplyCollect, gapply, getNumPartitions, group_by, head, histogram, insertInto, intersect, isLocal, join, limit, mutate, ncol, nrow, persist, printSchema, randomSplit, rbind, registerTempTable, rename, repartition, sample, saveAsTable, schema, selectExpr, select, showDF, show, storageLevel, str, subset, take, union, unpersist, withColumn, with, write.df, write.jdbc, write.json, write.orc, write.parquet, 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
# }