| union {SparkR} | R Documentation |
Return a new SparkDataFrame containing the union of rows in this SparkDataFrame
and another SparkDataFrame. This is equivalent to UNION ALL in SQL.
Input SparkDataFrames can have different schemas (names and data types).
union(x, y) unionAll(x, y) ## S4 method for signature 'SparkDataFrame,SparkDataFrame' union(x, y) ## S4 method for signature 'SparkDataFrame,SparkDataFrame' unionAll(x, y)
x |
A SparkDataFrame |
y |
A SparkDataFrame |
Note: This does not remove duplicate rows across the two SparkDataFrames. Also as standard in SQL, this function resolves columns by position (not by name).
A SparkDataFrame containing the result of the union.
union since 2.0.0
unionAll 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, exceptAll,
except, explain,
filter, first,
gapplyCollect, gapply,
getNumPartitions, group_by,
head, hint,
histogram, insertInto,
intersectAll, intersect,
isLocal, isStreaming,
join, limit,
localCheckpoint, merge,
mutate, ncol,
nrow, persist,
printSchema, randomSplit,
rbind, rename,
repartitionByRange,
repartition, rollup,
sample, saveAsTable,
schema, selectExpr,
select, showDF,
show, storageLevel,
str, subset,
summary, take,
toJSON, unionByName,
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 unioned <- union(df, df2)
##D unions <- rbind(df, df2, df3, df4)
## End(Not run)