Attach SparkDataFrame to R search path
attach.Rd
The specified SparkDataFrame is attached to the R search path. This means that the SparkDataFrame is searched by R when evaluating a variable, so columns in the SparkDataFrame can be accessed by simply giving their names.
Usage
# S4 method for SparkDataFrame
attach(
what,
pos = 2L,
name = paste(deparse(substitute(what), backtick = FALSE), collapse = " "),
warn.conflicts = TRUE
)
Arguments
- what
(SparkDataFrame) The SparkDataFrame to attach
- pos
(integer) Specify position in search() where to attach.
- name
(character) Name to use for the attached SparkDataFrame. Names starting with package: are reserved for library.
- warn.conflicts
(logical) If TRUE, warnings are printed about conflicts from attaching the database, unless that SparkDataFrame contains an object
See also
Other SparkDataFrame functions:
SparkDataFrame-class
,
agg()
,
alias()
,
arrange()
,
as.data.frame()
,
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()
,
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) {
attach(irisDf)
summary(Sepal_Width)
}