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.
# S4 method for SparkDataFrame
attach(what, pos = 2L,
name = deparse(substitute(what), backtick = FALSE), warn.conflicts = TRUE)
(SparkDataFrame) The SparkDataFrame to attach
(integer) Specify position in search() where to attach.
(character) Name to use for the attached SparkDataFrame. Names starting with package: are reserved for library.
(logical) If TRUE, warnings are printed about conflicts from attaching the database, unless that SparkDataFrame contains an object
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
, except
,
explain
, filter
,
first
, gapplyCollect
,
gapply
, getNumPartitions
,
group_by
, head
,
hint
, histogram
,
insertInto
, intersect
,
isLocal
, isStreaming
,
join
, limit
,
localCheckpoint
, merge
,
mutate
, ncol
,
nrow
, persist
,
printSchema
, randomSplit
,
rbind
, registerTempTable
,
rename
, repartition
,
rollup
, sample
,
saveAsTable
, schema
,
selectExpr
, select
,
showDF
, show
,
storageLevel
, str
,
subset
, summary
,
take
, toJSON
,
unionByName
, union
,
unpersist
, withColumn
,
withWatermark
, with
,
write.df
, write.jdbc
,
write.json
, write.orc
,
write.parquet
, write.stream
,
write.text
# NOT RUN {
attach(irisDf)
summary(Sepal_Width)
# }
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