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(),
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(),
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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