This function downloads the contents of a SparkDataFrame into an R's data.frame. Since data.frames are held in memory, ensure that you have enough memory in your system to accommodate the contents.
as.data.frame(x, row.names = NULL, optional = FALSE, ...)# S4 method for SparkDataFrame
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
a SparkDataFrame.
NULL or a character vector giving the row names for the data frame.
If TRUE, converting column names is optional.
additional arguments to pass to base::as.data.frame.
A data.frame.
Other SparkDataFrame functions:
SparkDataFrame-class,
agg(),
alias(),
arrange(),
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(),
unionAll(),
unionByName(),
union(),
unpersist(),
withColumn(),
withWatermark(),
with(),
write.df(),
write.jdbc(),
write.json(),
write.orc(),
write.parquet(),
write.stream(),
write.text()
# NOT RUN {
irisDF <- createDataFrame(iris)
df <- as.data.frame(irisDF[irisDF$Species == "setosa", ])
# }
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