drop
drop.RdReturns a new SparkDataFrame with columns dropped. This is a no-op if schema doesn't contain column name(s).
Usage
drop(x, col, ...)
# S4 method for SparkDataFrame,characterOrColumn
drop(x, col, ...)
# S4 method for ANY,ANY
drop(x)Arguments
- x
a SparkDataFrame.
- col
a list of columns or single Column or name.
- ...
additional column(s) if only one column is specified in
col. If more than one column is assigned incol,...should be left empty.
See also
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(),
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) {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
drop(df, "col1")
drop(df, c("col1", "col2"))
drop(df, df$col1)
drop(df, "col1", "col2")
drop(df, df$name, df$age)
}