SparkR (version 3.1.2)

drop: drop

Description

Returns a new SparkDataFrame with columns dropped. This is a no-op if schema doesn't contain column name(s).

Usage

drop(x, ...)
# S4 method for SparkDataFrame
drop(x, col)
# S4 method for ANY
drop(x)

Arguments

x

a SparkDataFrame.

...

further arguments to be passed to or from other methods.

col

a character vector of column names or a Column.

Value

A SparkDataFrame.

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(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples

# NOT RUN {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
drop(df, "col1")
drop(df, c("col1", "col2"))
drop(df, df$col1)
# }