subset {SparkR}R Documentation

Subset

Description

Return subsets of SparkDataFrame according to given conditions

Usage

subset(x, ...)

## S4 method for signature 'SparkDataFrame,numericOrcharacter'
x[[i]]

## S4 replacement method for signature 'SparkDataFrame,numericOrcharacter'
x[[i]] <- value

## S4 method for signature 'SparkDataFrame'
x[i, j, ..., drop = F]

## S4 method for signature 'SparkDataFrame'
subset(x, subset, select, drop = F, ...)

Arguments

x

a SparkDataFrame.

...

currently not used.

i, subset

(Optional) a logical expression to filter on rows. For extract operator [[ and replacement operator [[<-, the indexing parameter for a single Column.

value

a Column or an atomic vector in the length of 1 as literal value, or NULL. If NULL, the specified Column is dropped.

j, select

expression for the single Column or a list of columns to select from the SparkDataFrame.

drop

if TRUE, a Column will be returned if the resulting dataset has only one column. Otherwise, a SparkDataFrame will always be returned.

Value

A new SparkDataFrame containing only the rows that meet the condition with selected columns.

Note

[[ since 1.4.0

[[<- since 2.1.1

[ since 1.4.0

subset since 1.5.0

See Also

withColumn

Other SparkDataFrame functions: SparkDataFrame-class, agg, arrange, as.data.frame, attach, cache, coalesce, collect, colnames, coltypes, createOrReplaceTempView, crossJoin, dapplyCollect, dapply, describe, dim, distinct, dropDuplicates, dropna, drop, dtypes, except, explain, filter, first, gapplyCollect, gapply, getNumPartitions, group_by, head, histogram, insertInto, intersect, isLocal, join, limit, merge, mutate, ncol, nrow, persist, printSchema, randomSplit, rbind, registerTempTable, rename, repartition, sample, saveAsTable, schema, selectExpr, select, showDF, show, storageLevel, str, take, union, unpersist, withColumn, with, write.df, write.jdbc, write.json, write.orc, write.parquet, write.text

Other subsetting functions: filter, select

Examples

## Not run: 
##D   # Columns can be selected using [[ and [
##D   df[[2]] == df[["age"]]
##D   df[,2] == df[,"age"]
##D   df[,c("name", "age")]
##D   # Or to filter rows
##D   df[df$age > 20,]
##D   # SparkDataFrame can be subset on both rows and Columns
##D   df[df$name == "Smith", c(1,2)]
##D   df[df$age %in% c(19, 30), 1:2]
##D   subset(df, df$age %in% c(19, 30), 1:2)
##D   subset(df, df$age %in% c(19), select = c(1,2))
##D   subset(df, select = c(1,2))
##D   # Columns can be selected and set
##D   df[["age"]] <- 23
##D   df[[1]] <- df$age
##D   df[[2]] <- NULL # drop column
## End(Not run)

[Package SparkR version 2.1.2 Index]