SparkR (version 2.1.2)

subset: Subset

Description

Return subsets of SparkDataFrame according to given conditions

Usage

subset(x, ...)

# S4 method for SparkDataFrame,numericOrcharacter [[(x, i)

# S4 method for SparkDataFrame,numericOrcharacter [[(x, i) <- value

# S4 method for SparkDataFrame [(x, i, j, ..., drop = F)

# S4 method for 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.

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

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

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