Skip to contents

Return subsets of SparkDataFrame according to given conditions


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, ...)



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.


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.


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


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


[[ since 1.4.0

[[<- since 2.1.1

[ since 1.4.0

subset since 1.5.0


if (FALSE) {
  # 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