SparkR (version 2.4.6)

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(), 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(), drop(), 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(), summary(), take(), toJSON(), unionByName(), union(), unpersist(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Other subsetting functions: filter(), select()

Examples

# 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
# }