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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.
(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.
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.
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
, except
,
explain
, filter
,
first
, gapplyCollect
,
gapply
, getNumPartitions
,
group_by
, head
,
hint
, histogram
,
insertInto
, intersect
,
isLocal
, isStreaming
,
join
, limit
,
localCheckpoint
, merge
,
mutate
, ncol
,
nrow
, persist
,
printSchema
, randomSplit
,
rbind
, registerTempTable
,
rename
, 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
# 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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