| subset {SparkR} | R Documentation |
Return subsets of SparkDataFrame according to given conditions
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, ...)
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 |
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. |
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
Other SparkDataFrame functions: SparkDataFrame-class,
agg, arrange,
as.data.frame,
attach,SparkDataFrame-method,
cache, checkpoint,
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, hint,
histogram, insertInto,
intersect, isLocal,
isStreaming, join,
limit, merge,
mutate, ncol,
nrow, persist,
printSchema, randomSplit,
rbind, registerTempTable,
rename, repartition,
sample, saveAsTable,
schema, selectExpr,
select, showDF,
show, storageLevel,
str, take,
toJSON, union,
unpersist, withColumn,
with, write.df,
write.jdbc, write.json,
write.orc, write.parquet,
write.stream, write.text
Other subsetting functions: filter,
select
## 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)