[[ {SparkR} | R Documentation |
Return subsets of SparkDataFrame according to given conditions
## S4 method for signature 'SparkDataFrame,numericOrcharacter' x[[i]] ## S4 method for signature 'SparkDataFrame' x[i, j, ..., drop = F] ## S4 method for signature 'SparkDataFrame' subset(x, subset, select, drop = F, ...)
x |
A SparkDataFrame |
drop |
if TRUE, a Column will be returned if the resulting dataset has only one column. Otherwise, a SparkDataFrame will always be returned. |
subset |
(Optional) A logical expression to filter on rows |
select |
expression for the single Column or a list of columns to select from the SparkDataFrame |
A new SparkDataFrame containing only the rows that meet the condition with selected columns
Other SparkDataFrame functions: $
,
$<-
, select
,
select
,
select,SparkDataFrame,Column-method
,
select,SparkDataFrame,list-method
,
selectExpr
;
SparkDataFrame-class
,
dataFrame
; agg
,
agg
,
count,GroupedData-method
,
summarize
, summarize
;
arrange
, arrange
,
arrange
, orderBy
,
orderBy
, orderBy
,
orderBy
; as.data.frame
,
as.data.frame,SparkDataFrame-method
;
attach
,
attach,SparkDataFrame-method
;
cache
; collect
;
colnames
, colnames
,
colnames<-
, colnames<-
,
columns
, names
,
names<-
; coltypes
,
coltypes
, coltypes<-
,
coltypes<-
; columns
,
dtypes
, printSchema
,
schema
, schema
;
count
, nrow
;
dapply
, dapply
,
dapplyCollect
, dapplyCollect
;
describe
, describe
,
describe
, summary
,
summary
,
summary,AFTSurvivalRegressionModel-method
,
summary,GeneralizedLinearRegressionModel-method
,
summary,KMeansModel-method
,
summary,NaiveBayesModel-method
;
dim
; distinct
,
unique
; dropDuplicates
,
dropDuplicates
; dropna
,
dropna
, fillna
,
fillna
, na.omit
,
na.omit
; drop
,
drop
; dtypes
;
except
, except
;
explain
, explain
;
filter
, filter
,
where
, where
;
first
, first
;
groupBy
, groupBy
,
group_by
, group_by
;
head
; histogram
;
insertInto
, insertInto
;
intersect
, intersect
;
isLocal
, isLocal
;
join
; limit
,
limit
; merge
,
merge
; mutate
,
mutate
, transform
,
transform
; ncol
;
persist
; printSchema
;
rbind
, rbind
,
unionAll
, unionAll
;
registerTempTable
,
registerTempTable
; rename
,
rename
, withColumnRenamed
,
withColumnRenamed
;
repartition
; sample
,
sample
, sample_frac
,
sample_frac
;
saveAsParquetFile
,
saveAsParquetFile
,
write.parquet
, write.parquet
;
saveAsTable
, saveAsTable
;
saveDF
, saveDF
,
write.df
, write.df
,
write.df
; selectExpr
;
showDF
, showDF
;
show
, show
,
show,GroupedData-method
,
show,WindowSpec-method
; str
;
take
; unpersist
;
withColumn
, withColumn
;
write.jdbc
, write.jdbc
;
write.json
, write.json
;
write.text
, write.text
Other subsetting functions: $
,
$<-
, select
,
select
,
select,SparkDataFrame,Column-method
,
select,SparkDataFrame,list-method
,
selectExpr
; filter
,
filter
, where
,
where
## Not run: # Columns can be selected using `[[` and `[` df[[2]] == df[["age"]] df[,2] == df[,"age"] df[,c("name", "age")] # Or to filter rows df[df$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)) ## End(Not run)