gapply {SparkR} | R Documentation |
Groups the SparkDataFrame using the specified columns and applies the R function to each group.
gapply
## S4 method for signature 'SparkDataFrame' gapply(x, cols, func, schema) gapply(x, ...) ## S4 method for signature 'GroupedData' gapply(x, func, schema)
x |
a SparkDataFrame or GroupedData. |
cols |
grouping columns. |
func |
a function to be applied to each group partition specified by grouping
column of the SparkDataFrame. The function |
schema |
the schema of the resulting SparkDataFrame after the function is applied.
The schema must match to output of |
... |
additional argument(s) passed to the method. |
A SparkDataFrame.
gapply(SparkDataFrame) since 2.0.0
gapply(GroupedData) since 2.0.0
Other SparkDataFrame functions: $
,
$,SparkDataFrame-method
, $<-
,
$<-,SparkDataFrame-method
,
select
, select
,
select,SparkDataFrame,Column-method
,
select,SparkDataFrame,character-method
,
select,SparkDataFrame,list-method
;
SparkDataFrame-class
; [
,
[,SparkDataFrame-method
, [[
,
[[,SparkDataFrame,numericOrcharacter-method
,
[[<-
,
[[<-,SparkDataFrame,numericOrcharacter-method
,
subset
, subset
,
subset,SparkDataFrame-method
;
agg
, agg
, agg
,
agg,GroupedData-method
,
agg,SparkDataFrame-method
,
summarize
, summarize
,
summarize
,
summarize,GroupedData-method
,
summarize,SparkDataFrame-method
;
arrange
, arrange
,
arrange
,
arrange,SparkDataFrame,Column-method
,
arrange,SparkDataFrame,character-method
,
orderBy,SparkDataFrame,characterOrColumn-method
;
as.data.frame
,
as.data.frame,SparkDataFrame-method
;
attach
,
attach,SparkDataFrame-method
;
cache
, cache
,
cache,SparkDataFrame-method
;
coalesce
, coalesce
,
coalesce
,
coalesce,Column-method
,
coalesce,SparkDataFrame-method
;
collect
, collect
,
collect,SparkDataFrame-method
;
colnames
, colnames
,
colnames,SparkDataFrame-method
,
colnames<-
, colnames<-
,
colnames<-,SparkDataFrame-method
,
columns
, columns
,
columns,SparkDataFrame-method
,
names
,
names,SparkDataFrame-method
,
names<-
,
names<-,SparkDataFrame-method
;
coltypes
, coltypes
,
coltypes,SparkDataFrame-method
,
coltypes<-
, coltypes<-
,
coltypes<-,SparkDataFrame,character-method
;
count,SparkDataFrame-method
,
nrow
, nrow
,
nrow,SparkDataFrame-method
;
createOrReplaceTempView
,
createOrReplaceTempView
,
createOrReplaceTempView,SparkDataFrame,character-method
;
crossJoin
,
crossJoin,SparkDataFrame,SparkDataFrame-method
;
dapplyCollect
, dapplyCollect
,
dapplyCollect,SparkDataFrame,function-method
;
dapply
, dapply
,
dapply,SparkDataFrame,function,structType-method
;
describe
, describe
,
describe
,
describe,SparkDataFrame,ANY-method
,
describe,SparkDataFrame,character-method
,
describe,SparkDataFrame-method
,
summary
, summary
,
summary,SparkDataFrame-method
;
dim
,
dim,SparkDataFrame-method
;
distinct
, distinct
,
distinct,SparkDataFrame-method
,
unique
,
unique,SparkDataFrame-method
;
dropDuplicates
,
dropDuplicates
,
dropDuplicates,SparkDataFrame-method
;
dropna
, dropna
,
dropna,SparkDataFrame-method
,
fillna
, fillna
,
fillna,SparkDataFrame-method
,
na.omit
, na.omit
,
na.omit,SparkDataFrame-method
;
drop
, drop
,
drop
, drop,ANY-method
,
drop,SparkDataFrame-method
;
dtypes
, dtypes
,
dtypes,SparkDataFrame-method
;
except
, except
,
except,SparkDataFrame,SparkDataFrame-method
;
explain
, explain
,
explain,SparkDataFrame-method
;
filter
, filter
,
filter,SparkDataFrame,characterOrColumn-method
,
where
, where
,
where,SparkDataFrame,characterOrColumn-method
;
first
, first
,
first
,
first,SparkDataFrame-method
,
first,characterOrColumn-method
;
gapplyCollect
, gapplyCollect
,
gapplyCollect
,
gapplyCollect,GroupedData-method
,
gapplyCollect,SparkDataFrame-method
;
getNumPartitions
,
getNumPartitions,SparkDataFrame-method
;
groupBy
, groupBy
,
groupBy,SparkDataFrame-method
,
group_by
, group_by
,
group_by,SparkDataFrame-method
;
head
,
head,SparkDataFrame-method
;
histogram
,
histogram,SparkDataFrame,characterOrColumn-method
;
insertInto
, insertInto
,
insertInto,SparkDataFrame,character-method
;
intersect
, intersect
,
intersect,SparkDataFrame,SparkDataFrame-method
;
isLocal
, isLocal
,
isLocal,SparkDataFrame-method
;
join
,
join,SparkDataFrame,SparkDataFrame-method
;
limit
, limit
,
limit,SparkDataFrame,numeric-method
;
merge
, merge
,
merge,SparkDataFrame,SparkDataFrame-method
;
mutate
, mutate
,
mutate,SparkDataFrame-method
,
transform
, transform
,
transform,SparkDataFrame-method
;
ncol
,
ncol,SparkDataFrame-method
;
persist
, persist
,
persist,SparkDataFrame,character-method
;
printSchema
, printSchema
,
printSchema,SparkDataFrame-method
;
randomSplit
, randomSplit
,
randomSplit,SparkDataFrame,numeric-method
;
rbind
, rbind
,
rbind,SparkDataFrame-method
;
registerTempTable
,
registerTempTable
,
registerTempTable,SparkDataFrame,character-method
;
rename
, rename
,
rename,SparkDataFrame-method
,
withColumnRenamed
,
withColumnRenamed
,
withColumnRenamed,SparkDataFrame,character,character-method
;
repartition
, repartition
,
repartition,SparkDataFrame-method
;
sample
, sample
,
sample,SparkDataFrame,logical,numeric-method
,
sample_frac
, sample_frac
,
sample_frac,SparkDataFrame,logical,numeric-method
;
saveAsParquetFile
,
saveAsParquetFile
,
saveAsParquetFile,SparkDataFrame,character-method
,
write.parquet
, write.parquet
,
write.parquet,SparkDataFrame,character-method
;
saveAsTable
, saveAsTable
,
saveAsTable,SparkDataFrame,character-method
;
saveDF
, saveDF
,
saveDF,SparkDataFrame,character-method
,
write.df
, write.df
,
write.df
,
write.df,SparkDataFrame-method
;
schema
, schema
,
schema,SparkDataFrame-method
;
selectExpr
, selectExpr
,
selectExpr,SparkDataFrame,character-method
;
showDF
, showDF
,
showDF,SparkDataFrame-method
;
show
, show
,
show,Column-method
,
show,GroupedData-method
,
show,SparkDataFrame-method
,
show,WindowSpec-method
;
storageLevel
,
storageLevel,SparkDataFrame-method
;
str
,
str,SparkDataFrame-method
;
take
, take
,
take,SparkDataFrame,numeric-method
;
union
, union
,
union,SparkDataFrame,SparkDataFrame-method
,
unionAll
, unionAll
,
unionAll,SparkDataFrame,SparkDataFrame-method
;
unpersist
, unpersist
,
unpersist,SparkDataFrame-method
;
withColumn
, withColumn
,
withColumn,SparkDataFrame,character-method
;
with
,
with,SparkDataFrame-method
;
write.jdbc
, write.jdbc
,
write.jdbc,SparkDataFrame,character,character-method
;
write.json
, write.json
,
write.json,SparkDataFrame,character-method
;
write.orc
, write.orc
,
write.orc,SparkDataFrame,character-method
;
write.text
, write.text
,
write.text,SparkDataFrame,character-method
## Not run:
##D Computes the arithmetic mean of the second column by grouping
##D on the first and third columns. Output the grouping values and the average.
##D
##D df <- createDataFrame (
##D list(list(1L, 1, "1", 0.1), list(1L, 2, "1", 0.2), list(3L, 3, "3", 0.3)),
##D c("a", "b", "c", "d"))
##D
##D Here our output contains three columns, the key which is a combination of two
##D columns with data types integer and string and the mean which is a double.
##D schema <- structType(structField("a", "integer"), structField("c", "string"),
##D structField("avg", "double"))
##D result <- gapply(
##D df,
##D c("a", "c"),
##D function(key, x) {
##D y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
##D }, schema)
##D
##D We can also group the data and afterwards call gapply on GroupedData.
##D For Example:
##D gdf <- group_by(df, "a", "c")
##D result <- gapply(
##D gdf,
##D function(key, x) {
##D y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
##D }, schema)
##D collect(result)
##D
##D Result
##D ------
##D a c avg
##D 3 3 3.0
##D 1 1 1.5
##D
##D Fits linear models on iris dataset by grouping on the 'Species' column and
##D using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
##D and 'Petal_Width' as training features.
##D
##D df <- createDataFrame (iris)
##D schema <- structType(structField("(Intercept)", "double"),
##D structField("Sepal_Width", "double"),structField("Petal_Length", "double"),
##D structField("Petal_Width", "double"))
##D df1 <- gapply(
##D df,
##D df$"Species",
##D function(key, x) {
##D m <- suppressWarnings(lm(Sepal_Length ~
##D Sepal_Width + Petal_Length + Petal_Width, x))
##D data.frame(t(coef(m)))
##D }, schema)
##D collect(df1)
##D
##D Result
##D ---------
##D Model (Intercept) Sepal_Width Petal_Length Petal_Width
##D 1 0.699883 0.3303370 0.9455356 -0.1697527
##D 2 1.895540 0.3868576 0.9083370 -0.6792238
##D 3 2.351890 0.6548350 0.2375602 0.2521257
##D
## End(Not run)