| gapply {SparkR} | R Documentation |
Groups the SparkDataFrame using the specified columns and applies the R function to each group.
gapply(x, ...) ## S4 method for signature 'GroupedData' gapply(x, func, schema) ## S4 method for signature 'SparkDataFrame' gapply(x, cols, func, schema)
x |
a SparkDataFrame or GroupedData. |
... |
additional argument(s) passed to the method. |
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 |
cols |
grouping columns. |
A SparkDataFrame.
gapply(GroupedData) since 2.0.0
gapply(SparkDataFrame) since 2.0.0
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(),
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(),
subset(),
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:
Computes the arithmetic mean of the second column by grouping
on the first and third columns. Output the grouping values and the average.
df <- createDataFrame (
list(list(1L, 1, "1", 0.1), list(1L, 2, "1", 0.2), list(3L, 3, "3", 0.3)),
c("a", "b", "c", "d"))
Here our output contains three columns, the key which is a combination of two
columns with data types integer and string and the mean which is a double.
schema <- structType(structField("a", "integer"), structField("c", "string"),
structField("avg", "double"))
result <- gapply(
df,
c("a", "c"),
function(key, x) {
y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
}, schema)
The schema also can be specified in a DDL-formatted string.
schema <- "a INT, c STRING, avg DOUBLE"
result <- gapply(
df,
c("a", "c"),
function(key, x) {
y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
}, schema)
We can also group the data and afterwards call gapply on GroupedData.
For Example:
gdf <- group_by(df, "a", "c")
result <- gapply(
gdf,
function(key, x) {
y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
}, schema)
collect(result)
Result
------
a c avg
3 3 3.0
1 1 1.5
Fits linear models on iris dataset by grouping on the 'Species' column and
using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
and 'Petal_Width' as training features.
df <- createDataFrame (iris)
schema <- structType(structField("(Intercept)", "double"),
structField("Sepal_Width", "double"),structField("Petal_Length", "double"),
structField("Petal_Width", "double"))
df1 <- gapply(
df,
df$"Species",
function(key, x) {
m <- suppressWarnings(lm(Sepal_Length ~
Sepal_Width + Petal_Length + Petal_Width, x))
data.frame(t(coef(m)))
}, schema)
collect(df1)
Result
---------
Model (Intercept) Sepal_Width Petal_Length Petal_Width
1 0.699883 0.3303370 0.9455356 -0.1697527
2 1.895540 0.3868576 0.9083370 -0.6792238
3 2.351890 0.6548350 0.2375602 0.2521257
## End(Not run)