You might need MARGIN capitalized, this example works though:
c <- as.DataFrame(cars)
# rename the columns to c1, c2
c <- selectExpr(c, "speed as c1", "dist as c2")
cols_in <- dapplyCollect(c,
function(x) {apply(x[, paste("c", 1:2, sep = "")], MARGIN=2, FUN = function(y){
y %in% c(61, 99)})})
# dapplyCollect does not require the schema parameter
_____________________________
From: xingye <tracy.up...@gmail.com<mailto:tracy.up...@gmail.com>>
Sent: Friday, September 9, 2016 10:35 AM
Subject: questions about using dapply
To: <user@spark.apache.org<mailto:user@spark.apache.org>>
I have a question about using UDF in SparkR. I'm converting some R code into
SparkR.
* The original R code is :
cols_in <- apply(df[, paste("cr_cd", 1:12, sep = "")], MARGIN = 2, FUN =
"%in%", c(61, 99))
* If I use dapply and put the original apply function as a function for dapply,
cols_in <-dapply(df,
function(x) {apply(x[, paste("cr_cd", 1:12, sep = "")], Margin=2, function(y){
y %in% c(61, 99)})},
schema )
The error shows Error in match.fun(FUN) : argument "FUN" is missing, with no
default
* If I use spark.lapply, it still shows the error. It seems in spark, the
column cr_cd1 is ambiguous.
cols_in <-spark.lapply(df[, paste("cr_cd", 1:12, sep = "")], function(x){ x
%in% c(61, 99)})
16/09/08 ERROR RBackendHandler: select on 3101 failed Error in
invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: Reference 'cr_cd1' is ambiguous, could
be: cr_cd1#2169L, cr_cd1#17787L.;
* If I use dapplycollect, it works but it will lead to memory issue if data
is big. how can the dapply work in my case?
wrapper = function(df){
out = apply(df[, paste("cr_cd", 1:12, sep = "")], MARGIN = 2, FUN = "%in%",
c(61, 99))
return(out)
}
cols_in <-dapplyCollect(df,wrapper)
