Actually, you'll need to identify the values of the foreach loop in the for
loop for it to work...
require(doParallel)
require(foreach)
set.seed(666)
xyz<-as.data.frame(cbind(x=rep(rpois(50000,10),2)+1,
y=rep(rpois(50000,10),2)+1,z=round(runif(100000, min=-3, max=40),2)))
xyz$mins<-rep(NA, nrow(xyz))
xyz[order(xyz[,1],xyz[,2], xyz[,3]),]->xyz
cl<-makeCluster(4) #adjust to your cluster number
registerDoParallel(cl)
test<-foreach(i=unique(xyz[,1]), .combine=rbind, .verbose=T) %dopar% {
for(j in unique(xyz[xyz[,1] == i,2] )) {
# here ensure you pass on the right
data
xyz[xyz[,2] == j & xyz[,1] == i ,4]<-min(xyz[xyz[,2] == j & xyz[,1] ==
i,3]) # otherwise there are inf values here
nr=nrow(xyz[xyz[,2] == j & xyz[,1] == i ,4])
}
return(xyz[xyz[,1]== i,]) # you must return what you are farming out...
}
test[1:15,]
stopCluster(cl)
XXXXXXXXXXXXXXXXXXXXX Herry wrote XXXXXXXXXXXXXXXXXX
Hiya,
This now works...
test<-foreach(i=unique(xyz[,1]), .combine=rbind, .verbose=T) %dopar% {
for( j in unique(xyz[,2])) {
xyz[xyz[,2] == j & xyz[,1] == i ,4]<-min(xyz[xyz[,2] == j & xyz[,1] ==
i,3])
nr=nrow(xyz[xyz[,2] == j & xyz[,1] == i ,4])
}
return(xyz[xyz[,1]== i,]) # you must return what you are farming out...
}
head(test)
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