Alexander.Herr at csiro.au
2016-Aug-09 05:38 UTC
[R] foreach {parallel} nested with for loop to update data.frame column
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)