Using foreach instead of for loop - r

Question

I am trying to learn foreach to parallelise my task
My for-loop looks like this:
# create an empty matrix to store results
mat <- matrix(-9999, nrow = unique(dat$mun), ncol = 2)
for(mun in unique(dat$mun)) {
dat <- read.csv(paste0("data",mun,".csv")
tot.dat <- sum(dat$x)
mat[mat[,1]== mun,2] <- tot.dat
}
unique(dat$mun) has a length of 5563.
I want to use foreach to pararellise my task.
library(foreach)
library(doParallel)
# number of iterations
iters <- 5563
foreach(icount(iters)) %dopar% {
mun <- unique(dat$mun)[mun] # this is where I cannot figure out how to assing mun so that it read the data for mun
dat <- read.csv(paste0("data",mun,".csv")
tot.dat <- sum(dat$x)
mat[mat[,1]== mun,2] <- tot.dat
}

Answer 1

This could be one solution.
Do note that I'm using windows here, and i specified registerDoParallel() for it to work.
library(foreach)
library(doParallel)
# number of iterations
iters <- 5563
registerDoParallel()
mun <- unique(dat$mun)
tableList <- foreach(i=1:iters) %dopar% {
dat <- read.csv(paste0("data",mun[i],".csv")
tot.dat <- sum(dat$x)
}
unlist(tableList)
Essentially, whatever result inside {...} will be stored inside a list.
In this case, the result (tot.dat which is a number) is compiled in tableList, and by performing unlist() we can convert it to a vector for further use.
The result inside {...} can be anything, a single number, a vector, a dataframe, or anything.
Another approach for your problem would be to combine all existing data together, labelling it with its appropriate source file, so the middle component will look something like
library(plyr)
tableAll <- foreach(i=1:iters) %dopar% {
dat <- read.csv(paste0("data",mun[i],".csv")
dat$source = mun[i]
}
rbind.fill(tableAll)
Then we can use it for further analysis.