How do you create a progress bar when using the "foreach()" function in R?

Edit: After an update to the doSNOW package it has become quite simple to display a nice progress bar when using %dopar% and it works on Linux, Windows and OS X

doSNOW now officially supports progress bars via the .options.snow argument.

library(doSNOW)
cl <- makeCluster(2)
registerDoSNOW(cl)
iterations <- 100
pb <- txtProgressBar(max = iterations, style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
result <- foreach(i = 1:iterations, .combine = rbind, 
                  .options.snow = opts) %dopar%
{
    s <- summary(rnorm(1e6))[3]
    return(s)
}
close(pb)
stopCluster(cl) 

Yet another way of tracking progress, if you keep in mind the total number of iterations, is to set .verbose = T as this will print to the console which iterations have been finished.

Previous solution for Linux and OS X

On Ubuntu 14.04 (64 bit) and OS X (El Capitan) the progress bar is displayed even when using %dopar% if in the makeCluster function oufile = "" is set. It does not seem to work under Windows. From the help on makeCluster:

outfile: Where to direct the stdout and stderr connection output from the workers. "" indicates no redirection (which may only be useful for workers on the local machine). Defaults to ‘/dev/null’ (‘nul:’ on Windows).

Example code:

library(foreach)
library(doSNOW)
cl <- makeCluster(4, outfile="") # number of cores. Notice 'outfile'
registerDoSNOW(cl)
iterations <- 100
pb <- txtProgressBar(min = 1, max = iterations, style = 3)
result <- foreach(i = 1:iterations, .combine = rbind) %dopar% 
{
      s <- summary(rnorm(1e6))[3]
      setTxtProgressBar(pb, i) 
      return(s)
}
close(pb)
stopCluster(cl) 

This is what the progress bar looks like. It looks a little odd since a new bar is printed for every progression of the bar and because a worker may lag a bit which causes the progress bar to go back and forth occasionally.

This code is a modified version of the doRedis example, and will make a progress bar even when using %dopar% with a parallel backend:

#Load Libraries
library(foreach)
library(utils)
library(iterators)
library(doParallel)
library(snow)

#Choose number of iterations
n <- 1000

#Progress combine function
f <- function(){
  pb <- txtProgressBar(min=1, max=n-1,style=3)
  count <- 0
  function(...) {
    count <<- count + length(list(...)) - 1
    setTxtProgressBar(pb,count)
    Sys.sleep(0.01)
    flush.console()
    c(...)
  }
}

#Start a cluster
cl <- makeCluster(4, type='SOCK')
registerDoParallel(cl)

# Run the loop in parallel
k <- foreach(i = icount(n), .final=sum, .combine=f()) %dopar% {
  log2(i)
}

head(k)

#Stop the cluster
stopCluster(cl)

You have to know the number of iterations and the combination function ahead of time.

This is now possible with the parallel package. Tested with R 3.2.3 on OSX 10.11, running inside RStudio, using a "PSOCK"-type cluster.

library(doParallel)

# default cluster type on my machine is "PSOCK", YMMV with other types
cl <- parallel::makeCluster(4, outfile = "")
registerDoParallel(cl)

n <- 10000
pb <- txtProgressBar(0, n, style = 2)

invisible(foreach(i = icount(n)) %dopar% {
    setTxtProgressBar(pb, i)
})

stopCluster(cl)

Strangely, it only displays correctly with style = 3.

You can also get this to work with the progress package.

# loading parallel and doSNOW package and creating cluster ----------------
library(parallel)
library(doSNOW)

numCores<-detectCores()
cl <- makeCluster(numCores)
registerDoSNOW(cl)

# progress bar ------------------------------------------------------------
library(progress)

iterations <- 100                               # used for the foreach loop  

pb <- progress_bar$new(
  format = "letter = :letter [:bar] :elapsed | eta: :eta",
  total = iterations,    # 100 
  width = 60)

progress_letter <- rep(LETTERS[1:10], 10)  # token reported in progress bar

# allowing progress bar to be used in foreach -----------------------------
progress <- function(n){
  pb$tick(tokens = list(letter = progress_letter[n]))
} 

opts <- list(progress = progress)

# foreach loop ------------------------------------------------------------
library(foreach)

foreach(i = 1:iterations, .combine = rbind, .options.snow = opts) %dopar% {
  summary(rnorm(1e6))[3]
}

stopCluster(cl)