A lightweight package that adds progress bar to vectorized R functions (*apply). The implementation can easily be added to functions where showing the progress is useful (e.g. bootstrap). The type and style of the progress bar (with percentages or remaining time) can be set through options. The package supports several parallel processing backends, such as snow-type clusters and multicore-type forking (see overview here).


Install CRAN release version (recommended):


Development version:

if (!requireNamespace("remotes")) install.packages("remotes")

See user-visible changes in the NEWS file.

Use the issue tracker to report a problem, or to suggest a new feature.

How to get started?

1. You are not yet an R user

In this case, start with understaning basic programming concepts, such as data structures (matrices, data frames, indexing these), for loops and functions in R. The online version of Garrett Grolemund’s Hands-On Programming with R walks you through these concepts nicely.

2. You are an R user but haven’t used vectorized functions yet

Learn about vectorized functions designed to replace for loops: lapply, sapply, and apply. Here is a repository called The Road to Progress that I created to show how to got from a for loop to lapply/sapply.

3. You are an R user familiar with vectorized functions

In this case, you can simply add pbapply::pb before your *apply functions, e.g. apply() will be pbapply::pbapply(), etc. You can guess what happens. Now if you want to speed things up a little (or a lot), try pbapply::pbapply(..., cl = 4) to use 4 cores instead of 1.

If you are a Windows user, things get a bit more complicated, but not much. Check how to work with parallel::parLapply to set up a snow type cluster. Have a look at the The Road to Progress repository to see a worked example.

4. You are a seasoned R developer writing your own packages

Read on, the next section is for you.

How to add pbapply to a package

There are two ways of adding the pbapply package to another package.

1. Suggests: pbapply

Add pbapply to the Suggests field in the DESCRIPTION.

Use a conditional statement in your code to fall back on a base function in case of pbapply not installed:

out <- if (requireNamespace("pbapply", quietly = TRUE)) {
   pbapply::pblapply(X, FUN, ...)
} else {
   lapply(X, FUN, ...)

See a small example package here.

2. Depends/Imports: pbapply

Add pbapply to the Depends or Imports field in the DESCRIPTION.

Use the pbapply functions either as pbapply::pblapply() or specify them in the NAMESPACE (importFrom(pbapply, pblapply)) and use it as pblapply() (without the ::).

Customizing the progress bar in your package

Specify the progress bar options in the zzz.R file of the package:

This will set the options and pbapply will not override when loaded.

See a small example package here.

Suppressing the progress bar in your functions

Suppressing the progress bar is sometimes handy. By default, progress bar is suppressed when !interactive(). In other instances, put this inside a function:

pbo <- pboptions(type = "none")
on.exit(pboptions(pbo), add = TRUE)


n <- 2000
x <- rnorm(n)
y <- rnorm(n, model.matrix(~x) %*% c(0,1), sd=0.5)
d <- data.frame(y, x)
## model fitting and bootstrap
mod <- lm(y~x, d)
ndat <- model.frame(mod)
B <- 500
bid <- sapply(1:B, function(i) sample(nrow(ndat), nrow(ndat), TRUE))
fun <- function(z) {
    if (missing(z))
        z <- sample(nrow(ndat), nrow(ndat), TRUE)
    coef(lm(mod$call$formula, data=ndat[z,]))

## standard '*apply' functions
# system.time(res1 <- lapply(1:B, function(i) fun(bid[,i])))
#    user  system elapsed
#   1.096   0.023   1.127
system.time(res2 <- sapply(1:B, function(i) fun(bid[,i])))
#    user  system elapsed
#   1.152   0.017   1.182
system.time(res3 <- apply(bid, 2, fun))
#    user  system elapsed
#   1.134   0.010   1.160
system.time(res4 <- replicate(B, fun()))
#    user  system elapsed
#   1.141   0.022   1.171

## 'pb*apply' functions
## try different settings:
## "none", "txt", "tk", "win", "timer"
op <- pboptions(type="timer") # default
system.time(res1pb <- pblapply(1:B, function(i) fun(bid[,i])))
#    |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% ~00s
#    user  system elapsed
#   1.539   0.046   1.599

system.time(res2pb <- pbsapply(1:B, function(i) fun(bid[,i])))
#   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
#    user  system elapsed
#   1.433   0.045   1.518

pboptions(type="txt", style=1, char="=")
system.time(res3pb <- pbapply(bid, 2, fun))
# ==================================================
#    user  system elapsed
#   1.389   0.032   1.464

pboptions(type="txt", char=":")
system.time(res4pb <- pbreplicate(B, fun()))
#   |::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
#    user  system elapsed
#   1.427   0.040   1.481