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progressr: A Unifying API for Progress Updates

Life cycle: experimental

The progressr package provides a minimal API for reporting progress updates in R. The design is to separate the representation of progress updates from how they are presented. What type of progress to signal is controlled by the developer. How these progress updates are rendered is controlled by the end user. For instance, some users may prefer visual feedback such as a horizontal progress bar in the terminal, whereas others may prefer auditory feedback.

Three strokes writing three in Chinese

Design motto:

The developer is responsible for providing progress updates but it's only the end user who decides if, when, and how progress should be presented. No exceptions will be allowed.

Two Minimal APIs

Developer's API End-user's API
p <- progressor(n) with_progress(expr)
p(msg, ...) handlers(...)

A simple example

Assume that we have a function slow_sum() for adding up the values in a vector. It is so slow, that we like to provide progress updates to whoever might be interested in it. With the progressr package, this can be done as:

slow_sum <- function(x) {
  progress <- progressr::progressor(length(x))
  sum <- 0
  for (kk in seq_along(x)) {
    sum <- sum + x[kk]
    progress(message = sprintf("Added %g", x[kk]))

Note how there are no arguments in the code that specifies how progress is presented. The only task for the developer is to decide on where in the code it makes sense to signal that progress has been made. As we will see next, it is up to the end user of this code to decide whether they want to receive progress updates or not, and, if so, in what format.

Without reporting progress

When calling this function as in:

> y <- slow_sum(1:10)
> y
[1] 55

it will behave as any function and there will be no progress updates displayed.

Reporting progress

To get progress updates, we can call it as:

> library(progressr)
> with_progress(y <- slow_sum(1:10))
  |=====================                                |  40%

Customizing how progress is reported

The default is to present progress via utils::txtProgressBar(), which is available on all R installations. To change the default, to, say, progress_bar() by the progress package, set:


This progress handler will present itself as:

> with_progress(y <- slow_sum(1:10))
[==================>---------------------------]  40% Added 4

To set the default progress handler(s) in all your R sessions, call progressr::handlers(...) in your ~/.Rprofile file. An alternative, which avoids loading the progressr package if never used, is to set options(progressr.handlers = progress_handler).

Auditory progress updates

Note all progress updates have to be presented visually. This can equally well be done auditory. For example, using:


will present itself as sounds played at the beginning, while progressing, and at the end (using different beepr sounds). There will be no output written to the terminal;

> with_progress(y <- slow_sum(1:10))
> y
[1] 55

Concurrent auditory and visual progress updates

It is possible to have multiple progress handlers presenting progress updates at the same time. For example, to get both visual and auditory updates, use:

handlers("txtprogressbar", "beepr")

Support for progressr elsewhere

The plyr package

The functions in the plyr package take argument .progress, which can be used to produce progress updates. To have them generate progressr 'progression' updates, use .progress = "progressr". For example,

  y <- plyr::l_ply(1:5, function(x, ...) {
  }, .progress = "progressr")
## |=====================                                |  40%

The future framework

The future framework has built-in support for the kind of progression updates produced by the progressr package. Here is an example that uses future_lapply() of the future.apply package to parallelize on the local machine while at the same time signaling progression updates:


handlers("progress", "beepr")

  p <- progressr::progressor(5)
  y <- future_lapply(1:5, function(x, ...) {
    p(sprintf("x=%g", x))
## [=================>-----------------------------]  40% x=2


Because this project is under active development, the progressr API is currently kept at a very minimum. This will allow for the framework and the API to evolve while minimizing the risk for breaking code that depends on it. The roadmap for developing the API is roughly:

  1. Provide minimal API for producing progress updates, i.e. progressor() and with_progress()

  2. Add support for nested progress updates

  3. Add API to allow users and package developers to design additional progression handlers

For a more up-to-date view on what features might be added, see


Under the hood

When using the progressr package, progression updates are communicated via R's condition framework, which provides methods for creating, signaling, capturing, muffling, and relaying conditions. Progression updates are of classes progression and immediateCondition(*). The below figure gives an example how progression conditions are created, signaled, and rendered.

(*) The immediateCondition class of conditions are relayed as soon as possible by the future framework, which means that progression updates produced in parallel workers are reported to the end user as soon as the main R session have received them.

Figure: Sequence diagram illustrating how signaled progression conditions are captured by with_progress() and relayed to the two progression handlers 'progress' (a progress bar in the terminal) and 'beepr' (auditory) that the end user has chosen.


To debug progress updates, use:

> handlers("debug")
> with_progress(y <- slow_sum(1:10))
[13:33:49.743] (0.000s => +0.002s) initiate: 0/10 (+0) '' {clear=TRUE, enabled=TRUE, status=}
[13:33:49.847] (0.104s => +0.001s) update: 1/10 (+1) 'Added 1' {clear=TRUE, enabled=TRUE, status=}
[13:33:49.950] (0.206s => +0.001s) update: 2/10 (+1) 'Added 2' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.052] (0.309s => +0.000s) update: 3/10 (+1) 'Added 3' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.154] (0.411s => +0.001s) update: 4/10 (+1) 'Added 4' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.257] (0.514s => +0.001s) update: 5/10 (+1) 'Added 5' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.361] (0.618s => +0.002s) update: 6/10 (+1) 'Added 6' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.464] (0.721s => +0.001s) update: 7/10 (+1) 'Added 7' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.567] (0.824s => +0.001s) update: 8/10 (+1) 'Added 8' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.670] (0.927s => +0.001s) update: 9/10 (+1) 'Added 9' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.773] (1.030s => +0.001s) update: 10/10 (+1) 'Added 10' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.774] (1.031s => +0.003s) update: 10/10 (+0) 'Added 10' {clear=TRUE, enabled=TRUE, status=}
[13:33:50.776] (1.033s => +0.001s) shutdown: 10/10 (+0) '' {clear=TRUE, enabled=TRUE, status=ok}


R package progressr is only available via GitHub and can be installed in R as:


Pre-release version

To install the pre-release version that is available in Git branch develop on GitHub, use:


This will install the package from source.


This Git repository uses the Git Flow branching model (the git flow extension is useful for this). The develop branch contains the latest contributions and other code that will appear in the next release, and the master branch contains the code of the latest release.

Contributing to this package is easy. Just send a pull request. When you send your PR, make sure develop is the destination branch on the progressr repository. Your PR should pass R CMD check --as-cran, which will also be checked by Travis CI and when the PR is submitted.

Software status

Resource: GitHub Travis CI AppVeyor
Platforms: Multiple Linux & macOS Windows
R CMD check Build status
Test coverage Coverage Status
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