progressr: Appendix

How tos

Report on progress in non-interactive mode (“batch mode”)

When running R from the command line, R runs in a non-interactive mode (interactive() returns FALSE). The default behavior of progressr is to not report on progress in non-interactive mode. To reported on progress also then, set R options progressr.enable or environment variable R_PROGRESSR_ENABLE to TRUE. For example,

$ Rscript -e "library(progressr)" -e "with_progress(y <- slow_sum(1:10))"

will not report on progress, whereas

$ export R_PROGRESSR_ENABLE=TRUE
$ Rscript -e "library(progressr)" -e "with_progress(y <- slow_sum(1:10))"

will.

Notes of caution

Avoid sending progress updates too frequently

Signaling progress updates comes with some overhead. In situation where we use progress updates, this overhead is typically much smaller than the task we are processing in each step. However, if the task we iterate over is quick, then the extra time induced by the progress updates might end up dominating the overall processing time. If that is the case, a simple solution is to only signal progress updates every n:th step. Here is a version of slow_sum() that signals progress every 10:th iteration:

slow_sum <- function(x) {
  p <- progressr::progressor(length(x) / 10)
  sum <- 0
  for (kk in seq_along(x)) {
    Sys.sleep(0.1)
    sum <- sum + x[kk]
    if (kk %% 10 == 0) p(message = sprintf("Adding %g", x[kk]))
  }
  sum
}

The overhead of progress signaling may depend on context. For example, in parallel processing with near-live progress updates via ‘multisession’ futures, each progress update is communicated via a socket connections back to the main R session. These connections might become clogged up if progress updates are too frequent.

Known Limitations

The global progress handler cannot be set during package load

It is not possible to call handlers(global = TRUE) in all circumstances. For example, it cannot be called within tryCatch() and withCallingHandlers();

> tryCatch(handlers(global = TRUE), error = identity)
Error in globalCallingHandlers(NULL) : 
  should not be called with handlers on the stack

This is not a bug - neither in progressr nor in R itself. It’s due to a conservative design on how global calling handlers should work in R. If it allowed, there’s a risk we might end up getting weird and unpredictable behaviors when messages, warnings, errors, and other types of conditions are signaled.

Because tryCatch() and withCallingHandlers() is used in many places throughout base R, this means that we also cannot call handlers(global = TRUE) as part of a package’s startup process, e.g. .onLoad() or .onAttach().

Another example of this error is if handlers(global = TRUE) is used inside package vignettes and dynamic documents such as Rmarkdown. In such cases, the global progress handler has to be enabled prior to processing the document, e.g.

> progressr::handlers(global = TRUE)
> rmarkdown::render("input.Rmd")

A progressor cannot be created in the global environment

It is not possible to create a progressor in the global environment, e.g. in the the top-level of a script. It can only be created inside a function, within with_progress({ ... }), local({ ... }), or a similar construct. For example, the following:

library(progressr)
handlers(global = TRUE)

xs <- 1:5
p <- progressor(along = xs)
y <- lapply(xs, function(x) {
  Sys.sleep(0.1)
  p(sprintf("x=%g", x))
  sqrt(x)
})

results in an error if tried:

Error in progressor(along = xs) : 
  A progressor must not be created in the global environment unless wrapped in a
  with_progress() or without_progress() call. Alternatively, create it inside a
  function or in a local() environment to make sure there is a finite life span
  of the progressor

The solution is to wrap it in a local({ ... }) call, or more explicitly, in a with_progress({ ... }) call:

library(progressr)
handlers(global = TRUE)

xs <- 1:5
with_progress({
  p <- progressor(along = xs)
  y <- lapply(xs, function(x) {
    Sys.sleep(0.1)
    p(sprintf("x=%g", x))
    sqrt(x)
  })
})
#  |====================                               |  40%

The main reason for this is to limit the life span of each progressor. If we created it in the global environment, there is a significant risk it would never finish and block all of the following progressors.

Known Issues

Positron

Setting global progressr handlers during startup does not work

Positron does not support setting global calling handlers during R’s startup process, e.g. in ~/.Rprofile. Even if such handlers are registered, they have no effect. This is a bug in Positron (2025-03-20), which was most recently confirmed with Position 2025.09.0 on Linux. Because of this, having something like in your ~/.Rprofile:

if (requireNamespace("progressr", quietly = TRUE)) {
  progressr::handlers(global = TRUE)
}

will have no effect. If used, the workaround is to manually re-registering all calling handlers at the R prompt, which can be done as:

globalCallingHandlers(globalCallingHandlers(NULL))

Alternatively, call:

progressr::handlers(global = FALSE)  ## important
progressr::handlers(global = TRUE)

Messages add a extra newline before the final progress step

One of the features of progressr is that messages are buffered during progress reporting and relayed as soon as possible, which typically happens just before handlers re-render the progress output. This way you can use message() as usual, regardless whether progress is reported or not.

Currently, when using Positron (e.g. Positron 2025.09.0), any message() output adds an extra newline before the final progress step is reported, e.g.

> progressr::handlers(global = TRUE)
> progressr::handlers("cli")
> y <- progressr::slow_sum(1:5, message = TRUE)
M: Added value 1
M: Added value 2
M: Added value 3
M: Added value 4

M: Added value 5
>

I do not fully understand the reason for this, but I hope we can get to the bottom of it and fix it, either in progressr or in Positron, e.g. Bug #9486 (2025-09-18).

Jupyter Notebook and Jupyter Lab

Reporting progress to stderr does not work

The default for most terminal progress renders, including the ones for progressr, display the progress on standard error (stderr). Due to limitation in Jupyter, this default does not work there. The reason is that Jupyter silently drops any output send to stderr, e.g.

> cat("hello stderr\n", file = stderr())
> cat("hello stdout\n", file = stdout())
hello stdout
>

If we try the following

library(progressr)
handlers(globals = TRUE)
handlers("txtprogressbar")
y <- slow_sum(1:20)

there will be no progress being reported. This is not specific to progressr, we have the same problem with for instance cli. Try for instance,

void <- cli::cli_progress_demo(delay = 1.0)

The workaround is to direct all progress output to the standard output (stdout) when working in Jupyter. For this to work, we also need to disable the buffering (“delaying”) of any other output to stdout.

library(progressr)
handlers(globals = TRUE)

## Workaround for Jupyter
options(progressr.enable = TRUE, progressr.delay_stdout = FALSE)

## Jupyter requires that progress is rendered to standard output;
## it does not work with the default standard error
handlers(handler_txtprogressbar(file = stdout()))

y <- slow_sum(1:20)

handlers(“progress”) output is messy

Jupyter has other outputting issues. Specifically, Jupyter injects an extra newline at the end of every message, e.g.

> message("abc", appendLF = FALSE); message("def", appendLF = FALSE)
abc
def
> message("abc"); message("def")
abc

def

>

This causes any progress framework (e.g. the progress package) that reports via messages to render progress output very poorly or not at all.

Design and Implementation

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(), or the global progression handler, and relayed to the two progression handlers ‘progress’ (a progress bar in the terminal) and ‘beepr’ (auditory) that the end user has chosen.

Roadmap

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:

Provide minimal API for producing progress updates, i.e. progressor(), with_progress(), handlers()
Add support for global progress handlers removing the need for the user having to specify with_progress(), i.e. handlers(global = TRUE) and handlers(global = FALSE)
Make it possible to create a progressor also in the global environment (see ‘Known issues’ above)
Add support for nested progress updates
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 https://github.com/futureverse/progressr/issues.

Henrik Bengtsson