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.
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.
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")
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.
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, which
was last 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)
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.
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)
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.
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.
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:
[x] Provide minimal API for producing progress updates,
i.e. progressor(), with_progress(), handlers()
[x] 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.