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 - One For Developers and One For End-Users
## 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:
```r
slow_sum <- function(x) {
p <- progressr::progressor(along = x)
sum <- 0
for (kk in seq_along(x)) {
Sys.sleep(0.1)
sum <- sum + x[kk]
p(message = sprintf("Adding %g", x[kk]))
}
sum
}
```
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 on progress
When calling this function as in:
```r
> y <- slow_sum(1:10)
> y
[1] 55
>
```
it will behave as any function and there will be no progress
updates displayed.
### Reporting on progress
If we are only interested in progress for a particular call, we can
do:
```r
> library(progressr)
> with_progress(y <- slow_sum(1:10))
|==================== | 40%
```
However, if we want to report on progress from _every_ call, wrapping
the calls in `with_progress()` might become too cumbersome. If so, we
can enable the global progress handler:
```r
> library(progressr)
> handlers(global = TRUE)
```
so that progress updates are reported on wherever signaled, e.g.
```r
> y <- slow_sum(1:10)
|==================== | 40%
> y <- slow_sum(10:1)
|======================================== | 80%
```
This requires R 4.0.0 or newer. To disable this again, do:
```r
> handlers(global = FALSE)
```
In the below examples, we will assume `handlers(global = TRUE)` is
already set.
## Customizing how progress is reported
### Terminal-based progress bars
The default is to present progress via `utils::txtProgressBar()`,
which is available on all R installations. It presents itself as an
ASCII-based horizontal progress bar in the R terminal. This is
rendered as:
We can tweak this "txtprogressbar" handler to use red hearts for the
bar, e.g.
```r
handlers(handler_txtprogressbar(char = cli::col_red(cli::symbol$heart)))
```
which results in:
Another example is:
```r
handlers(handler_pbcol(
adjust = 1.0,
complete = function(s) cli::bg_red(cli::col_black(s)),
incomplete = function(s) cli::bg_cyan(cli::col_black(s))
))
```
which results in:
To change the default, to, say, `cli_progress_bar()` by the **[cli]**
package, set:
```r
handlers("cli")
```
This progress handler will present itself as:
To instead use `progress_bar()` by the **[progress]** package, set:
```r
handlers("progress")
```
This progress handler will present itself as:
To set the default progress handler, or handlers, in all your R
sessions, call `progressr::handlers(...)` in your
~/.Rprofile startup file.
### Auditory progress updates
Progress updates do not have to be presented visually. They can
equally well be communicated via audio. For example, using:
```r
handlers("beepr")
```
will present itself as sounds played at the beginning, while progressing, and at the end (using different **[beepr]** sounds). There will be _no_ output written to the terminal;
```r
> y <- slow_sum(1:10)
> y
[1] 55
>
```
### Concurrent auditory and visual progress updates
It is possible to have multiple progress handlers presenting progress
updates at the same time. For example, to get both visual and
auditory updates, use:
```r
handlers("txtprogressbar", "beepr")
```
### Silence all progress
To silence all progress updates, use:
```r
handlers("void")
```
### Further configuration of progress handlers
Above we have seen examples where the `handlers()` takes one or more
strings as input, e.g. `handlers(c("progress", "beepr"))`. This is
short for a more flexible specification where we can pass a list of
handler functions, e.g.
```r
handlers(list(
handler_progress(),
handler_beepr()
))
```
With this construct, we can make adjustments to the default behavior
of these progress handlers. For example, we can configure the
`format`, `width`, and `complete` arguments of
`progress::progress_bar$new()`, and tell **beepr** to use a different
`finish` sound and generate sounds at most every two seconds by
setting:
```r
handlers(list(
handler_progress(
format = ":spin :current/:total (:message) [:bar] :percent in :elapsed ETA: :eta",
width = 60,
complete = "+"
),
handler_beepr(
finish = "wilhelm",
interval = 2.0
)
))
```
## Sticky messages
As seen above, some progress handlers present the progress message as
part of its output, e.g. the "progress" handler will display the
message as part of the progress bar. It is also possible to "push"
the message up together with other terminal output. This can be done
by adding class attribute `"sticky"` to the progression signaled.
This works for several progress handlers that output to the terminal.
For example, with:
```r
slow_sum <- function(x) {
p <- progressr::progressor(along = x)
sum <- 0
for (kk in seq_along(x)) {
Sys.sleep(0.1)
sum <- sum + x[kk]
p(sprintf("Step %d", kk), class = if (kk %% 5 == 0) "sticky", amount = 0)
p(message = sprintf("Adding %g", x[kk]))
}
sum
}
```
we get
```r
> handlers("txtprogressbar")
> y <- slow_sum(1:30)
Step 5
Step 10
|==================== | 43%
```
and
```r
> handlers("progress")
> y <- slow_sum(1:30)
Step 5
Step 10
/ [===============>-------------------------] 43% Adding 13
```
## Use regular output as usual alongside progress updates
In contrast to other progress-bar frameworks, output from `message()`,
`cat()`, `print()` and so on, will _not_ interfere with progress
reported via **progressr**. For example, say we have:
```r
slow_sqrt <- function(xs) {
p <- progressor(along = xs)
lapply(xs, function(x) {
message("Calculating the square root of ", x)
Sys.sleep(2)
p(sprintf("x=%g", x))
sqrt(x)
})
}
```
we will get:
```r
> library(progressr)
> handlers(global = TRUE)
> handlers("progress")
> y <- slow_sqrt(1:8)
Calculating the square root of 1
Calculating the square root of 2
- [===========>-----------------------------------] 25% x=2
```
This works because **progressr** will briefly buffer any output
internally and only release it when the next progress update is
received just before the progress is re-rendered in the terminal.
This is why you see a two second delay when running the above example.
Note that, if we use progress handlers that do not output to the
terminal, such as `handlers("beepr")`, then output does not have to be
buffered and will appear immediately.
_Comment_: When signaling a warning using `warning(msg, immediate. =
TRUE)` the message is immediately outputted to the standard-error
stream. However, this is not possible to emulate when warnings are
intercepted using calling handlers, which are used by
`with_progress()`. This is a limitation of R that cannot be worked
around. Because of this, the above call will behave the same as
`warning(msg)` - that is, all warnings will be buffered by R
internally and released only when all computations are done.
## Support for progressr elsewhere
Note that progression updates by **progressr** is designed to work out
of the box for any iterator framework in R. Below is an set of
examples for the most common ones.
### Base R Apply Functions
```r
library(progressr)
handlers(global = TRUE)
my_fcn <- function(xs) {
p <- progressor(along = xs)
lapply(xs, function(x) {
Sys.sleep(0.1)
p(sprintf("x=%g", x))
sqrt(x)
})
}
my_fcn(1:5)
# |==================== | 40%
```
### The foreach package
```r
library(foreach)
library(progressr)
handlers(global = TRUE)
my_fcn <- function(xs) {
p <- progressor(along = xs)
foreach(x = xs) %do% {
Sys.sleep(0.1)
p(sprintf("x=%g", x))
sqrt(x)
}
}
my_fcn(1:5)
# |==================== | 40%
```
### The purrr package
```r
library(purrr)
library(progressr)
handlers(global = TRUE)
my_fcn <- function(xs) {
p <- progressor(along = xs)
map(xs, function(x) {
Sys.sleep(0.1)
p(sprintf("x=%g", x))
sqrt(x)
})
}
my_fcn(1:5)
# |==================== | 40%
```
### The plyr package
```r
library(plyr)
library(progressr)
handlers(global = TRUE)
my_fcn <- function(xs) {
p <- progressor(along = xs)
llply(xs, function(x, ...) {
Sys.sleep(0.1)
p(sprintf("x=%g", x))
sqrt(x)
})
}
my_fcn(1:5)
# |==================== | 40%
```
Note how this solution does not make use of **plyr**'s `.progress`
argument, because the above solution is more powerful and more
flexible, e.g. we have more control on progress updates and their
messages. However, if you prefer the traditional **plyr** approach,
you can use `.progress = "progressr"`, e.g. `y <- llply(..., .progress
= "progressr")`.
### The knitr package
When compiling ("knitting") an knitr-based vignette, for instance, via
`knitr::knit()`, **[knitr]** shows the progress of code chunks
processed thus far using a progress bar. In **knitr** (>= 1.42) [to
be released as of 2022-12-12], we can use **progressr** for this
progress reporting. To do this, set R option `knitr.progress.fun` as:
```r
options(knitr.progress.fun = function(total, labels) {
p <- progressr::progressor(total, on_exit = FALSE)
list(
update = function(i) p(sprintf("chunk: %s", labels[i])),
done = function() p(type = "finish")
)
})
```
This configures **knitr** to signal progress via the **progressr**
framework. To report on these, use:
```r
progressr::handlers(global = TRUE)
```
### Replace any cli progress bars with progressr updates
The **cli** package is used for progress reporting by many several
packages, notably tidyverse packages. For instance, in **purrr**, you
can do:
```r
y <- purrr::map(1:100, \(x) Sys.sleep(0.1), .progress = TRUE)
```
to report on progress via the **cli** package as `map()` is iterating
over the elements. Now, instead of using the default, built-in
**cli** progress bar, we can customize **cli** to report on progress
via **progressr** instead. To do this, set R option
`cli.progress_handlers` as:
```r
options(cli.progress_handlers = "progressr")
```
With this option set, **cli** will now report on progress according to
your `progressr::handlers()` settings. For example, with:
```r
progressr::handlers(c("beepr", "rstudio"))
```
will report on progress using **beepr** and the RStudio Console
progress panel.
To make **cli** report via **progressr** in all your R session, set
the above R option in your ~/.Rprofile startup file.
_Note:_ A **cli** progress bar can have a "name", which can be
specfied in **purrr** function via argument `.progress`,
e.g. `.progress = "processing"`. This name is then displayed in front
of the progress bar. However, because the **progressr** framework
does not have a concept of progress "name", they are silently ignored
when using `options(cli.progress_handlers = "progressr")`.
## Parallel processing and progress updates
The **[future]** framework, which provides a unified API for parallel
and distributed processing in R, has built-in support for the kind of
progression updates produced by the **progressr** package. This means
that you can use it with for instance **[future.apply]**, **[furrr]**,
and **[foreach]** with **[doFuture]**, and **[plyr]** or
**[BiocParallel]** with **doFuture**. In contrast, _non-future_
parallelization methods such as **parallel**'s `mclapply()` and,
`parallel::parLapply()`, and **foreach** adapters like **doParallel**
do _not_ support progress reports via **progressr**.
### future_lapply() - parallel lapply()
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:
```r
library(future.apply)
plan(multisession)
library(progressr)
handlers(global = TRUE)
handlers("progress", "beepr")
my_fcn <- function(xs) {
p <- progressor(along = xs)
future_lapply(xs, function(x, ...) {
Sys.sleep(6.0-x)
p(sprintf("x=%g", x))
sqrt(x)
})
}
my_fcn(1:5)
# / [================>-----------------------------] 40% x=2
```
### foreach() with doFuture
Here is an example that uses `foreach()` of the **[foreach]** package
together with `%dofuture%` of the **[doFuture]** package to
parallelize while reporting on progress. This example parallelizes on
the local machine, it works alsof for remote machines:
```r
library(doFuture) ## %dofuture%
plan(multisession)
library(progressr)
handlers(global = TRUE)
handlers("progress", "beepr")
my_fcn <- function(xs) {
p <- progressor(along = xs)
foreach(x = xs) %dofuture% {
Sys.sleep(6.0-x)
p(sprintf("x=%g", x))
sqrt(x)
}
}
my_fcn(1:5)
# / [================>-----------------------------] 40% x=2
```
For existing code using the traditional `%dopar%` operators of the
**[foreach]** package, we can register the **[doFuture]** adaptor and
use the same **progressr** as above to progress updates;
```r
library(doFuture)
registerDoFuture() ## %dopar% parallelizes via future
plan(multisession)
library(progressr)
handlers(global = TRUE)
handlers("progress", "beepr")
my_fcn <- function(xs) {
p <- progressor(along = xs)
foreach(x = xs) %dopar% {
Sys.sleep(6.0-x)
p(sprintf("x=%g", x))
sqrt(x)
}
}
my_fcn(1:5)
# / [================>-----------------------------] 40% x=2
```
### future_map() - parallel purrr::map()
Here is an example that uses `future_map()` of the **[furrr]** package
to parallelize on the local machine while at the same time signaling
progression updates:
```r
library(furrr)
plan(multisession)
library(progressr)
handlers(global = TRUE)
handlers("progress", "beepr")
my_fcn <- function(xs) {
p <- progressor(along = xs)
future_map(xs, function(x) {
Sys.sleep(6.0-x)
p(sprintf("x=%g", x))
sqrt(x)
})
}
my_fcn(1:5)
# / [================>-----------------------------] 40% x=2
```
_Note:_ This solution does not involved the `.progress = TRUE`
argument that **furrr** implements. Because **progressr** is more
generic and because `.progress = TRUE` only supports certain future
backends and produces errors on non-supported backends, I recommended
to stop using `.progress = TRUE` and use the **progressr** package
instead.
### BiocParallel::bplapply() - parallel lapply()
Here is an example that uses `bplapply()` of the **[BiocParallel]**
package to parallelize on the local machine while at the same time
signaling progression updates:
```r
library(BiocParallel)
library(doFuture)
register(DoparParam()) ## BiocParallel parallelizes via %dopar%
registerDoFuture() ## %dopar% parallelizes via future
plan(multisession)
library(progressr)
handlers(global = TRUE)
handlers("progress", "beepr")
my_fcn <- function(xs) {
p <- progressor(along = xs)
bplapply(xs, function(x) {
Sys.sleep(6.0-x)
p(sprintf("x=%g", x))
sqrt(x)
})
}
my_fcn(1:5)
# / [================>-----------------------------] 40% x=2
```
### plyr::llply(..., .parallel = TRUE) with doFuture
Here is an example that uses `llply()` of the **[plyr]** package to
parallelize on the local machine while at the same time signaling
progression updates:
```r
library(plyr)
library(doFuture)
registerDoFuture() ## %dopar% parallelizes via future
plan(multisession)
library(progressr)
handlers(global = TRUE)
handlers("progress", "beepr")
my_fcn <- function(xs) {
p <- progressor(along = xs)
llply(xs, function(x, ...) {
Sys.sleep(6.0-x)
p(sprintf("x=%g", x))
sqrt(x)
}, .parallel = TRUE)
}
my_fcn(1:5)
# / [================>-----------------------------] 40% x=2
```
_Note:_ As an alternative to the above, recommended approach, one can
use `.progress = "progressr"` together with `.parallel = TRUE`. This
requires **plyr** (>= 1.8.7).
### Near-live versus buffered progress updates with futures
As of November 2020, there are four types of **future** backends that are known(*) to provide near-live progress updates:
1. `sequential`,
2. `multicore`,
3. `multisession`, and
4. `cluster` (local and remote)
Here "near-live" means that the progress handlers will report on
progress almost immediately when the progress is signaled on the
worker. For all other future backends, the progress updates are only
relayed back to the main machine and reported together with the
results of the futures. For instance, if `future_lapply(X, FUN)`
chunks up the processing of, say, 100 elements in `X` into eight
futures, we will see progress from each of the 100 elements as they
are done when using a future backend supporting "near-live" updates,
whereas we will only see those updated to be flushed eight times when
using any other types of future backends.
(*) Other future backends may gain support for "near-live" progress
updating later. Adding support for those is independent of the
**progressr** package. Feature requests for adding that support
should go to those future-backend packages.
## Note of caution - 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.
## Progress updates 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,
```sh
$ Rscript -e "library(progressr)" -e "with_progress(y <- slow_sum(1:10))"
```
will _not_ report on progress, whereas
```sh
$ export R_PROGRESSR_ENABLE=TRUE
$ Rscript -e "library(progressr)" -e "with_progress(y <- slow_sum(1:10))"
```
will.
## 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:
* [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' below)
* [ ] 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