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The progressify package allows you to easily add progress
reporting to sequential and parallel map-reduce code by piping to the
progressify() function. Easy!
library(progressify)
handlers(global = TRUE)
library(foreach)
slow_fcn <- function(x) {
Sys.sleep(0.1) # emulate work
x^2
}
xs <- 1:100
ys <- foreach(x = xs) %do% slow_fcn(x) |> progressify()
This vignette demonstrates how to use this approach to add progress
reporting to the foreach foreach() construct and the
doFuture %dofuture% operator.
For example, consider:
library(foreach)
xs <- 1:100
ys <- foreach(x = xs) %do% slow_fcn(x)
This foreach() construct provides no feedback on how far it has
progressed. We can easily add progress reporting by piping to
progressify():
library(foreach)
library(progressify)
handlers(global = TRUE)
xs <- 1:100
ys <- foreach(x = xs) %do% slow_fcn(x) |> progressify()
Using the default progress handler, the progress reporting will appear as:
|===== | 20%
The same approach works with the doFuture package for parallel foreach evaluation:
library(doFuture)
plan(multisession)
library(progressify)
handlers(global = TRUE)
xs <- 1:100
ys <- foreach(x = xs) %dofuture% slow_fcn(x) |> progressify()
The progressify() function supports the following foreach
operators:
foreach(...) %do% { ... }foreach(...) %dopar% { ... }and the following doFuture operator:
foreach(...) %dofuture% { ... }These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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