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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)
slow_fcn <- function(x) {
Sys.sleep(0.1) # emulate work
x^2
}
xs <- 1:100
ys <- lapply(xs, slow_fcn) |> progressify()
This vignette demonstrates how to use this approach to add progress
reporting to functions such as lapply(), tapply(), apply(), and
replicate() in the base package. For example, consider the base
R lapply() function, which is commonly used to apply a function to
the elements of a vector or a list, as in:
xs <- 1:100
ys <- lapply(xs, slow_fcn)
Here lapply() provides no feedback on how far it has progressed,
but we can easily add progress reporting by using:
library(progressify)
handlers(global = TRUE)
ys <- lapply(xs, slow_fcn) |> progressify()
Using the default progress handler, the progress reporting will appear as:
|===== | 20%
The progressify() function supports the following base package
functions:
lapply(), vapply(), sapply(), tapply()mapply(), .mapply(), Map()eapply()apply()replicate()by()The progressify package works together with the futurize package. You can both parallelize and add progress reporting in a single pipeline:
library(futurize)
plan(multisession)
library(progressify)
handlers(global = TRUE)
xs <- 1:100
ys <- lapply(xs, slow_fcn) |> futurize() |> progressify()
The BiocGenerics package defines generic functions lapply(),
sapply(), mapply(), and tapply(). These S4 generic functions
override the non-generic, counterpart functions in the base
package. If BiocGenerics is attached, the solution is to specify
that it is the base version we wish to progressify, i.e.
y <- base::lapply(1:3, sqrt) |> progressify()
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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