Progress updates for base-R apply functions

The progressify package allows you to easily add progress reporting to sequential and parallel map-reduce code by piping to the progressify() function. Easy!

TL;DR

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()

Introduction

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%

Supported Functions

The progressify() function supports the following base package functions:

• lapply(), vapply(), sapply(), tapply()
• mapply(), .mapply(), Map()
• eapply()
• apply()
• replicate()
• by()

Combining with futurize

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()

Known issues

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()