Progress updates for 'furrr' 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)
library(furrr)
plan(multisession)

slow_fcn <- function(x) {
  Sys.sleep(0.1)  # emulate work
  x^2
}

xs <- 1:100
ys <- xs |> future_map(slow_fcn) |> progressify()

Introduction

This vignette demonstrates how to use this approach to add progress reporting to furrr functions such as future_map(), future_map_dbl(), and future_walk().

The furrr future_map() function is commonly used to apply a function to the elements of a vector or a list in parallel. For example,

library(furrr)
plan(multisession)

xs <- 1:100
ys <- xs |> future_map(slow_fcn)

Here future_map() provides no feedback on how far it has progressed, but we can easily add progress reporting by using:

library(furrr)
plan(multisession)

library(progressify)
handlers(global = TRUE)

xs <- 1:100
ys <- xs |> future_map(slow_fcn) |> progressify()

Using the default progress handler, the progress reporting will appear as:

  |=====                    |  20%

Supported Functions

The progressify() function supports the following furrr functions: