Progress updates for 'plyr' 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(plyr)

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

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

Introduction

This vignette demonstrates how to use this approach to add progress reporting to plyr functions such as llply(), maply(), and ddply().

The plyr llply() function is commonly used to apply a function to the elements of a list and return a list. For example,

library(plyr)
xs <- 1:100
ys <- llply(xs, slow_fcn)

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

library(plyr)

library(progressify)
handlers(global = TRUE)

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

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

  |=====                    |  20%

Supported Functions

The progressify() function supports the following plyr functions:

• l_ply(), laply(), ldply(), llply()
• m_ply(), maply(), mdply(), mlply()
• r_ply(), raply(), rdply(), rlply()