Progress updates for 'stats' functions

Henrik Bengtsson

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(stats)

d <- as.dendrogram(hclust(dist(USArrests)))
d2 <- dendrapply(d, function(n) { Sys.sleep(0.01); n }) |> progressify()

Introduction

This vignette demonstrates how to use this approach to add progress reporting to functions such as dendrapply() in the stats package. For example, consider the dendrapply() function, which is commonly used to apply a function to the nodes of a dendrogram, as in:

d <- as.dendrogram(hclust(dist(USArrests)))
d2 <- dendrapply(d, function(n) { Sys.sleep(0.01); n })

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

library(progressify)
handlers(global = TRUE)

d2 <- dendrapply(d, function(n) { Sys.sleep(0.01); n }) |> progressify()

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

  |=====                    |  20%

Supported Functions

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

• dendrapply()