# 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()`