# Progress updates for 'crossmap' 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(crossmap)

    slow_fcn <- function(x, y) {
      Sys.sleep(0.1)  # emulate work
      x * y
    }

    xs <- list(1:5, 1:5)
    ys <- xmap(xs, slow_fcn) |> progressify()

# Introduction

This vignette demonstrates how to use this approach to add progress reporting to crossmap functions such as `xmap()`.

The crossmap package extends purrr with functions that apply a function to every combination of elements in a list of inputs. For example, `xmap()` computes the cross product of its inputs:

    library(crossmap)
    xs <- list(1:5, 1:5)
    ys <- xmap(xs, slow_fcn)

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

    library(crossmap)

    library(progressify)
    handlers(global = TRUE)

    xs <- list(1:5, 1:5)
    ys <- xmap(xs, slow_fcn) |> progressify()

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

      |=====                    |  20%

# Supported Functions

The `progressify()` function supports the following crossmap functions:

- `xmap()` and variants (`xmap_chr()`, `xmap_dbl()`, `xmap_int()`, `xmap_lgl()`, `xmap_vec()`, `xmap_dfc()`, `xmap_dfr()`, `xmap_mat()`, `xmap_arr()`)
- `xwalk()`
- `map_vec()`, `map2_vec()`, `pmap_vec()`, `imap_vec()`