# Introduction

library(cli)
options(cli.progress_show_after = 0)
options(cli.progress_clear = FALSE)

This document discusses the structure and simplest uses of the cli progress bar API. For more advanced usage and the C progress bar API, see the ‘Advanced cli progress bars’ article and the manual pages.

From version 3.0.0 cli provides a set of functions to create progress bars. The main goals of the progress bar API are:

• Reduce clutter. Try to avoid verbose syntax, unless necessary.
• Flexibility from R and C/C++ code. Support all cli features in progress bars: glue interpolation, theming, pluralization, etc.
• Predictably small performance penalty. A very small constant penalty per iteration, and a reasonable penalty per second.

# The traditional progress bar API

Add a progress bar in three steps:

1. Call cli_progress_bar() to add create a progress bar.
2. Call cli_progress_update() to update it.
3. Call cli_progress_done() to terminate it.

For example:

clean <- function() {
cli_progress_bar("Cleaning data", total = 100)
for (i in 1:100) {
Sys.sleep(5/100)
cli_progress_update()
}
cli_progress_done()
}
clean()

The traditional API provides full control w.r.t when to create, update and terminate a progress bar.

# The current progress bar

For conciseness, the progress bar functions refer to the current progress bar by default. Every function has at most one current progress bar at any time. The current progress bar of a function is terminated when the function creates another progress bar or when the function returns, errors or is interrupted.

The current progress bar lets us omit the cli_progress_done() call:

clean <- function() {
cli_progress_bar("Cleaning data #1", total = 100)
for (i in 1:100) {
Sys.sleep(3/100)
cli_progress_update()
}

cli_progress_bar("Cleaning data #2", total = 100)
for (i in 1:100) {
Sys.sleep(3/100)
cli_progress_update()
}
}
clean()

# Unknown total number of units

In some cases the total number of progress units is unknown, so simply omit them from cli_progress_bar() (or set them to NA). cli uses a different display when total is unknown:

walk_dirs <- function() {
cli_progress_bar("Walking directories")
while (TRUE) {
if (runif(1) < 0.01) break
Sys.sleep(0.01)
cli_progress_update()
}
cli_progress_update(force = TRUE)
}
walk_dirs()

# Quick loops

By default, cli does not show progress bars that are terminated within two seconds after their creation. The end user can configure this limit with the cli.progress_show_after global option.

For example, in this document we set the limit to zero seconds, so progress bars are shown at their first update.

# Progress bars for mapping functions: cli_progress_along()

cli_progress_along() is currently experimental.

To add a progress bar to a call to lapply() or another mapping function, wrap the input sequence into cli_progress_along():

lapply(cli_progress_along(X), fun)

cli_progress_along() works similarly to seq_along(), it returns an index vector. If you use cli_progress_along(), then lapply() will pass the indices of the elements in X to fun, instead of the elements themselves.

cli_progress_along() expects that the index vector will be used only once, from beginning to end. It is best to never assign the return value of cli_progress_along() to a variable.

An example:

f <- function() {
rawabc <- lapply(
cli_progress_along(letters),
function(i) {
charToRaw(letters[i])
Sys.sleep(0.5)
}
)
}
f()

cli_progress_along() uses ALTREP, so it only works from R 3.5.0 and later. On older R versions it is equivalent to seq_along() and it does not create a progress bar.

## for loops

You can also use cli_progress_along() in for loops, with the additional complication that if you use break, then you might need to terminate the progress bar explicitly:

for (i in cli_progress_along(seq)) {
...
if (cond) cli_progress_done() && break
...
}

cli_progress_done() always returns TRUE to allow this form.

Alternatively, you can terminate the progress bar right after loop:

for (i in cli_progress_along(seq)) {
...
if (cond) break
...
}
cli_progress_done()

If the function containing the for loop returns after the loop, or you create another progress bar with cli_progress_along() or cli_progress_bar(), then no explicit cli_progress_done() is needed.

# Simplified API

Often you don’t need the full power of the progress bar API, and only want to show a status message. The cli_progress_message() and cli_progress_step() functions are tailored for this.

cli_progress_message() shows a (potentially templated) message in the status bar. For convenience, the progress bar rules still apply here by default: * Status messages are removed when their calling function exits. * A status message removes the previous status message or progress bar of the same caller function.

f <- function() {
Sys.sleep(2)

Sys.sleep(2)

step <- 1L
cli_progress_message("Task three is underway: step {step}")
for (step in 1:5) {
Sys.sleep(0.5)
cli_progress_update()
}
}
f()

Status messages may use glue interpolation, cli styling and pluralization, as usual. You can call cli_progress_update() to update a status message.

cli_progress_step() is slightly different from cli_progress_message(): * it adds cli’s alert themes to the status messages (info, success or danger), * prints the duration of each step (by default), and * it keeps the messages on the screen after they are terminated.

f <- function() {
Sys.sleep(2)

cli_progress_step("Importing data")
Sys.sleep(1)

cli_progress_step("Cleaning data")
Sys.sleep(2)

cli_progress_step("Fitting model")
Sys.sleep(3)
}
f()

As usual, you can use cli_progress_step() to update an existing status message.

f <- function(n = 10) {
i <- 0
cli_progress_step("Got {i}/{n} {qty(i)}file{?s}.")
for (i in seq_len(n)) {
Sys.sleep(0.5)
cli_progress_update()
}
}
f()

If you can update the status message frequently enough, then you can also add a spinner to it:

f <- function() {
for (i in 1:100) { cli_progress_update(); Sys.sleep(2/100) }
cli_progress_step("Importing data", spinner = TRUE)
for (i in 1:100) { cli_progress_update(); Sys.sleep(1/100) }
cli_progress_step("Cleaning data", spinner = TRUE)
for (i in 1:100) { cli_progress_update(); Sys.sleep(2/100) }
cli_progress_step("Fitting model", spinner = TRUE)
for (i in 1:100) { cli_progress_update(); Sys.sleep(3/100) }
}
f()

cli_progress_step() automatically handles errors, and styles the status message accordingly:

f <- function() {
cli_progress_step("First step, this will succeed")
Sys.sleep(1)
cli_progress_step("Second step, this will fail")
Sys.sleep(1)
stop("Something is wrong here")
}
f()