A progressbar that fits into a dplyr::mutate -> purrr::map* pipeline

spawn_progressbar.R

library(rlang)
library(tidyr)
library(dplyr)
library(purrr)

spawn_progressbar <- function(x, .name = .pb, .times = 1) {
  .name <- substitute(.name)
  n <- nrow(x) * .times
  eval(substitute(.name <<- dplyr::progress_estimated(n)))
  x
}

## make function to be map'ed accept progressbar as argument and
## update on call
slow_mean <- function(x, .var, .pb) {
  Sys.sleep(1)
  .pb$tick()$print()
  .var <- rlang::enexpr(.var)
  mean(x[[.var]])
}

## setting up and updating progressbar is now part of the pipeline;
## it's default name is .pb
mtcars %>%
  dplyr::group_by(carb) %>%
  tidyr::nest() %>%
  spawn_progressbar() %>%
  dplyr::mutate(mean_qsec = purrr::map_dbl(data, slow_mean, qsec, .pb))

## this means we can have multiple sequential progress bars, like so:
mtcars %>%
  dplyr::group_by(carb) %>%
  tidyr::nest() %>%
  spawn_progressbar(.pb_qsec) %>%
  spawn_progressbar(.pb_disp) %>%
  dplyr::mutate(mean_qsec = purrr::map_dbl(data, slow_mean, qsec, .pb_qsec),
                mean_disp = purrr::map_dbl(data, slow_mean, disp, .pb_disp))

## or one progress bar for all computations by knowing the number of
## calls to map* in advance, like so:
mtcars %>%
  dplyr::group_by(carb) %>%
  tidyr::nest() %>%
  spawn_progressbar(.times = 2) %>%
  dplyr::mutate(mean_qsec = purrr::map_dbl(data, slow_mean, qsec, .pb),
                mean_disp = purrr::map_dbl(data, slow_mean, disp, .pb))