This is an R package implementing generators and async blocks.

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Generators

g <- gen({...}) allow you to write a block of sequential code that “pauses”. A generator runs until it hits a yield() call, then returns the value. The next time you call the generator it picks up where it left off and runs until the next yield.

From the “outside” a generator implements the iterator interface as defined by the iterators package You extract each yielded value with nextElem(g), and you can use generators anywhere you can use an iterator, and with tools from the iterators or itertools packages.

Example: Collatz sequence

Consider a sequence of numbers x[i], starting with an arbitrary x[1], where each subsequent element is produced by applying the rule:

  • If x[i] is even, then the next value will be x[i+1] = x[i]/2.
  • if x[i] is odd, the next value will be x[i+1] = 3*x[i]+1.

An infinite sequence of numbers will continue form each staring point x[1], but it is conjectured that all sequences will eventually reach the loop 1, 4, 2, 1, 4, 2, …. The following generator produces the Collatz sequence, starting from x, and terminating when (or if?) the sequence reaches 1.

collatz <- function(x) { force(x)
  async::gen({
    yield(x)
    while (x > 1) {
      x <- if (x %% 2 == 0) x / 2L else 3L * x + 1
      yield(x)
    }
  })
}

The call to gen produces a generator. You can get values one at a time with nextElem()

ctz <- collatz(12)
ctz <- collatz(12)
nextElem(ctz)
# [1] 12
nextElem(ctz)
# [1] 6
nextElem(ctz)
# [1] 3
nextElem(ctz)
# [1] 10
nextElem(ctz)
# [1] 5

You can also use any other method that applies to an iterator, like as.list.

collatz(27L) |> as.list |> as.numeric
#   [1]   82   41  124   62   31   94   47  142   71  214  107  322  161  484  242
#  [16]  121  364  182   91  274  137  412  206  103  310  155  466  233  700  350
#  [31]  175  526  263  790  395 1186  593 1780  890  445 1336  668  334  167  502
#  [46]  251  754  377 1132  566  283  850  425 1276  638  319  958  479 1438  719
#  [61] 2158 1079 3238 1619 4858 2429 7288 3644 1822  911 2734 1367 4102 2051 6154
#  [76] 3077 9232 4616 2308 1154  577 1732  866  433 1300  650  325  976  488  244
#  [91]  122   61  184   92   46   23   70   35  106   53  160   80   40   20   10
# [106]    5   16    8    4    2    1
collatz(63728127L) |> as.list |> as.numeric

For more examples, see the “Clapping Music” vignette.

Async/await

Like gen, async({...}) takes a block of sequential code, which runs until it reaches a call to await(p). The argument p should be a promise, (as defined by the promises package, which represents an unfinished external computation.) In turn, async() constructs and returns a promise.

An async block runs until it reaches a call to await(p) and pauses. When the promise p resolves, the async block continues. If p rejects, that is evaluated like an error; you can put await(p) into a tryCatch to handle rejections. When the async block finishes, or throws an error, its promise resolves or rejects.

Examples:

async doesn’t handle running parallel tasks by itself; it builds on existing packages like future and later. The later package lets you assign tasks to be done in the event loop, when R is idle.

Ring a bell 5 times at 10 second intervals (subject to R being idle):

async({
  for (i in 1:5) {
    await(delay(10))  #delay() uses later::later()
    cat("Beep", i, "\n")
    beepr::beep(2)
  }
})

Shiny apps

async() can be used in Shiny apps! For an example, here is a version of the “Cranwhales” demo app using async/await..

Background processing

async can also work with future objects to run computations in parallel. Download, parse, and summarize a dataset in background processes:

library(future)
library(dplyr)
plan(multiprocess(workers=2))

url <- "http://analytics.globalsuperhypermegamart.com/2020/March.csv.gz"
dest <- "March.csv.gz"

dataset <- async({
  if(!file.exists(dest)) {
    await(future({
      cat("Downloading\n")
      download.file(url, dest)
    }))
  }
  data <- await(future({
    cat("Parsing\n")
    read.csv(dest) |>
    mutate(time = hms::trunc_hms(time, 60*60)) |>
    group_by(time) |>
    summarize(sales=sum(amount)) |>
  }))
})

# When the data is ready, plot it (in the main process:)
async({
  await(dataset) |>
  ggplot(aes(time, n)) +
    xlab("Time") +
    ylab("Sales")
})