Do it well, do it fast 🏎️

Functional programming with purrr, parallelized with mirai

Functions instead of loops; do multiple things at the same time. Learn purrr’s functional style, then parallelize it with a single wrapper function.

“We build our computer (systems) the way we build our cities: over time, without a plan, on top of ruins.”
–Ellen Ullman

Level: Advanced  ·  Time: ~45 min  ·  Prerequisites: Everything in its right place  ·  Tools: purrr, mirai

You will learn to

• Replace a for-loop with purrr’s map() family, functionally.
• Iterate over two or more lists in parallel with map2()/pmap().
• Understand why a self-contained function is what makes parallelisation possible at all.
• Parallelise existing purrr code across cores with mirai’s in_parallel(), without rewriting it.

PART I: The for-loop has to go

You already know the shape of this problem. You have a list of things –countries, files, model subsets, API responses– and you need to do the same operation to each one. The instinct from other languages is a for loop:

results <- vector("list", length(inputs))
for (i in seq_along(inputs)) {
  results[[i]] <- some_function(inputs[[i]])
}

It works. It is also four lines of bookkeeping (pre-allocate, index, assign, remember the loop variable) around one line of actual logic. purrr::map() is that one line:

results <- purrr::map(inputs, some_function)

map() always returns a list. Its typed siblings return a vector instead, and fail loudly if your function doesn’t produce the type you promised –a feature, not an inconvenience: silently getting a list back when you expected numbers is a classic source of bugs.

library(purrr)

1map_dbl(1:4, \(x) x^2)
2map_chr(1:4, \(x) paste("Number", x))
3map(1:3, \(x) x^2)

1

map_dbl() –always returns a double vector, errors if a result isn’t length-1 numeric.

2

map_chr() –same idea, for character output.

3

Plain map() –always returns a list, whatever the contents.

[1]  1  4  9 16
[1] "Number 1" "Number 2" "Number 3" "Number 4"
[[1]]
[1] 1

[[2]]
[1] 4

[[3]]
[1] 9

map2() and pmap() extend this to two or many parallel inputs walked together:

1map2_dbl(1:3, 4:6, \(x, y) x + y)

list(a = 1:3, b = 4:6, c = 7:9) |>
2  pmap_dbl(\(a, b, c) mean(c(a, b, c)))

1

map2_dbl() walks two vectors together, element by element.

2

pmap_dbl() generalizes this to as many parallel inputs as you like, taken from a list.

[1] 5 7 9
[1] 4 5 6

And when you want the side effect, not the return value –printing, saving a file, sending a Slack message– walk() is map()’s quiet twin: it runs your function for every element and returns its input invisibly, so nothing clutters your console.

1:3 |> walk(\(x) cat("Processing", x, "\n"))

Processing 1 
Processing 2 
Processing 3 

A real (slightly slow) example

Here’s where this stops being a toy. Say you need to fit a model per group –a common pattern: split a dataset, fit the same model shape to every piece, compare the results. We’ll pretend each fit takes real work (a tenth of a second) so the timing later in this session means something:

slow_lm <- function(formula, data) {
1  Sys.sleep(0.1)
  lm(formula, data)
}

t0 <- Sys.time()
models <- mtcars |>
2  split(mtcars$cyl) |>
3  map(\(df) slow_lm(mpg ~ wt + hp, data = df))
t1 <- Sys.time()

cat("Sequential time:", round(as.numeric(t1 - t0, units = "secs"), 2), "sec\n")
4models |> map(summary) |> map_dbl("r.squared")

1

Stand-in for “this is genuinely slow” –a database query, an API call, a heavy computation.

2

Base R’s split() breaks mtcars into one data frame per cylinder count.

3

One slow_lm() call per group, via map() –no loop, no pre-allocation.

4

map_dbl() again, this time plucking a named element ("r.squared") out of each model summary.

Sequential time: 0.37 sec
        4         6         8 
0.6807065 0.5889239 0.4970692 

Three groups, a tenth of a second each: about 0.3 seconds if nothing overlaps. Hold that number.

PART II: When parallel is actually worth it

Everything above ran sequentially –one group after another, on one CPU core. Your computer almost certainly has several cores sitting idle while that happened. The obvious next thought is “why not do all three at once?” Sometimes you should. Sometimes you shouldn’t:

NoteParallel helps when…

• Each iteration takes a real amount of time –at least a millisecond, ideally much more. Model fitting, API calls, web scraping, heavy simulation.
• The work is CPU-bound (lots of computation) or I/O-bound (waiting on a network/disk) –both benefit, for different reasons: CPU-bound work spreads across cores, I/O-bound work overlaps the waiting.
• The data passed to each worker isn’t enormous –sending gigabytes to every parallel process costs more than it saves.

Warning…and hurts when

Spinning up parallel workers has fixed overhead –starting processes, sending data and code to them, collecting results back. For quick operations (simple arithmetic, a fast filter, anything sub-millisecond), that overhead outweighs the work itself. Sequential map() will often be faster on small, fast tasks. Parallelize the slow thing, not the whole script.

This is a judgment call, not a rule you can memorize –the right response to “should I parallelize this?” is usually to time both versions and look, which is exactly what the rest of this session does.

PART III: mirai, purrr’s parallel engine

mirai is a lightweight async/parallel framework from Posit’s Charlie Gao, and as of purrr 1.1.0 it’s purrr’s own official parallel backend. daemons() launches persistent background R processes (“daemons”) that stick around waiting for work:

library(mirai)

1daemons(4)

2m <- mirai({ Sys.sleep(0.2); mean(rnorm(1e5)) })
3unresolved(m)
Sys.sleep(0.5)
4m[]

5daemons(0)

1

Launch 4 background R processes, sitting idle and ready.

2

mirai({...}) submits one expression to run on a free daemon –non-blocking: your R session gets control back immediately.

3

unresolved() checks whether it’s still running, without waiting for it.

4

m[] retrieves the result –blocks only if the task isn’t done yet.

5

daemons(0) shuts the background processes down when you’re finished.

[1] TRUE
[1] -0.002073532

For mapping many inputs at once, mirai_map() does the same thing map() does, distributed across daemons:

daemons(4)
1mirai_map(1:9, \(x) { Sys.sleep(0.2); x^2 })[.progress, .flat]
daemons(0)

1

.progress shows a live progress bar; .flat returns a flat vector instead of a list of length-1 results –both are optional subsetting flags on the result.

PART IV: The one-line bridge –purrr::in_parallel()

You don’t have to rewrite your map() calls to use mirai directly. purrr::in_parallel() wraps the function you’re already passing to map(), and purrr routes the work to whatever daemons are running:

daemons(4)

t0 <- Sys.time()
models_parallel <- mtcars |>
  split(mtcars$cyl) |>
1  map(in_parallel(\(df) slow_lm(mpg ~ wt + hp, data = df), slow_lm = slow_lm))
t1 <- Sys.time()

cat("Parallel time:", round(as.numeric(t1 - t0, units = "secs"), 2), "sec\n")
2models_parallel |> map(summary) |> map_dbl("r.squared")

daemons(0)

1

The only change from PART I’s code: slow_lm(...) wrapped in in_parallel(...), with slow_lm = slow_lm passed through explicitly (more on why in PART V).

2

Same R² values as the sequential version –parallelizing doesn’t change what you compute, only how long it takes.

Parallel time: 0.24 sec
        4         6         8 
0.6807065 0.5889239 0.4970692 

Same three groups, same tenth-of-a-second delay each, same results –but this time the three slow_lm() calls overlap instead of queueing, so the wall-clock time drops toward “however long the slowest single group takes,” not “the sum of all of them.” If you never call daemons(), in_parallel() silently falls back to running sequentially –it’s a real gotcha: your code won’t error, it just won’t be any faster, and you might not notice for a while.

PART V: The gotcha –parallel functions must be self-contained

A mirai daemon is a separate R process with no access to your R session’s workspace. Whatever in_parallel() wraps gets serialized and sent over, which means it can’t casually reach for variables sitting in your global environment:

my_data <- c(1, 2, 3)
map(1:3, in_parallel(\(x) mean(my_data)))          # ❌ errors: my_data doesn't exist over there
map(1:3, in_parallel(\(x) mean(my_data), my_data = my_data)) # ✅ passed explicitly

The same applies to packages: reference functions with pkg::fun(), or library() inside the function body, rather than relying on a package you loaded in your main session:

map(1:3, in_parallel(\(x) vctrs::vec_init(integer(), x)))     # ✅ explicit namespace

map(1:3, in_parallel(\(x) {
  library(vctrs)
  vec_init(integer(), x)
}))                                                            # ✅ also fine

ImportantWhy this is the #1 way parallel code silently misbehaves

Forgetting to pass a dependency doesn’t always error clearly –sometimes it errors confusingly, sometimes (if the daemon happens to have something similarly named loaded) it does the wrong thing quietly. When a map(in_parallel(...)) call breaks and a plain map() call with the same function doesn’t, check this first.

Already used this, one level up

The AI track’s ellmer::parallel_chat_structured() (from Talking to machines) –scoring 48 songs concurrently– is this exact idea, wrapped: ellmer manages the daemons and the request batching for you, because “call this LLM prompt many times at once” is common enough to deserve its own shortcut. Now you know what’s underneath it, and that the same mechanism is available for any R function, not just LLM calls.

TipWhere this goes next: responsive Shiny apps

mirai also ships event-driven promises and is the recommended async backend for Shiny’s ExtendedTask –meaning a slow computation (a big model fit, a batch API call) can run in the background without freezing the app for every other user. That’s a production-Shiny concern, not something we build out today, but it’s the same daemons()/mirai() machinery from PART III underneath.

🏗 Practice 9: Map first, parallelize second

The purrr half of this session runs right here in your browser – the mirai half needs real CPU cores, so it waits for your own R session in the Advanced tier.

NoteBasic

Rewrite this for-loop as a single typed map call. The loop is shown in comments – fill in the two blanks so results is a plain numeric vector.

NoteHint

You promised a numeric vector, so you want the typed sibling of map() that always returns doubles.

TipSolution

library(purrr)

results <- map_dbl(1:5, \(x) sqrt(x))
results

ImportantIntermediate

Reproduce the split-then-map pattern from PART I, but split mtcars by gear instead of cylinders, and pull out each model’s R². Two blanks: the splitting variable and the typed map that plucks "r.squared".

NoteHint

split() takes the grouping column; the session’s own example ends by plucking a named element out of each summary with a typed map.

TipSolution

library(purrr)

models <- mtcars |>
  split(mtcars$gear) |>
  map(\(df) lm(mpg ~ wt, data = df))

models |> map(summary) |> map_dbl("r.squared")

CautionAdvanced

Parallelism needs real cores – these run in your own R session:

• Take one of session 6’s scraping functions (fetching one page per artist/URL) and parallelize the fetch with daemons() + in_parallel(). Time both versions –I/O-bound tasks like network requests often parallelize better than CPU-bound ones, since the wait, not the computation, is what overlaps.
• Deliberately break the self-containment rule (reference a variable from your global environment without passing it to in_parallel()) and read the resulting error –this is the error you want to recognize instantly in the wild.
• Time the sequential PART I example again with 6 groups instead of 3 –does the gap between sequential and parallel grow?
• Read mirai’s docs on remote daemons (SSH, or an HPC scheduler like Slurm) –the purrr code you write doesn’t change at all; only the daemons() call does. What does that separation buy you?

Key takeaways

• map() and its variants replace a for-loop with a function applied to every element – same result, more composable.
• A function has to be self-contained (no reaching into the global environment) before it can be safely parallelised.
• mirai parallelises existing purrr code by wrapping it in in_parallel() – the loop logic itself doesn’t change.
• I/O-bound work (network calls, scraping) often benefits from parallelism more than CPU-bound work does.

← Previous: Smart reports

Track overview: Build with R →

Explore another track: Work with AI →