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futurize: Parallelize Common Functions via One Magic Function The 'futurize' hexlogo

TL;DR

The futurize package makes it extremely simple to parallelize your existing map-reduce calls, but also a growing set of domain-specific calls. All you need to know is that there is a single function called futurize() that will take care of everything, e.g.

y <- lapply(x, fcn) |> futurize()
y <- map(x, fcn) |> futurize()
b <- boot(city, ratio, R = 999) |> futurize()

The futurize() function parallelizes via futureverse, meaning your code can take advantage of any supported future backends, whether it be parallelization on your local computer, across multiple computers, in the cloud, or on a high-performance compute (HPC) cluster. The futurize package has only one hard dependency - the future package. All other dependencies are optional “buy-in” dependencies as shown in the below tables.

Supported calls

Supported map-reduce packages

The futurize package supports transpilation of functions from multiple packages. The tables below summarize the supported map-reduce and domain-specific functions, respectively. To programmatically see which packages are currently supported, use:

To see which functions are supported for a specific package, use:

Package Functions Requires
base lapply(), sapply(), tapply(), vapply(), mapply(), .mapply(), Map(), eapply(), apply(), by(), replicate(), Filter() future.apply
stats kernapply() future.apply
purrr map() and variants, map2() and variants, pmap() and variants, imap() and variants, modify(), modify_if(), modify_at(), map_if(), map_at(), invoke_map() furrr
crossmap xmap() and variants, xwalk(), map_vec(), map2_vec(), pmap_vec(), imap_vec() (itself)
foreach %do%, e.g. foreach() %do% { }, times() %do% { } doFuture
plyr aaply() and variants, ddply() and variants, llply() and variants, mlply() and variants doFuture
BiocParallel bplapply(), bpmapply(), bpvec(), bpiterate(), bpaggregate() doFuture

Table: Map-reduce functions currently supported by futurize() for parallel transpilation.

Here are some examples:

library(futurize)
plan(multisession)

xs <- 1:10
ys <- lapply(xs, sqrt) |> futurize()

xs <- 1:10
ys <- purrr::map(xs, sqrt) |> futurize()

xs <- 1:10
ys <- crossmap::xmap_dbl(xs, ~ .y * .x) |> futurize()

library(foreach)
xs <- 1:10
ys <- foreach(x = xs) %do% { sqrt(x) } |> futurize()

xs <- 1:10
ys <- plyr::llply(xs, sqrt) |> futurize()

xs <- 1:10
ys <- BiocParallel::bplapply(xs, sqrt) |> futurize()

and

ys <- replicate(3, rnorm(1)) |> futurize()

y <- by(warpbreaks, warpbreaks[,"tension"],
        function(x) lm(breaks ~ wool, data = x)) |> futurize()

xs <- EuStockMarkets[, 1:2]
k <- kernel("daniell", 50)
xs_smooth <- stats::kernapply(xs, k = k) |> futurize()

Supported domain-specific packages

You can also futurize calls from a growing set of domain-specific packages (e.g. boot, caret, glmnet, lme4, mgcv, and tm) that have optional built-in support for parallelization.

Package Functions Requires
boot boot(), censboot(), tsboot() future
caret bag(), gafs(), nearZeroVar(), rfe(), safs(), sbf(), train() doFuture
glmnet cv.glmnet() doFuture
lme4 allFit(), bootMer() future
mgcv bam(), predict.bam() future
tm TermDocumentMatrix(), tm_index(), tm_map() future

Table: Domain-specific functions currently supported by futurize() for parallel transpilation.

Here are some examples:

ctrl <- caret::trainControl(method = "cv", number = 10)
model <- caret::train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()

ratio <- function(d, w) sum(d$x * w)/sum(d$u * w)
b <- boot::boot(boot::city, ratio, R = 999) |> futurize()

cv <- glmnet::cv.glmnet(x, y) |> futurize()

m <- lme4::allFit(models) |> futurize()

b <- mgcv::bam(y ~ s(x0, bs = bs) + s(x1, bs = bs), data = dat) |> futurize()

m <- tm::tm_map(crude, content_transformer(tolower)) |> futurize()