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This is the first version submitted to CRAN.
Add supported_packages() and
supported_package_functions().
Rename argument chunk.size to
chunk_size.
Add custom print() method for transpiled calls such
that attributes are displayed for arguments and their content.
Transpiler can now handle nested, complex wrapped expressions.
Error messages now suggest using %do% when trying to
futurize foreach() with %dopar% or
%dofuture%.
Error messages now distinguish between infix operators
(e.g. %do%) and functions
(e.g. lapply()).
Add support for mgcv,
e.g. b <- bam(...) |> futurize().
{ ... }, ( ... ), local( ... ),
I(), and identity(), e.g.
local({ lapply(x, f) }) |> futurize() is the same as
local({ lapply(x, f) |> futurize() }).Handle nested transpilers.
Add futurize(when = {condition}) for futurizing
conditioned on an R expression at runtime,
e.g. lapply(xs, fun) |> futurize(when = (length(xs) > 10)).
Add futurize(FALSE) and futurize(TRUE)
for disabling and enabling futurizing of calls.
Add support for tm,
e.g. m <- tm_map(crude, content_transformer(tolower)) |> futurize().
The default future options for futurize() are now
customized such that they work in more cases, e.g. there is no need to
declare seed = TRUE for
replicate(3, rnorm(1)) |> futurize().
futurize() gained argument eval, which
can be used to return the futurized expression instead of evaluating
it.
Add support for caret,
e.g. model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize().
Add support for times() and %:% of
foreach, which require special care when it comes to
passing future options,
e.g. futurize(seed = FALSE).
The futurize package unifies our current
future.apply, furrr, and
doFuture solutions into a minimal, unified API. This
means you no longer need to learn those future-specific packages and
their APIs, and all you need to know is the
... |> futurize() syntax. The default behavior of
futurize() is sufficient for most use cases and users, but,
if needed, it comes with one unifying, unique set of arguments that can
be used to configure how the futures are resolved, how they are
partitioned into chunks, and how output and conditions are relayed,
among other things.
Add support for base R,
e.g. y <- lapply(xs, fcn) |> futurize(),
y <- by(xs, idxs, fcn) |> futurize(), and
xs <- kernapply(x, k) |> futurize().
Add support for purrr,
e.g. y <- map(xs, fcn) |> futurize().
Add support for crossmap,
e.g. y <- xmap_dbl(xs, fcn) |> futurize().
Add support for foreach,
e.g. y <- foreach(x = xs) %do% { fcn(x) } |> futurize().
Add support for plyr,
e.g. y <- llply(xs, fcn) |> futurize().
Add support for BiocParallel,
e.g. y <- bplapply(xs, fcn) |> futurize().
Add support for boot, e.g. `b <- boot(data, statistic, R =
Add support for glmnet,
e.g. cv <- cv.glmnet(x, y) |> futurize().
Add support for lme4,
e.g. gm <- allFit(gm) |> futurize().
futurize()
function that takes a call expression to any base-R apply function and
transpiles it such that it runs in parallel via futures. This works by
transpiling the original map-reduce call to evaluate each iteration via
a lazy, vanilla future. These futures are then partitioned into chunks,
where the number of chunks defaults to the number of parallel workers.
The futures in each chunk are merged into a single future. These futures
are then launched in parallel on the current future backend. When
resolved, the results are reduced back to the structure that the
original base R apply function would return.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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