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%\VignetteIndexEntry{A Future for R: Apply Function to Elements in Parallel}
%\VignetteAuthor{Henrik Bengtsson}
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%\VignetteKeyword{future}
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%\VignetteKeyword{parallel}
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# A Future for R: Apply Function to Elements in Parallel

## Introduction

The purpose of this package is to provide worry-free parallel alternatives to base-R "apply" functions, e.g. `apply()`, `lapply()`, and `vapply()`.  The goal is that one should be able to replace any of these in the core with its futurized equivalent and things will just work.  For example, instead of doing:
```r
library(datasets)
library(stats)
y <- lapply(mtcars, FUN = mean, trim = 0.10)
```
one can do:
```r
library(future.apply)
plan(multisession) ## Run in parallel on local computer

library(datasets)
library(stats)
y <- future_lapply(mtcars, FUN = mean, trim = 0.10)
```

Reproducibility is part of the core design, which means that perfect, parallel random number generation (RNG) is supported regardless of the amount of chunking, type of load balancing, and future backend being used.  To enable parallel RNG, use argument `future.seed = TRUE`.


## Role

Where does the **[future.apply]** package fit in the software stack?  You can think of it as a sibling to **[foreach]**, **[furrr]**, **[BiocParallel]**, **[plyr]**, etc.  Just as **parallel** provides `parLapply()`, **foreach** provides `foreach()`, **BiocParallel** provides `bplapply()`, and **plyr** provides `llply()`, **future.apply** provides `future_lapply()`.  Below is a table summarizing this idea:

 Package
 Functions
 Backends

 future.apply (https://cran.r-project.org/package=future.apply)

 Future-versions of common goto *apply() functions available in base R (of the base and stats packages):
future_apply(), 
future_by(), 
future_eapply(), 
future_Filter(), 
future_lapply(), 
future_kernapply(),
future_Map(), 
future_mapply(), 
future_.mapply(), 
future_replicate(),
future_sapply(),
future_tapply(), and
future_vapply().

The following function is not implemented:
future_rapply()

 All future backends

 parallel

 mclapply(), mcmapply(),
clusterMap(), parApply(), parLapply(), parSapply(), ...

 Built-in and conditional on operating system

 foreach (https://cran.r-project.org/package=foreach)

 foreach(),
times()

 All future backends via doFuture (https://cran.r-project.org/package=doFuture)

 furrr (https://cran.r-project.org/package=furrr)

 future_imap(),
future_map(),
future_pmap(),
future_map2(),
...

 All future backends

 BiocParallel (https://bioconductor.org/packages/release/bioc/html/BiocParallel.html)

 Bioconductor's parallel mappers:
bpaggregate(),
bpiterate(),
bplapply(), and
bpvec()

 All future backends via doFuture (https://cran.r-project.org/package=doFuture) (because it supports foreach) or via BiocParallel.FutureParam (https://github.com/futureverse/BiocParallel.FutureParam) (direct BiocParallelParam support; prototype)

 plyr (https://cran.r-project.org/package=plyr)

 **ply(..., .parallel = TRUE) functions:
aaply(),
ddply(),
dlply(),
llply(), ...

 All future backends via doFuture (https://cran.r-project.org/package=doFuture) (because it uses foreach internally)

Note that, except for the built-in **parallel** package, none of these higher-level APIs implement their own parallel backends, but they rather enhance existing ones.  The **foreach** framework leverages backends such as **[doParallel]**, **[doMC]** and **[doFuture]**, and the **future.apply** framework leverages the **[future]** ecosystem and therefore backends such as built-in **parallel**, **[future.callr]**, and **[future.batchtools]**.

By separating `future_lapply()` and friends from the **[future]** package, it helps clarifying the purpose of the **future** package, which is to define and provide the core Future API, which higher-level parallel APIs can build on and for which any futurized parallel backends can be plugged into.

The API and identity of the **future.apply** package will be kept close to the `*apply()` functions in base R.  In other words, it will _neither_ keep growing nor be expanded with new, more powerful apply-like functions beyond those core ones in base R.  Such extended functionality should be part of a separate package.


[batchtools]: https://cran.r-project.org/package=batchtools
[BiocParallel]: https://bioconductor.org/packages/BiocParallel/
[doFuture]: https://cran.r-project.org/package=doFuture
[doMC]: https://cran.r-project.org/package=doMC
[doParallel]: https://cran.r-project.org/package=doParallel
[foreach]: https://cran.r-project.org/package=foreach
[future]: https://cran.r-project.org/package=future
[future.apply]: https://cran.r-project.org/package=future.apply
[future.batchtools]: https://cran.r-project.org/package=future.batchtools
[future.callr]: https://cran.r-project.org/package=future.callr
[furrr]: https://cran.r-project.org/package=furrr
[plyr]: https://cran.r-project.org/package=plyr
