foreach 1.5.0 now available on CRAN

This post is to announce that version 1.5.0 of the foreach package is now on CRAN. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. The foreach package is now more than 10 years old, and is used by nearly 700 packages across CRAN and Bioconductor. (If you're interested in an overview of the foreach package and its history, this RStudio::conf talk by Bryan Lewis is worth watching.)

The main change is 1.5.0 is intended to help reduce errors associated with modifying global variables. Now, %dopar% loops with a sequential backend will evaluate the loop body inside a local environment. Why make that change? Let's illustrate with an example:

library(foreach)

registerDoSEQ()



a <- 0

foreach(i=1:10) %dopar% {a <- a + 1}

a

## [1] 0

Here, the assignment inside the %dopar% is to a local copy of a, so the global variable a remains unchanged. The reason for this change is because %dopar% is intended for use in a parallel context, where modifying the global environment doesn’t make sense: the work will be taking place in different R processes, sometimes on different physical machines, possibly in the cloud. In this context there is no shared global environment to manipulate, unlike the case of a sequential backend.

Because of this, it’s almost always a mistake to modify global variables from %dopar%, even if it used to succeed. This change will hopefully reduce the incidence of programming errors where people prototype their code with a sequential backend, only to have it fail when they use it with a real (parallel) backend.

Note that the behaviour of the %do% operator, which is intended for a sequential backend, remains the same. It matches that of a regular for loop:

a <- 0

foreach(i=1:10) %do% {a <- a + 1}

a

## [1] 10

If you have any questions or comments, please email me or open an issue at the GitHub repo.