multidplyr

multidplyr is a dplyr backend that partitions data frames across multiple R processes to enable parallel computation on multi-core systems. You split your data with partition(), perform dplyr operations in parallel, and retrieve results with collect().

This package is most valuable for parallelizing complex, slow functions on datasets with 10+ million rows, where the computation cost outweighs the overhead of distributing data across cores. It works best when you can partition data by meaningful groups or read different files directly on each worker. For simpler operations on smaller datasets, the communication overhead makes alternatives like dtplyr more efficient.