Parallel computing has established itself as another standard method for applied research and data analysis. The R system, being internally constrained to mostly singly-threaded operations, can nevertheless be used along with different parallel computing approaches. This brief review covers OpenMP and Intel TBB at the cpu- and compiler level, moves to process-parallel approaches before discussing message-passing parallelism and big data technologies for parallel processing such as Spark, Docker and Kubernetes before concluding with a focus on the future package integrating many of these approaches.