> library (snowfall)
# initialize cluster
> sfInit (parallel=TRUE , cpus=4)
# parallel computing
> result <- sfLapply(1:10, log)
# stop cluster
> sfStop ()
Simply replace "1:10" and "log" with your parameter and function to make you own parallel computing.
Function sfLapply() is a parallelized version of lapply(). Some other fuctions are sfSapply, sfApply, sfRapply and sfCapply.
More examples on data mining with R can be found in my book "R and Data Mining: Examples and Case Studies", which is downloadable as a .PDF file at the link.
With a Mac, parallel computing can be achieved with package multicore. Unfortunately, it does not work under Windows.
A simple way for parallel computing under Windows (and also Mac) is using package snowfall, which can work with multi-CPU or multi-core on a single machine, as well as a cluster of multiple machines.
For parallel computing on a single machine, it is simple and easy as below.