Very simple parallelized implementation of dplyr::do() using mclapply.

mcdo.R

library(parallel)
library(pbmcapply)
library(dplyr)

mcdo <- function(.data, ...) {
	if (progress.bar) {
		pkg.require(c('pbmcapply'))
		pfun <- pbmclapply
	} else pfun <- mclapply

	indices <- if ('grouped_df' %in% class(.data)) attr(.data, 'indices') else 1:nrow(.data)

	pfun(indices, function(i) {
		do(.data[i + 1,], ...)
	}) %>% bind_rows()
}