Given the unprecedented rates of data generation, processing, and storing, the computing power of modern computers have been increasingly improved thanks to advances in multi-core microprocessors and more efficient hard drives. Individuals can benefit from these advances as almost all recent personal computers come equipped with multiple processors/cores. In this workshop, I will provide a gentle introduction to parallel computing using the R statistical language and the for each package. We will discuss about the scope, utility and capabilities of parallel computing. Code will be provided to illustrate two common and easily parallelizable computational algorithms: Monte Carlo simulations, and permutation tests. Time permitting, an additional illustration of Monte Carlo simulations performing Bayesian analyses with parallel Markov chains will be provided. A working understanding of “for” loops will be beneficial but otherwise no prior knowledge of parallel computing will be assumed.