Bayesian methods have become commonplace in the social sciences. Bayesian models allow for flexibility in model specification, the injection of prior information, and a viable estimation strategy when dealing with populations. However, researchers are often deterred from utilizing these methods due to the difficulty of running them in R. This workshop will cover the basics of Bayesian models in R by offering (i) a general intuition regarding how sampling techniques work in estimating a Bayesian model, (ii) code for implementing Bayesian models using JAGS (with an emphasis on setting up hierarchical models), and (iii) a variety of techniques to visualize posterior estimates and convergence diagnostics. Attendees will leave the workshop with an intuition of MCMC, example code for implementation, and strategies for working with posterior estimates.