Modern epidemiology gives us insight into study planning and causal inference, but the success of these approaches require friendly and accessible software. I will discuss two R packages for modern methods in study design and causal inference: precisely and ggdag. precisely is a study planning tool to calculate sample size based on precision rather than power. Calculating sample size based on precision focuses on the width of the confidence interval instead of statistical significance. precisely is a fast and flexible R implementation of the work by Rothman and Greenland on this subject, including a Shiny web app for calculating sample size. ggdag is a toolkit for working with causal directed acyclic graphs (DAGs), a central tool in causal inference. DAGs help identify many types of bias, like confounding, selection bias, and measurement error, as well as tell us how to correct for it. ggdag makes it easy to create, analyze, and plot DAGs in ggplot2.