Read this guidance before you post

Applying R for public health

Please read this before you post:

  1. Questions in this category should address how R can be applied to solve analytical challenges in applied epidemiology and public health. For example:
  • How to best visualize/communicate a public health finding using {ggplot2}
  • Why your data-cleaning pipeline (%>%) isn’t producing the expected results
  • How to apply a generic R package to perform a public health-specific task?
  • Get help editing an R Markdown reports
  1. Do your research first
  1. Summarize the steps you already took in a reproducible way
  1. "Tag" your question with relevant terms so that others can easily find it (e.g. R Markdown, Shiny, etc.)

Please read this before replying

  1. This is meant to be a space welcoming to beginners and forgiving of mistakes
  2. Multiple responses to one topic are welcome
  3. We suggest that you familiarize yourself with Applied Epi R training resources such as the Epi R Handbook, which to aid beginners emphasizes the following:
  • {tidyverse} for general data handling
  • {rio}, {here}, and RStudio projects for general data import and directory handling
  • {pacman} p_load() for package install & load
  • {janitor} tabyl() for quick summary tables, or {dplyr} or {gtsummary}
  • Use of the <- assignment operator, not =
  • Writing full argument names for clarity, such as mapping = aes() in ggplot() and ifelse(test = , yes = , no = )