R Bootcamp (2017 EPI/FACETS Summer Program)

Over the the two 3-hour sessions, we will examine the following topics.

• 01 R and RStudio: getting around
• 02 tidyverse: preparing tidy data
• 03 ggplot2: grammar of graphics
• 04 statistical tools
• 05 regression modeling
• 06 tbd

There are several “languages” that you encounter in data analysis. We will focus on R.

• R
• SAS
• Stata
• Python
• Julia

There are point-and-click analysis software, why do we bother to code?

• Reproducible research
• Coding keeps track of what you did better than point-and-click
• Beyond occasional casual use, programming provides more opportunities for workflow efficiency improvement.

• R language is the language.
• R/R.app/R.exe is the “interpreter” that interprets what you say in R to your machine.
• RStudio is the “integrated development environment (IDE)”.
• RStudio provides a nice coding environment and makes interaction with R interpreter easier.
• RStudio can also edit various static and dynamic documents, e.g., presentation (this!), RMarkdown, LaTeX, and web app (shiny).

The following sessions are largely based on Wickahm & Grolemund's R for Data Science, which is available as a free website or a physical book. This book covers the current state of “tidyverse” very nicely.

Some additional notable resources are:

• cheat sheet
• tidyverse
• R-bloggers
• R Graphics Cookbook
• Advanced R
• R packages