1.1 Transitioning to R
Moving to R from other statistical software generally requires a fundamental shift in the way we think about and interact with data. Aside from this shift in thinking, there is also a substantial amount of code to learn, which can be both frustrating and intimidating. The primary goal for this chapter is to make this shift less intimidating and the learning curve less steep.
We will focus primarily on three components: data processing (munging/manipulating/wrangling), data visualization, and reproducible workflows. We will be oriented around the philosophy of tidy data and, as such, primarily rely on tools within the tidyverse for manipulating and visualizing data. The tidyverse is a suite of packages developed by RStudio, generally led by Hadley Wickham, which are all optimized for tidy data. The focus of this course is on working with R, as opposed to the specifics of any given analysis. Statistical models will be used for illustrative examples throughout the chapter, but high-level statistical knowledge (e.g., multilevel modeling or structural equation modeling) is not a prerequisite.