Science in R: Utilizing R for Scientific Research

R is a statistical computing environment. Mastering the R language means becoming proficient in state-of-the art software used to organize, understand, and explain data. R is a freely available open-source program, and is increasingly used in academia as well as industry: Indeed, mid through 2015, R stands as the world’s 6th most used programming language (not just statistics!) [ see: IEEEspectrum.org ].

R is powerful, flexible, and rapidly advancing. This progress results in large parts from the activity of a dynamic and active community of developers, statisticians, and scientists who work with data. As a byproduct, social science PhD students who become proficient in R will not only find their elementary scientific computing needs met within a single programming language, but will simultaneously benefit from generously available online support regarding many questions that arise while learning to code and to generally “work with data”.

The goal for this seminar is to equip graduate students with a bird’s-eye view of the global R environment. This means that students will learn the larger landscape of tools that exist in R and be introduced to how these tools can be utilized to efficiently streamline data-aspects of scientific research.

No prior knowledge of R is required. Several homework sets will be provided from which students can develop familiarity with basic R syntax, and a brief introduction to R and RStudio will be covered at the beginning of the course. The course is not a statistics course, and lessons will emphasize the development of a general overview regarding powerful data handling tools available in R and how to utilize these in research.

Of central importance to the course will be the treatment of relatively new R packages for handling data (e.g. dplyr & magrittr), packages to create nifty graphics (ggplot2 & ggvis), as well as various tools that assist in proper documentation and convenient presentation of analysis (e.g. tidyr, knitr, & shiny).

The course is conceived as a graduate seminar for credit. Students from the Social Sciences, Psychology, and Economics are primarily addressed, moreover, intended participants should be actively engaged in ongoing research. Students will be given the opportunity to present on particular features of R, relevant to their field.

Schedule