R is the de facto standard for statistical analysis in a wide range of disciplines such as computational biology, finance, sociology, political science and digital humanities. This two-part workshop will help participants to get started with R’s abilities, ranging from data structure to visualization. Designed for students without any programming experience, this course will better prepare you for introductory statistics courses and quantitative research at Princeton.
Part 1: Introductory Workshop in Statistical Computing with R
In the first session, you will become familiar with the R programming environment and learn how to work with variables, vectors and data frames. You’ll learn how to import data from a file, to filter it, and to extract summary statistics. You’ll then learn how to use the powerful ggplot2 package to visualize your data, including scatter plots, histograms and boxplots.
Part 2: Intermediate Workshop in Statistical Computing with R
In the second session, you’ll be introduced to R’s tools for statistics and exploratory data analysis. You’ll learn to use R’s built-in statistical functions to test hypotheses about your data, including computing correlations, comparing two samples, and performing linear regressions. You’ll then learn further methods of manipulating and summarizing data using the dplyr package, and learn the basics of exploratory data analyses.
PLEASE NOTE: The best way to learn R is to attend both sessions. The second session will assume students are familiar with both R data structures and the ggplot2 package. To meet the goals of each session, and out of respect for those who enrolled in both, the Instructor will not be able to review material for students not present for Part 1. If you absolutely must miss the first session, reviewing the material in Lessons 1 and 2 of the online course, and passing the corresponding interactive quizzes, would help acquire the necessary basis for Part 2.