Please join the R Workshop Series as they host a session on using R to clean data, led by Ph.D. candidate Max Weylandt.
Experiments let us solve the fundamental problem of causal inference -- showing that a factor causes an outcome. R can be helpful both in setting up surveys and in analyzing their results, and this workshop provides a sneak peek at its capabilities. Participants will learn how they can use R to plan experiments and even write questions that can be imported easily into Qualtrics. Then, using existing data, we will make a (presentable) analysis of covariate balance and briefly look at the results of the experiment.
This workshop serves to introduce the R programming language and promote best practices in its use. For many years, R has been favored by a passionate user-base of social scientists and statisticians. These users have contributed many packages to simplify the task of analyzing data and reporting results. Today, R stands apart in the statistical software ecosystem because of the extensiveness and quality of these open-source libraries. In previous eras, finding such high-quality software would require using expensive, proprietary systems such as STATA or SAS. While non-academic researchers often favor other languages (notably Python), R is still regarded as very useful outside academia. While each programming language has its wrinkles, familiarity with R will make learning other languages favored by data scientists considerably easier.