Description:

Please join the R Workshop Series as they host a session on using R to clean data, led by Ph.D. candidate Pilar Manzi.

Data cleaning: the process of identifying, correcting, or removing inaccurate raw data for downstream purposes, or, more colloquially, an unglamorous yet wholely necessary first step towards an analysis-ready dataset. Participants in the workshop will learn how to use R packages for Data Cleaning and provide an overview of how Data Cleaning helps ensure you are familiar with your data, can identify structural errors and data irregularities, and make choices about how to account for missing or incomplete data when performing statistical analysis.

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.