Upon successful completion of the course, students will be conversant with the R programming language, and familiar with several statistical packages. Specifically, students will be able to manage different types of variables and data structures, perform the analytic techniques described in the course Multivariate Analysis, graphically display descriptive and inferential results, and interpret various output. Students who successfully complete the course will be able to write R scripts to appropriately test specific research questions and model assumptions; as well as interpret and present these results.

This course introduces R as a statistical platform for the analysis and graphical representation of multivariate statistical techniques. The flexibility of this program offers students a general statistical “solution” to analyzing simple and complex data structures, in that, the packages available in this program allow for a wealth of analytic functionality. The course begins with an overview of the R language. Data management and issues associated with pre-processing of data are initially described. Specific attention is paid to the visualization of data, both in terms of descriptive and inferential results. Lectures then expand upon the concepts and analytic techniques described in Multivariate Statistics (Stats I) and require students to translate this knowledge into the R “statistical” language. This course is meant to introduce students to a statistical program that may be used to perform a wide range of statistical analyses. R will be featured in the Mixed-effects Modeling and Structural Equation Modeling statistical courses, and is compatible with the Python program, which is described in the programming skills course: Python. R is free, open-source, platform independent, and continuously maintained, ensuring that students will be able to use their newly mastered skills in any future context.
Teaching methods: Lectures and computer-laboratory.