**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.

**Dates**: **10/9** **–** Part 1: Introductory Workshop in Statistical Computing with R

** 11/13 – **Part 2: Intermediate Workshop in Statistical Computing with R

**Time**: Wednesdays, 7pm – 9pm

**Location**: New Media Center (NMC), 1st Floor, Lewis Science Library

**To Register** for the workshop series, please fill out the registration form here. Or access the form via the QR code to the right. Limited space available.

**Description**:

*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 data. Using the command line interface, you will learn how to load and save data, subset and modify data, obtain summary statistics, and about data structures and classes. Correlation and t-test will be used to demonstrate how to use built-in functions to carry out a statistical analysis.

*Part 2: Intermediate Workshop in Statistical Computing with R*

In the second session, you will learn for-loops, and conditional statements, and basic visualization. From scatter plots to histograms, visualizing data is a crucial step in analyzing data. And, for-loops and conditional statements enable you to automate time consuming statistical tasks. You will work with a real data set to practice these crucial functions, and in the process, you will learn how to organize your statistical analysis (i.e., a script).

**PLEASE NOTE**: **The best way to learn R is to attend both sessions**. Particularly for the second session, learning will be more effective if each student has taken the introductory session. 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 are only able to attend Part 2, you should already have a basic understanding of R. Specifically, you should be able to understand and complete the directives contained in Handouts 1 & 2 of the Q-APS Politics Statistical Programming Camp, available here.

**Instructor Bio**: Neo Chung, is a 5th year graduate student in Quantitative and Computational Biology. Motivated by large-scale genomic studies, he develops statistical learning methods in R for application to a wide range of biomedical and genomic data. Previously, Neo has worked as an Assistant-in-Instruction for an introductory statistics course, and led workshops in statistical programming at the McGraw Center and J Street Library & Media Center.