Statistical Computing in R
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 two-part series 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 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.
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).
Instructor Bio (on 11/13/13): 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.
Instructor Bio (on 10/21/13 and 11/12/13): David Robinson is a third-year PhD student in the Storey Lab within the Quantitative and Computational Biology program. His interests include bioinformatics, especially genomics, statistics and programming. David has substantial experience teaching R at both Princeton and Harvard University.