Course Detail
Units:
3.0
Course Components:
Lecture
Description
This course develops students’ ability to write R scripts which address complex questions that cannot be otherwise addressed with existing R packages. The nature of these scripts are often intensive and require sophistication to debug and increase the efficiency of run time. Topics covered include: functions and vectorized programming, debugging and code optimization, parallel computing, C/C++ to R coding, the linux environment, and running jobs on the university’s high performance computing nodes. Students will learn through seeing (lectures), doing (homeworks), and teaching (helping one another improve coding). This course is aimed for students with at least moderate proficiency in R programming and/or strong proficiency in another programming language such as Python and C/C++. Students must be able to write their own functions and submit homework solutions in R markdown or latex. Prior to the course, students may ask the professor for an example problem to assess their comfort with R programming.