Official Description: R is powerful, flexible, open-source software for manipulating and analyzing data. It is constantly being improved and expanded by a diverse group of people. Many researchers claim that data analysis with R is the way of the future because of its accessibility (it is free), versatility (advanced tools for almost any field, from finance to GIS and genomics), flexibility (the question is not if you can do it, but how), and reproducibility of analysis (through short scripts). This course will help you to become comfortable with basic data manipulation in R, beginning with installing and setting up the program, learning the basic idiom, and continuing on to manipulating and organizing your data, creating good graphics, and learning how to find more help.

Learning Objectives:

  1. Be able to import, clean, and tidy data in R
  2. Be able to calculate descriptive statistics and create illustrative plots from that data 3 . Develop/discover a personalized and effective workflow, including file storage, file naming, and script organization
  3. Become comfortable finding and using R-help resources

Pre-requisites: None

Instructors: Anna Steel taught this course in Winter 2013. A version was taught by Deb Niemeier in Fall 2013. Ryan Peek & Mark Lubell taught an ECL290 version in Winter 2017.

Other info and comments: 2013 syllabus here (*.docx). Plans for offering this course in future years are still in flux.

Software used: R

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