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For this lab, we’ll be taking a look at some aspects of programming in R that can get glossed over when people start learning R in the context of a specific project. We’ll be talking about different programming environments for R and how they differ from R itself, the fundamental data structures of R and why it’s so important to have a solid understanding of these, and finally, how programming in R actually fits in with your own goals as well as your organization’s goals.

As a part of this exploration, we’ll work through a series of exercises intended to highlight the basic components and techniques involved in R programming. You can access the exercises here.




Post Author: Jen Schellinck

Jen Schellinck is the principal of Sysabee and an adjunct professor at Carleton's Institute of Cognitive Science. She founded Sysabee in 2012 with the goal of taking analysis techniques from machine learning and systems modeling and making these available to organizations who are seeking to gain the benefits of technology supported analysis and decision making. For each project, she draws from a pool of expert consultants to create a team customized to the specific needs of the project. She is also the founding member of the Data Science Experts Group, an association of data professionals that build flexible, customized solutions for data-driven companies and organizations. She remains an active participant in academic research via Carleton’s Cognitive Modeling Lab.