Programming with Data in R (PSYC 5170-003)
The purpose of this course is to help graduate students acquire programming skills that make organizing, transforming, visualizing, and presenting data more efficient and reproducible as well as support data analysis workflows and model building. This is a lab based course and lectures are minimal. Class time will be spent practicing the art of programming with data under the instructor’s guidance. Students are encouraged to interject their own data and projects into the course. We will be using R and RStudio but the programming concepts and practices translate to other languages and environments like Python. Students are expected to have a basic familiarity with R and have completed at least the introductory statistics courses. Ideally students will have already had some experience in analyzing their own data. Students will be evaluated on their performance in the weekly lab activities. The labs are designed to be completed within the class time. Students are encouraged to integrate what they learn in the class into their data analysis activities outside of class. Topics include Basic R Commands and Concepts , Data Objects, Subsetting, Control Flow, Functions, Environments, Conditions, Scripts, Functional Programming in R, Debugging, Performance, Interfacing with other languages, Visualization, and formatting articles and presentations. Topics and depth of coverage will be adjusted to student need and interest.
Who is this course for?
Anyone who has experience with R and data analysis and who wants to learn more about how to use a programming language (like R) to organize, analyze, model, share and communicate findings.
What will you get out of this course?
Students will acquire a conceptual understanding of how R works and an appreciate for Object Oriented and Functional programming styles. In addition, students will learn how to write code that is more general, efficient and that better serves the demands of reproducible science. Students will learn about basic programming principles that make code more reliable and useful. These principles can be transferred to other languages like Python.
What this course is not.
This is not a course for students interested in just learning a specific statistical analysis or a particular package in R. Students will be encouraged to bring their own data and varied analysis needs to class in order to apply general skills acquired in the course to their own research needs and settings.
What is the course format?
Currently the course is offered as online. Each week there will be a 3 hour lab assignment. A portion of each lab will be an interactive online group meeting. The group portion will be scheduled based on student and instructor availability. All data, R documents and assignments will be available online through a course Github.
If you would like to know more about the course, please feel free to email me (adam.sheya@uconn.edu)
For more information, contact: Adam Sheya at adam.sheya@uconn.edu