Looking for an extremely worthwhile elective next spring? If you've taken a biometry class (GEOG306 or BIOM301, BMGT230, EDMS451, STAT400, etc) and want to excel at using Program R...definitely check out this class below! We'll be happy to have it count as one of your electives!
Data Wrangling and Visualization with R (PLSC498B).
The R Statistical Environment has fundamentally changed how scientists manage, analyze, share, and communicate data. The power and flexibility of R open myriad opportunities. The downside of this power is that it creates a daunting barrier to entering the R universe. Development of a constellation of tools called The Tidyverse is lowering those barriers, but its unique language and philosophy can still be daunting. The purpose of this course is to immerse you in the Tidyverse so that you can use its tools to harness the power of R in ways that will help you transform and elevate your research.
Through a combination of lecture, hands-on demos, quizzes, and increasingly independent coding exercises, you will learn the fundamentals of using R. You will gain experience with approaches for handling, summarizing, and plotting data in ways that are repeatable and transparent. This is NOT, however, a statistics class. Rather, you will learn to get your data into R, wrangle it into the form needed for your chosen analysis, get your results out, and document your workflow for yourself and the greater scientific community. Rather than focusing on a few specific types of analyses, you will learn basic skills and general principles that you can use to analyze data for your field. When you take your statistics courses you will be able to focus on the statistical concepts because you will be fluent in the reproducible vocabulary and grammar of R focusing on methods from the Tidyverse.
- The course is designed for advanced undergraduate students and graduate students who are meeting R for the first time.
- Students who are just designing their research can benefit by understanding how to collect and manage their data from the outset.
- Students who already have data will jumpstart their ability to process and analyze their data.
- Slightly more advanced students with some R experience can bolster their skills.
- Truly advanced R users seeking in-depth instruction on specific types of analyses or high-level programming techniques will likely not benefit much. Such students will find a better fit in one of the many other great courses in statistics or advanced programming.