![]() We’ll cover scatterplots, boxplots, histograms, and their variants. Here, we just provide an introduction, specifically an introduction to ggplot (subsequent courses in this serious will cover visualization in depth). Data visualization is another big and important topics. Here, we will primarily focus on filtering, slicing, selecting, renaming, and mutating data frames. Here, we just provide an introduction (subsequent courses in this series will cover wrangling in depth). Data wrangling, which is the art of cleaning and restructuring data is a big topic. Any materials, such as slides, data sets, etc., will be shared via GitHub. Solutions to these exercises and brief discussions of them will take place after each break.Īlthough not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.Īll the sessions will be video recorded, and made available immediately on a private video hosting website. For the breaks between sessions, and between days, optional exercises will be provided. ![]() Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. Then, we will cover how to perform the various statistical analyses using R. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. ![]() This course will be largely practical, hands-on, and workshop based. ![]()
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