An alternative approach is to use a virtualized hardware in the cloud (e.g. EC2, Azure, etc.). More modest configurations can be more than adequate (e.g., a mid-to-high end desktop) for the vast majority of use cases, however care should be given when working with larger datasets in a shared environment. Students taking courses who are not affiliated with the department are issued temporary visitor accounts which expire at the end of the semester. This works well for upper division and graduate level courses as most students are directly affiliated with the department. The figure below is a sketch of the architecture of the centralized RStudio server approach, which shows that students connect to a single RStudio server instance via a departmental login. If this server is in the department, instructors can be given direct control over all aspects of the computing environment. One approach that might work particularly well for higher level courses that require a shared infrastructure and higher end computational resources is running academically licensed RStudio Server Pro on a powerful server (e.g. 32 cores, 512 GB RAM). If you prefer to not use RStudio Cloud, you might consider one of the following approaches. The goal is that all students will be able to continue using R even if they no longer have access to departmental resources. Teaching personal setup is best done progressively throughout a semester, usually via one-on-one interactions during office hours or after class. It should be noted that the goal isn’t to completely dissuade students from downloading and installing R and RStudio locally, we just don’t want it to be a prerequisite for getting started. In a course at this level students should access the RStudio IDE through a centralized RStudio server instance, which allows us to provide students with uniform computing environments. In this section we describe alternative setups for your course.
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