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Both R Studio Server and JupyterHub web environments have a large number of packages available. Instructors can also request that additional software be installed. Such requests should ideally be made at least two weeks before the start of a course.
Note that long-running R and Python processes should generally be run from the command line rather than in the web applications, as duration of work there is limited by session timeouts (e.g. 120 minutes currently for R Studio Server).
See About R and R Studio Server for information about the R Studio Server web application and the command-line R environments, and how to troubleshoot common problems.
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- Post assignments to Canvas. Have students download to their personal computers, then Upload to their Home directory in RStudio Server or JupyterHub.
When the assignment is complete, students can copy the files from their Home directory back to their personal computer using a remote file system transfer tool such as scp. This transfer must be initiated from the user's computer, as shown here. Then the completed assignment can be uploaded to Canvas from there.
- Note: There is a 64 Megabyte file-size limit on uploading files in both JupyterHub and RStudio Server, so an alternate method should be used for files larger than 64M.
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# From the student's personal computer cd my_homework_directory scp amb599@educcomp01.ccbb.utexas.edu:~/homework1.amb599.R . |
Instructors can post assignments to their shared /stor/work/<Class_Semester> directory as describe in the Posting assignments to the shared course directory section.
After posting an assignment, instructors can have students copy it to their Home directory using a Terminal pane in RStudio Server, or JupyterHub, renaming it in the process. When finished, use a Terminal pane to copy the assignment to the shared /stor/work/<Course_Semester> directory.
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