Would like to use RStudio Cloud for a 1-day workshop?

[dtwrub]

Hi there, apologies if this is not the correct place to ask this, but I could find no other contact information about RStudio Cloud.

I am co-organizing a one-day (approximately 4-hour) introductory R workshop for chemists, to be held at the fall 2019 ACS meeting in San Diego. We are expecting approximately 20 people plus 3 instructors. Is there someone I can contact about logistical information to set this up? I know that there is a limit to sharing individual RStudio Cloud projects - ideally we would like a shared environment for all 23 of our participants. We are in the process of designing the curriculum now.

If someone could contact me to discuss this further I would greatly appreciate it.

Much thanks,
Donna

[Robby]

Hi Donna,

Thanks for your interest in using Cloud for your workshop. All you should need to do is create a space for the workshop (if you haven't already done so), then go to the Members page of the space and press the Request More Capacity button. Fill out and submit the form that appears with your info, and we'll get you set up with the capacity you need.

Also, if you haven't already, you might want to take a look at the Cloud Guide (https://rstudio.cloud/learn/guide), particularly the section titled "Using Private Spaces in Courses and Workshops" - you'll find some info there that should be helpful.

Thanks,
Robby

[mishabalyasin]

What if I need/want to use package versions (e.g., tidyr, dplyr etc.) from GitHub in a workshop? If I understand correctly, dplyr installation will fail since it needs more than 1 Gb of RAM. Is there any way to get it inside of a project with this limitation in mind?

[Robby]

Right - because we currently limit RAM for our projects to 1GB, compiling some packages will fail. However, we do make an effort to compile, cache and serve up packages from CRAN in a binary format so that you don't need to compile them. When we encounter a (CRAN package & version & R version) we do not have cached, we serve the source package and queue an attempt to compile it.
If the compilation is successful, then the next time it is requested, we will serve a binary package.

If it's essential you pull a version directly from GitHub, then our caching mechanism won't help you, but if you can use the released versions of packages (e.g. tidyr, dplyr etc.), you should be all set.