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View Code? Open in Web Editor NEWUse R, Rstudio, Shiny, Radiant, Python, and Jupyter in a Docker container
Use R, Rstudio, Shiny, Radiant, Python, and Jupyter in a Docker container
Hi!
I've been reading the repo documentation and source code, but I couldn't find a way to use the radiant docker container in a way that I can select the state.rda
file I want to be preloaded into Radiant. This data file could be shared via shared volumes with the container.
Also, I'd like to be able to run radiant only with the data
mode, would that be possible? (radiant.data("/mydata.state.rda")
).
Thank you for your attention!
Hi,
I have recently re-installed the rsm setup after a year (the last version was during April 2021). I have changed the user name compared to the initial installation (2019) of rsm setup. Now, I couldn't locate the git folder just like I always had access with my old user name under the C: drive. By reading this issue, I see that the user remains jovyan for everyone in the environment.
There is no problem in using jupyter or R but if I want to upload data from an older git folder or even new data or files, I prefer to directly move it in the windows explorer rather than adding them to the git folder by drag and dropping on to the git folder shown on the below screen:
Hi,
I was using your image and it worked excellent, but now we have integrated z2jh with NFS mounts and so on, so we need to add custom /etc/passwd and /etc/groups to the image. So rebuilding your image like this, trows the Unable to connect error after 10s, roughly.
17:31 # cat rsm-jupyterhub/Dockerfile
FROM vnijs/rsm-jupyterhub:latest
ADD ./passwd /etc/passwd
ADD ./group /etc/group
USER $NB_UID
Do you have any idea about what I could be doing wrong?
Cheers.
Hi,
in case one needs to serve the GUI over https, what would then be way to configure ssl? In addition, how can one overwrite the standard behaviour (data sources, reduce the number of menu), keeping the dockerized structure?
I tried to install a package locally.
By running, pip3 list
, I can see that the latest version is installed. But when I do:
import package
print(package.__version__)
I see an older version.
Any insights on where the packages are installed within the docker environment (when I use pip3 install --user package)
.
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