Migrating away from RStudio
R workspaces will be migrated from RStudio to DataCamp’s notebook editor or JupyterLab, keeping in line with how you can edit Python workspaces on DataCamp Workspace today. We are making this change so R users can enjoy all the latest Workspace features, such as sharing, collaboration, and commenting as well as upcoming features, such as native SQL integrations.

Migration timeline

  • Before March 23, 2022: all R workspaces you create, can be edited in RStudio. New R workspaces created from a dataset, a template or from scratch will contain a notebook.Rmd file.
  • March 23, 2022: Most newly created R workspaces will open in DataCamp's notebook editor or JupyterLab (you can choose). The workspaces created from a dataset, a template or from scratch will contain a Jupyter-compatible notebook.ipynb file instead of the notebook.Rmd file from before.
  • March 30, 2022: R workspaces created before March 23, 2022 can still be edited in RStudio, but you can choose to convert the R Markdown files into Jupyter-compatible notebook files so that you can start editing them in DataCamp's notebook editor or JupyterLab.
  • April 28, 2022: All remaining R Markdown files in R workspaces that were created before March 23, 2022, but that weren't converted by the workspace owners will be automatically converted into Jupyter-compatible notebook files.
  • After April 28, 2022: RStudio is no longer available on DataCamp Workspace.

Details regarding the conversion

Regardless of whether you manually convert your R workspaces or whether you wait for the automatic conversion of April 28 to happen, the conversion will work the same: all R markdown files in your R workspace(s) will be converted into Jupyter notebook files. The original R markdown files will still be available as a reference.
There are some R Markdown features that are not readily supported in Jupyter notebooks, such as options in the YAML header for setting the theme and configuring a table of contents, as well as advanced code appearance settings for including or excluding source code, printing out warnings and errors, etc. The conversion maximally tries to preserve all settings and configuration of the R markdown files, but some things can get lost. If your R Markdown files contain some of the configuration mentioned above, please review the resulting Jupyter notebook files after the conversion has happened.

Need help?

If you are unsure about how this migration will affect you or your work if you have additional questions or feedback, so not hesitate to reach out through any of the channels listed at Support.