Ways to do data science in Renku
This is the main way Python and Julia users will interactively do data science. In the JupyterLab interface, use the file navigator in the left sidebar to go to the Files (1) tab. Here, you see a listing of files and folders from your project. To create a new notebook, first double-click on the notebooks folder (2), then on the ‘+’ button (3). Select Python 3 or Julia 1.*.* to create a new notebook (4).
To rename the notebook, right-click on its name (Untitled.ipynb) and
select rename.
You can read more about JupyterLab in the JupyterLab documentation if you are not familiar with it already. You may want to take some time to play with the JupyterLab interface before continuing.
To quickly save your work, the easiest is to use the renku save command from
the terminal - this will commit any uncommitted files and sync the changes with
the git server. You can specify a custom commit message with -m.
$ renku save -m "saving the new notebook"
Successfully saved to branch master:
notebooks/new notebook.ipynb
OK
Alternatively, if you want more control over the commit process, you can go to the console and use
git to add your work to the repository. For example, if you want to keep
the new notebook(s), run the following in the terminal:
# track everything inside the notebooks folder
$ git add notebooks
$ git commit -m "Added some notebooks"
$ git push
[master 0fb9ac1] Added some notebooks
1 file changed, 32 insertions(+)
create mode 100644 notebooks/MyNewNotebook.ipynb
Counting objects: 4, done.
Delta compression using up to 8 threads.
Compressing objects: 100% (4/4), done.
Writing objects: 100% (3/3), 639 bytes | 639.00 KiB/s, done.
Total 4 (delta 1), reused 0 (delta 0)
To https://dev.renku.ch/gitlab/john.doe/flights-tutorial.git
c1dcfe4..0fb9ac1 master -> master
If you prefer to use a GUI, an extension providing access to the basic git
commands is built into JupyterLab. Click on the git icon on the left sidebar of
JupyterLab (1) to open the git panel. Here, you can add untracked files by
selecting them and clicking the up arrow (2). You can enter a commit message in
(3) and click the Commit button. Finally, sync your changes with the server
by clicking on the push icon (4).
The canonical way of working interactively with R is using the RStudio IDE. As default, there are four panels available in the user interface, which cover a code editor (1), Console/Terminal section (2), Environment panel (3) and a file explorer (4).
We note that it is actually possible to run Jupyter Notebooks with an R kernel, if one navigates to the /lab endpoint of the URL of the renku project. There you can select the R kernel and proceed as usual. However, we proceed with this part of the tutorial in RStudio.
The R console and script editor can be used together to quickly prototype data pipelines. An advantage of RStudio is that the Environment panel will show all the data frames and variables stored in local memory.
In order keep R scripts organized, you may want to create a src folder in the
main project directory.
An important part of using renku within RStudio is accessing the Terminal (1). This is where you can access renku commands and make sure you have tracked changes using git. The Git tab in the Sessions panel (2) also gives you the option of a graphical user interface (GUI) to git.
