This repository is a combination of these two templates provided by binder-examples repositories:
Potentially interesting is also the following template:
This repo builds on the r binder and jupyter lab binder and is complementary to the multi-language-demo binder with examples on using both R and python in both Jupyter Lab and RStudio.
Example files included:
python_example_for_Jupyter.ipynb- Notebook file using a python kernel for working in JupyterR_example_for_Jupyter.ipynb- Notebook file using an R kernel for working in Jupyterpython_example_for_RStudio.Rmd- RMarkdown file with python code chunks for working in RStudioR_example_for_RStudio.Rmd- RMarkdown file with R code chunkcs for working in RStudio
Note: to mix R and python in a single Jupyter notebook, use cell magic as demonstrated in the multi-language-demo binder examples. To mix R and python in a single RMarkdown file (and exchange information between them), make use of reticulate. The latter requires RStudio 1.2+ to work interactively, which is not yet installed as the default binder version (i.e. interactive python and python plots will not yet work in RMarkdown and the above example file uses reticulate for plotting in the knitted file).
Modify the files in the binder sub-directory to specify required dependencies for R and python. Binder will generate a docker image for your repository after every change to the repo and then will re-use the docker image on subsequent launches. This means that the first time you launch binder for the latest version of your branch, it may take some time to launch (especially with a lot of R dependencies which are all compiled from source) but will be fast for everyone else afterwards. Switching between RStudio vs. Jupyter Lab as the IDE is accomplished easily by changing the binder link as described below - the required dependencies for both are automatically installed and do not need to be explicitly listed in the respective configuration files.
Important: binder will only work for public repositories. If your repository is private, you will have to make it public in the repository settings before you can launch it in binder.
Binder supports using R and RStudio, with libraries pinned to a specific snapshot on MRAN.
You need to have a runtime.txt file that is formatted like r-<YYYY>-<MM>-<DD> where YYYY-MM-DD is a snapshot at MRAN that will be used for installing libraries.
In this line, you can request a specific version of R.
To do this list the version between the 'r' and the year, as in r-3.6-2019-09-24.
Right now the default version of R is 3.6.
You can also have an install.R file that will be executed during build, and can be used to install libraries.
Dependencies can be from CRAN, bioconductor or GitHub. Since GitHub hosted libraries are not part of the MRAN snapshot, it is best to specify a commit or release tag to ensure that a compatible version of the package is installed in the binder.
Both RStudio and IRKernel are installed by default, so you can use either the Jupyter notebook interface or the RStudio interface.
This repository also contains an example of a Shiny app.
Last, note that if your Binder URL points to a folder, as in http://mybinder.org/v2/gh/binder-examples/r/master?urlpath=shiny/bus-dashboard/, you will need (1) to put in the final slash in the URL, and (2) to avoid converted spaces-'%20'-in the URL, instead placing a hyphen.
You also need a Python notebook file such as this one.
- modify the
binder/environment.ymlfile to specify the dependencies. The file is a standard conda environment config file and thus supports conda packages, version definitions, multiple source channels as well as pip installations. - modify the
binder/postBuildfile for any JupyterLab extensions or other direct install commands