General Coding
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Implement unit testing for novel method codes.
Write documentations! A usage example is always a plus.
Keep data compilation scripts well-maintained, and log/store intermediate results.
Provide a test dataset.
Provide a setup requirement file, or conda/pip environment.
An interactive demo in Google Colab is great, depending on the specific project.
Use jupyter notebook whenever and wherever you can.
Clean up your Jupyter notebook! Consider using a .
Provide processed data files as a downloadable Tarball, so that all Jupyter notebooks can run without raw data processing. Consider using Google drive and .
Use to format your code for better Python code readability.
Use .
Write unit-tests, and check code coverage by .
Use R whenever possible; avoid for-loops.
Use as a workhorse for data processing.
Write functions to reuse code; be aware of R's .