I have made an experiment with my last paper: Write everything from scatch in Jupyter Notebook, including data preprocessing and generation of all figures (etc.) (10 Notebooks in total).
Start of the conceptualization was in 2017, we just submitted it 2 weeks ago (it got desk rejected for not fitting the journals topic).
I learned a lot and it was definitly worth it. The next paper will be easier with this knowledge. Nonetheless, there is an overhead and I feel that this overhead is not valued with the current makeup of journals, where you really need to dig deep to find any supplementary materials.
I did [something similar] too when I started my PhD ... I had one Makefile managed project that ran everything with dependencies. From raw data, to figures and even embedding the numbers into the final, Latex-based PDF.
My supervisor manually copied all of the text from my PDF into a word document on his first revision ...
I learned a lot and it was definitly worth it. The next paper will be easier with this knowledge. Nonetheless, there is an overhead and I feel that this overhead is not valued with the current makeup of journals, where you really need to dig deep to find any supplementary materials.