rorywhite200
commented
3 months ago Hi Joel, I reckon this is ready for your review! I tried to keep the main structure and content of the first chapter while introducing a few novel elements. These include: use of Mermaid for diagrams, new 'Mission' on food emissions using LLM-based theory exercises, use of AI generated educational song and using the .panel-tabset class that allows us to tab between Altair and ggplot views.
Some of these elements are pretty 'out there' but I thought it's preferable to experiment and see what you like and don't like.
A few notes:
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I'm not sure how we track dependencies maybe a conda environment? I was using the 531 one (specified at top of chapter_1 using
jupyter: "531"but installed a few extras like rpy2.ipython. -
The data for food emissions is from Our World in Data. https://ourworldindata.org/environmental-impacts-of-food and the food supply info is from the Food and Agricultural Organization (https://www.fao.org/faostat/en/#data/FBSH), downloaded for Canada. I combined the data as aggregated__food_data.csv.
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For the LLM exercises, I put the OpenAI API call on a basic Heroku proxy server to avoid exposing the OpenAI API key. Seems like overkill but I could think of another way yet. Used GPT4o model which is a lot cheaper even than 3.5.
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