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This project was a lot of fun to take part in. My process for this project began with exploring the NHL APIs in an R file. Once I understood the structure of the API, I began writing helper functions. I started with simple functions to return tibbles from an API call. Then, I started to build complexity. I wanted to utilize functions to split up responsibilites. The Records API was relatively simple but the Stats API had more complexity, especially when it came to expanding dataframe results that were nested within an initial return. Allowing for multiple optional arguments for the API calls was also a challenge, and I had to research the best way to do that.

Once I had my wrapper function coded, it was relatively easy to start the the data exploration. I think this is a great way to go about data exploration in general - creating helper functions in the beginning can be an investment but save time in the long run.

With so much data available, it was difficult to decide what to focus on in my data analysis. I think this is an area where I could improve next time - I ended up wanting to explore a variety of tables but would rather dig in deeper to a subset next time. Perhaps I would start with a few hypotheses and explore the data this way, ending up with a few conclusions about the data.