Using R, I clustered my dataset and determined 5 main eating patterns.
February was an interesting month to track because of the change in lifestyle. I went to Vancouver from February 16 - 21, which was when a spike in amount of types of food occurred. This was because as a student, I'm normally limited to the options I have around me regularly, plus how I prefer to cook at home most of the time. I started feeling a bit ill in Vancouver, and looking back at the food choices, I'm not surprised; a normal day for me at school would constitute of "egg, grilled zucchini, baked chicken, popcorn" but Vancouver yielded a lot more variety, from sushi nights to "beer-battered scallops and prawns, fries, takeout noodles".
I chose not to separate my types of protein (animal/plant-based) for my analysis because I don't tend to eat much meat, which is a mild point of concern to my parents. Thus, I wanted to see my normal protein distribution, regardless of the source.
I took the liberty to conduct some statistical analysis on my dataset. In February, I started really liking kimchi omelettes. Given the condition of me eating an egg, I made a regression model that showed that if I ate an egg that day, the likelihood of it being part of a kimchi omelette was 17.58%.
In total, I'd say that monitoring my food intake really made me more aware about how unhealthily I eat. I do try to fill my day with as much fruits/vegetables and protein as possible, but I actually eat a lot of filler carbs and snacks without realizing it. It pains me to admit, but I especially eat an unhealthy amount of cookies.