Some people eat to live, but I’m in the camp that lives to eat. I consider myself a foodie: a person who is very interested in […] eating different kinds of food (Oxford, n.d.).

As a scientist in training, I knew better than to make a baseless claim without any supporting evidence, so I sought to do a quantified self (QS) project. Broadly speaking, the quantified self movement is about learning about oneself, or addressing a personal research question through data.

Here is the personal research question I posed:

Do my eating patterns (e.g., frequency, diversity of cuisines and meal types) reflect and warrant my self-proclaimed “foodie” title?

Note: The average American eats out roughly 4.9 times a week per year (Zagat, 2018), or 254+ meals a year, so I aimed to exceed that. As for number of cuisines, I didn’t have an average consumption statistic, so I set personal goal of at least 10.

Who stands to benefit from this information?

I would consider myself as the primary beneficiary from this information, as I would be deriving self knowledge. It would be personally useful in several ways: 1) I can use the information I obtain to help me decide on what cuisines to eat (e.g., was there a cuisine I particularly preferred, were there some I seldom had or have yet to try?), 2) it can provide insight on ways I can diversify my meals, and 3) it can serve as a diary since I will annotate memorable meals and also events that may have influenced my eating habits.

However, I can see it potentially benefitting people who may be interested in where and what I eat. I have had friends and social media followers ask me questions about food spots and recommendations in the past, so this might be helpful for those individuals.

How do I answer my question?

My first step was to gather information about the meals I had in a given timeframe, I decided to start from the beginning of last year, January 01, 2019. While I didn’t have written records of every meal I enjoyed, I am one of those people that takes photos of a lot of their meals. My friends joke that my camera eats before I do.

The photos came in handy; I ended up going through my camera roll and instagram stories and recorded the date, cuisine type, meal type, and location (thanks, metadata!) of every meal I had on record.

I also happen to be a regular user of reservation applications and food delivery services, which kept records of the aforementioned data as well.

After all the data were entered and organized, I opted to create a Tableau dashboard of several visuals that touched on the following: how frequent I ate, how diverse my meals were (both cuisine and type-wise), where I ate, how I obtained my meals, and my eating habits across time.

I present to you, Dear food diary:

(or click here to view via Tableau Public)

Data and design decisions.

Because my overarching research question was multifaceted, I thought a dashboard would be most appropriate. Each chart or data-object within the dashboard represented data from the same timeframe: January 01, 2019 to October 23, 2020.

I chose to title the visualization “Dear food diary,” and signed it off with “Sincerely, Melissa,” in a handwritten script font to emphasize the personal aspect of the project. Food elements (e.g., letter O as a dinner plate and food vectors below the text) were utilized to add to the food theme. Like a “diary,” I organized the dashboard to be read from up to down, left to right. The color scheme is a light one, based on personal preference (which I felt was appropriate for a diary-like design) and kept consistent throughout.

First, I added a brief summary to introduce the research question and highlight in a different text color the overall conclusion I came up with after going over the data.

Next, I decided to use big bolded numeric values to visualize how many meals I had and how many cuisines I had tried. These were my primary questions and were the metrics I would use to answer my question. I felt that this representation made the numbers really stand out.

To get more details about where, what, how, and when I ate, I utilized several charts.

Where: I mapped out all of the locations of the meals I had via a geographical heat map, with darker colors representing areas my “hot spots,” or places where I eat at more. If the viewer hovers over a dot, they are able to see when I had the meal, what meal and cuisine it was, and a more details on the location.

What: I used a tree map to depict which cuisines were my favorites. A tree map is appropriate because I wanted to show all the cuisines I had tried, but highlight my go-to’s since they would be occupying the most surface area and be darker in color.

How: To see which method I used most often, I chose a donut (adaptation of a pie) chart because I wanted to show proportions and I only had 3 categories (in-person, delivery, takeout). It’s easy for the viewer to see which category occupies the largest piece of the donut.

When: Originally, I considered a line graph because I was interested in temporal trends, but as I started to model the data, I realized that some of the meals could get “lost” that way. In other words, I wanted to find a way to be able to view my eating patterns across time, but additionally represent the data such that I could show the viewer snippets of a particular week. I had so many fantastic meals and memorable moments associated with them that I wanted to incorporate it. This was supposed to be a diary, after all. It was then when I figured that I could do much more with a circle view graph. I represented meal type by color and method by shape, with number of meals on the y-axis and week on the x-axis. I also inserted annotations of important events that had happened during the time period and photos of some of the meals that I had, viewable via the tooltip. The top circle graph was for the year 2019, whereas the one on the bottom is for 2020. I decided to go with two to further emphasize the difference in frequency and diversity of my meals last year compared to this year.

Next steps.

Compiling the data for this project made me realize how much richer the story could have been had I made more detailed records. It would be interesting to examine the cost of my meals, my rating of the meals, and perhaps even record my subjective feelings prior to purchasing the meal and thereafter.

I drew important insights from my completion of this project. I met my own criteria of a “foodie,” but now I had several more questions. Was Japanese cuisine a top cuisine because I had spent nearly two months in Japan? I try to could filter out those study abroad dates and see whether its ranking still stands. It also appears that I eat more often and a greater variety of meal types when I am traveling. In fact, as the shutdown happened, my meals grew increasingly homogenous, almost always delivery lunches. It’d be great to follow-up on this project and see my habits once the pandemic is over.