Data Visualization

Tracking 2018 - A look at a year in numbers and stats

I wanted to investigate the idea of quantifying myself by recording and analyzing my habits. I don't think a person can be broken down into mere numbers (there are more nuances than that), but I wanted to challenge my understanding of humanity either way. This is a personal project of mine, as well as a technical exercise to build up my data visualization and analysis skills.

For 2018, I collected and tracked my own habits to create and analyze my own data. Each month focused on a different theme or habit, as well as different methods to collect data.

This project is currently on hiatus. The study only features January - April, but data has been collected for the rest of the months and is currently being analyzed.

January - Caffeinated Beverages

To kickstart my foray into this project, I took baby steps and documented my newfound appreciaton for caffeine. It's not much to be honest—I simply picked up the habit of a daily coffee or milk tea during my term in Singapore in late 2017. But I soon came to enjoy the taste and wanted to see how regularly I drink coffees/teas/hot chocolates.

Prior to Singapore, I didn't drink any coffee and I only relied on an occasional hot chocolate to satisfy sweet tooth cravings.

This is a numerical breakdown of the number of drinks I drank this month, as well as a percentage breakdown of the specific types.

The blocks are proportional to each other, meaning I drank double the amount of coffee as hot chocolate.

My coffee intake started to spike and pick up around Tuesday, January 16, because I brought in a Tassimo from home. According to my stats, 50% of my total monthly intake was from home-brewed Tassimo coffee, meaning that most of my consumption was in the second half of the month. This also made sense seeing as I had a heavier workload and some competitions to prepare for at the end of January as well.

February - Food Type

In February, I tracked my eating habits. The visualization below illustrates the food groups (plus caffeine) that I consumed. I annotated the foods I ate for the month in an Excel sheet, and cleaned the data accordingly to derive sets of analyses from it.

February was an interesting month to track because I went to Vancouver from February 16 - 21, which was when a spike in amount of types of food occurred. As a student, my diet is very simple, but I had a sudden influx of eating out and trying new types of food.

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.

The two most popular snacks in February were Lara bars and cookies. This graph illustrates how many times I would eat cookies/bars a day. Typically, I ate 1 Lara bar at a time, and sometimes 1-2 cookies at a time.

My Lara bar consumption dropped while my cookie consumption continued (and even peaked at one point) mostly due to lack of accessiblity in Vancouver, as well as the cost.

March - Outfits

For March, I tracked the outfits I wore. I took an "Outfit of the Day" photo every day, and then quantified and analyzed them according to article of clothing, brands worn, and tried to extrapolate trends from my daily aesthetic.

The graphic above depicts a general overview of the colour combinations I wore during the month. Each row represents the day of the month (i.e. 1-8, 9-16, etc.)

The graphic below shows the general outfit trends I seemed to follow.

April - In-person interactions

April was a month of interactions. I looked towards the people I spoke to; my cue for this month was based on verbal communication after speaking to a friend, and him suggesting that I focus on something that "speaks about how I interact with others". It was a refreshing twist to record these because my first three months were solely about my own habits.

I took note of the types of people I interacted with, such as the more obvious family & friends categories, but also the strangers I talked to. I could've kept a small conversation going or simply said a thank you to a service industry person, but I included all the strangers I spoke a word to. I was actually the most excited about this part of my analysis.

On average, I spoke to 2 family members, 2 close friends, 2 friends, 1 acquaintance, and 7 strangers.

While the first graphic broke down the types of relationships I have with those I interacted with, this bar graph shows the number of people I spoke to on a daily basis. The width of the coloured bars in the first graphic are proportionate to the number of unique folks that day.

May onwards

Collection and/or analysis in progress.

© Tara Tsang 2019