My Happiness Record
A data analysis of my everyday mood tracked over 3 years
I have always been a hoarder of emotions, trying to hold onto memories by repeating them over and over in my head, filling up journals, and lately digital notes on my phone, but growing up it became difficult to keep up with recording life beyond a few pictures.
As I entered my 20s, I felt like time was flying by so fast that everything was a blur whenever I tried to look back and remember a moment. I was slowly losing my photographic memory to the spiral of short attention span and unannounced crises. Journaling regularly was too effortful, what was worse was that I hardly ever tried going through journals from the past years, mainly because they were cringe haha, but also because they were lengthy, with no way to know what a particular entry was about, was it something good or bad, and which one to even begin with?
That’s when I decided to start minimally recording my emotions as quantitative data, and I’ve been doing it for more than 3 years now. In this article, I’ll talk about how it turned out to be a fun self-reflection project and what the data said about my days.
I rated my mood on a scale of 1 to 5 at the end of every day as follows:
- Rating 1: Horrible day; most probably cried over something
- Rating 2: Sad day; was low due to some thought/event/person
- Rating 3: Neutral day; nothing eventful happened
- Rating 4: Happy Day; felt upbeat and/or enjoyed particular moments
- Rating 5: Amazing Day; went to bed feeling good about life
I made it into a heatmap (bad — reds, good — greens, neutral — yellow). Here’s a template if you want to start creating your own.
Initially, it was effortful to remember filling it everyday, but it soon became a fun thing to do, it was like rating the universe for its service lol.
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Keeping the analysis simple, we’ll look at some basic graphs and judge my life. I will use a maximum of 1188 consecutive data points (Oct ’21 — Dec’24) recorded till the day I’m writing this article.
Note — I’m following very basic methodology to record data as complex as human emotions/mood, thus having obvious shortcomings and biases in my data, I’ll be highlighting some of those in the form of notes throughout this article.
Graph 1 shows the percentage distribution of moods, with a total of 36% good/amazing days as compared to 20% sad/horrible days, while the rest were neutral.
Staying afloat with an average mood rating of 3.23, which ain’t a lot but it is what it is :’)
Note — Each day can have only 1 mood rating assigned to it. However, there were days when I experienced contradicting emotions during different parts of the day. A day with a cheerful morning but a sad evening will cumulatively be a neutral 3, which is not an accurate representation of the actual mood.
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My longest streak of Sadness lasted 37 Days (12th June ’24–18th July ’24)
My longest streak of Happiness lasted 36 days (11th Nov ‘22–16th Dec ’22)
The above streaks of sadness and happiness are identified by the consecutive absence of good/amazing and sad/horrible days respectively.
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Graph 2, shows the frequency of mood ratings across 3 years.
As seen above, the year 2022 had a balanced share of good and bad days; 2023 had the lowest record of horrible (Mood 1) days, while also having the highest number of good (Mood 4) and amazing (Mood 5) days, indicating it to be a happier year as compared to the other two.
2023 was a happening year for sure! My WFO schedule wasn’t strict back then so I went on 10 trips, travelling across 16 cities, and exploring places with friends, family, date and solo. The best part? I managed to meet almost all my college friends while hopping cities.
2024 seems to have an increase in bad days and a reduction in good days as compared to 2023, and it was a shitty year indeed with peak job frustration, I spent many of my leaves just to rot in bed :’)
However, in the face of prolonged sadness in 2024, I planned a few trips to create a future possibility of good days. Perhaps that’s how it fared better than 2022 in terms of good/amazing days even though they lasted only as long as the trips did.
Note — When quantifying something subjective like mood/emotions, the ratings might get skewed due to the past data if not judged carefully. Eg- If a long streak of horrible days (Ratng 1) is followed by a neutral day, it might feel like a good day (Rating 4) in comparision.
Over a long time frame, one’s range of emotions might expand or contract. The happiness level on my good days (Rating 4) of now may become what I would consider my amazing days (Rating 5) some years later because of not having had significantly better experiences over many past months.
A small check to confirm against the short-period bias mentioned above — how quickly moods rebound after Bad/Horrible days:
- 71.33% of the time, the mood transitions to Neutral
- 28.67% of the time, the mood transitions directly to Good/Amazing
This indicates that most of the time, there is an intermediate recovery period as expected, and the bias is low.
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Graphs below show the mood average fluctuations across days of the week, I’ve split it into 2 periods: a) When I was still studying/on break (until May '22) and b) After I started working (June ’22 onwards).
Graph 3a) is based upon only 7 months of data from my college life and should be taken with a grain of salt since it was around wave 2 of COVID-19 with ongoing curfews and quarantines on campus, and I was also having a short heartbreak episode that season lmao (more on this later XD).
Graph 3b) pretty much sums up my life in corporate slavery, and the desperate urge to break free on the weekends. Talking of which, Graph 4 has a trend of significant boost in mood during the holiday season (Nov-Dec at MNCs).
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After months of recording my daily mood, I realized that the data was too minimal to let me remember or correlate what made the past days amazing or horrible. So, I began adding tiny notes mentioning the triggers for good/bad mood each day.
This interestingly brought up periods of prolonged sadness without any active triggers causing the lows, indicating depressive phases/burnout.
The pattern of trigger notes depicted that 90% of the time my mood depended on the following parameters:
Only 10% of the time it was triggered by random events like — disturbing news, issues in my flat, kindness from a stranger, winning a giveaway etc.
Hence, I started recording my mood w.r.t. each of these parameters along with my overall mood during the day on a similar scale of 5.
Ideally, the considered parameters affecting mood should have been mutually exclusive, but we find some overlaps on days like the following:
- When 2 parameters turn up in combination (Correlation): A pottery workshop weekend with a friend would mark a good day for both Hobbies and Friends
- When one parameter starts affecting the others (Causation): In times of persistent anxiety due to my job, I find it difficult to experience joy in my relationship and friendships because I’m too tired or too frustrated to spend quality time when I meet up, if at all.
Note — For people-based parameters, i.e. Relationship, Friends and Family, the moods are highly dependent on physical proximity with the respective people. Eg. Unless I’m at home or on a family trip, the mood corresponding to Family majorly remains Neutral due to limited virtual interaction. This makes it difficult to seperate out genuinely Neutral records from Neutral due to distance.
As per Graph 5, Work had the maximum Bad moods. Most of the Good days at Work were usually the ones with less workload that let me spend more time outside of work, some of them corresponded to team lunch, fun events with colleagues, etc. and a few when work itself was fulfilling.
Friends and Family usually make my days better :)
Note — For Family, the frequency of Bad/Horrible days can be misleading as it marks not only the days when I was upset due to a family member (external trigger), but also days when snapped at my parents and then felt very guilty about it (internal trigger) and even days of parents’ health issues (situational)
Relationship has the maximum Good/Amazing moods and a fair share of Bad/Horrible moods. Among the 6 parameters, I seem to feel the maximum w.r.t. Relationship and a diverse range at that.
Health on the Bad end is due to sick days, and the small Good slice is from days when I went for walks/exercise or cooked food at home. Perhaps it’s time for the New Year resolution of a healthier lifestyle.
Hobbies largely uplift my mood, and have been mentioned in the trigger notes as shown in Fig 3 below with numbers indicating the frequency of mentions.
My Hobbies are centred around Art & Craft, and almost as much around Movies & Shows (online and offline). As evident, I prefer reading books as a standalone activity rather than during Travel.
I did not expect to see significant mentions of Dance, they seem to be from classes around my brother’s wedding and during outings complemented by drinks with friends.
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PS — I will later add about possible adjustments to improve future data quality and reduce biases mentioned in Notes. Discussions and suggestions are appreciated.
My analysis is pretty much complete by this point and you can close the article. But if you decide to continue, kindly expect some non-contextual blabbering from me :’)
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Since I mentioned emotional turbulence during the last semester of college earlier in Graph 3a, let me spill some tea :P The graph below shows the influence of people in my life during that time.
Blue bars represent the number of times each person was mentioned in my trigger notes.
Red line represents the average mood associated with each individual.
I generated this graph by prompting ChatGPT on my trigger notes, and this was by far the funniest thing that came out of my dataset, because of how accurate it seemed when I thought about those months.
The new love interest (sounds cheeky but wasn’t XD should rather refer to as my online friend of 4 years) mentioned in the graph was what turned into Relationship some months down the line :3 And of course, I’m glad to admit that I’m over the heartbreak guy haha
My Best Friend and Bro continue to be a source of joy even though the contact has reduced now that we are in different cities. My friendships with Friend 2 and Friend 4 have evolved to be much more meaningful over the years. I’ve lost touch with Friend 1 and Friend 3 as it was mostly a group dynamic that was disrupted due to some matter between the mutuals.
Some of the best friendships are transient and the constant ones can have their period of lows, but all of them are worth treasuring :)
The people in the graph are those with whom I had the highest interaction during those 5 months on campus, and it doesn’t encompass all the genuine friendships that I’ve found even with distances and fewer interactions. Even though this is totally out of context, I want to take up a few words to thank all my friends, those whom I’ve hugged and cried in my lows, those whom I meet maybe once a year, those who cheer me on the artistic side, those who engage in my random discussions, those who give me pep talks, those who have seen this tomboy turn into a girlie, those where friendship was unexpected but found its way, even those whom I’ve never met but exchange rants with. I might not say this in person because it would be weird, but I love you all for everything you’ve been to this otherwise loner girl with exactly zero friends in her hometown. In my lows even today, Gulzar’s lines stand true: “Mujhe mujhse milne ke liye, ek purane dost se milna padta hai” Life feels cold as an adult and I often miss the warm presence of you all ❤
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I had a lot of fun looking at all this data that I’ve been recording for a long time now, it wasn’t always this structured and I had to collate it all from multiple files. It took me 7–8 hours to just clean the data but I enjoyed every moment of that too, as all the notes took me down the memory lane, reminding me of the past ups and downs. It was interesting to look at my journey from self-doubt to faith, but also from stability to unhealthy detachment and existential dread, the push and pull of life in its varied ways.
This exercise made me want to create and experiment after a very long time, I kept fiddling with it all week. That’s how I celebrated my new year too and kinda loved it.
It was nice to look at and remember how life has changed in so many ways within just 3 years. All this data but I still can’t say for better or for worse lol. Hoping to find some meaning in 2025. On another note, I’ve been watching ‘Abstract: The Art of Design’ and it is making me feel something like hope, almost reckless.
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