I have found that juggling academics, athletics, and my social life has been quite difficult the past couple of months. I often feel great during during class and practice, but then don’t have enough energy to do anything after. Or vice-versa, I have enough energy for class and for social activities, but I am tired at practice. When given the opportunity to track something in my life I thought that this was a perfect opportunity to see how my energy levels are throughout the day and how I might be able to improve them.
I started doing this by first looking up a couple of things that could impact energy levels throughout the day and I found hours slept incorporated with the quality of those hours, Hours exercised, bottles of water drank per day, and the amount of drinks including electrolytes drank per day directly affect energy levels throughout the day. Over the past week tracked these metrics to see if they affected my energy levels positively or negatively.
Originally I found from this experiment that the best combination for maximum energy was having between 7-9 hours of quality sleep, around 2 hours exercised total and an increasingly amount of water and drinks with electrolytes drank. What I did not account for ,however, was getting sick near the backend of my study. This heavily skewed my data making it hard to tell what was the best combination for maximum energy. I am planning on starting the same study over the next 2 weeks to see how the data changes with me being healthy.
For this assignment, I tracked 5 aspects of my personality as shown in the graph. I would like to know how does each aspect compare to another. In other words, I tracked my strength and weaknesses. I was not concerned with how each element changes with the days of a week so my visualization does not reflect the data of individual days but an average score through out a week. However, I only employed the data from Monday to Friday in the radar chart because during weekends my daily behavior changes drastically, resulting in inconsistent statistics if synthesized into the final score. The radar chart shows that my biggest weakness is discipline and my strong suit is optimism. I was aware of these before I did this assignment due to their obvious nature. For the other three elements, I was not so conscious of them but when I look at the graph and reflect the actions I measured for them to turn out this way on the graph, I finds that it does make some sense to me.
While gathering my data, I decided to record the things I did each day that reflect on each of these categories. For example, for self-discipline, I recorded time I spent on entertainment and how many work I procrastinated and other things. For optimism and empathy, I employed a mix of both subjective and objective variables. To develop a comparable score for each one, I standardized my data and roughly calculated a sum. If I were to do this in the future, I would have focus on just one category at a time and record more detailed objective variables. I think it could be a good tool for self analysis if sufficient data is recorded to provide a reliable and comprehensive conclusion.
In these charts I tracked my daily social interactions in regard to their duration, quality, and effect on my energy levels. I categorized my social interactions into the categories of either roommate, friends, classmates and professors, family members, or strangers. I used these 5 categories in the following 3 charts so my data would not get too complex to chart.
In my first chart I made a stacked row chart to show how much time I spent socializing with a specific group each day. The chart closely reflected my different schedules since on days I have multiple classes, like Tuesday and Thursday, I have more social interactions than on lighter days where I have little to no tasks to do. A judgment call I had to make in this chart was whether or not to include the time spent with myself into the chart, but I decided to add myself since a big chunk of time would be unaccounted for. In my second chart I looked at my social interactions in regard to how I would rate the overall quality of them. I chose to show this data in the form of a bar chart because it allows for me to compare different categories of data next to each other, letting any trends be easily spotted. Looking at the chart, I notice a trend where the quality of interactions with familiar individuals like family, roommates, and friends heavily outweighed the quality of interactions with people I didn’t know as well, like classmates and strangers. This made me realize my tendency of being rather closed off with people I don’t know as well since those interactions were rated lower in quality. In the third chart I looked at the same interactions I had that week, but instead I focused on their aftermath. I ranked each interaction on a scale from 1 to 10 based on how much they affected my energy levels. A key trend I found in this chart was that on Tuesday and Thursday the lowest rankings were recorded. Seeing as these days I have multiple back to back classes, I can correlate how the more tasks I have to do, the more impactful social interactions are on my energy levels. Another detail that adds to this interpretation is that on lighter days, like Monday and Wednesday, my energy levels after social interactions were higher since I had fewer tasks on my plate. However, to improve my findings, I could’ve added the time of day into my data. By adding this detail in, I could compare how my change in energy levels after social interactions correlated to the time of day.
I chose to track happiness, focusing on five main categories: the hours of sleep, hours of studying, number of hugs, the rating, and the hours of screen time I get each for for a week or so. I was curious on whether there were any sort of patterns between the five categories and if any of them correlated with one another. I chose a grouped infographic so that it was easier to visualize and compare the hours or amount of each category I get and can see them side by side.
Some patterns made sense for example the days that had the highest rating were also the days with some of the fewest hours of studying. However, I was surprised that there was a correlation between the rating of my day and the hours of screen time. The days were I had the highest amount of screen time were also the days where I rated the day the lowest. This may be because when I am on my phone, I am not with my friends or doing things that would’ve made my day better. Correlation does no necessarily mean causation, but it is interesting to see the similar patterns. If I were to continue this project in the future I would consider more categories and over a longer period of time to see how much these categories correlate with each other or if it were just a coincidence.
Concept: Consistency
Categories (1-10, with 1 being consistent and 10 being inconsistent):
Sleep schedule
Day
Rating 1 (same) – 10 (different)
1
3
2
2
3
4
4
5
5
1
6
1
7
3
8
2
9
5
10
1
Environments
Day
Rating 1 (same) – 10 (different)
1
3
2
9
3
7
4
1
5
3
6
2
7
5
8
1
9
4
10
9
Routes/Paths
Day
Rating 1 (same) – 10 (different)
1
10
2
2
3
7
4
5
5
1
6
2
7
3
8
5
9
4
10
6
Food and Meals
Day
Rating 1 (same) – 10 (different)
1
2
2
10
3
7
4
6
5
2
6
1
7
2
8
5
9
1
10
6
Daily Routine (right after/before sleeping)
Day
Rating 1 (same) – 10 (different)
1
2
2
1
3
4
4
4
5
1
6
1
7
2
8
1
9
8
10
2
Total Overall Consistency: 3.52
Day
Rating 1 (same) – 10 (different)
1
4
2
4.8
3
5.8
4
4.2
5
1.6
6
1.4
7
3
8
2.8
9
2.8
10
4.8
I decided on my abstract concept to be consistency. I figured that this would be something that is not normally measured but still plausible to. In this case, I settled on five categories that I thought would best represent my daily consistency: Sleep schedule, environments, routes/paths, foods and meals, and daily routine before and after sleeping.
In the end, I was able to answer the question about myself, realizing that on a noraml day, I am pretty consistent. However, there were several days that were highly inconsistent, whether that was due to a party keeping me up late or an errand changing my environment and meals. Because I recorded my ratings at the end of the day, I did not face a lot of judgement. I chose to visualize this data in a graph per day to best show how consistent I was per day, and it made me realize how certain days were consistent. I thought this was kind of funny as I could recall the events I did that day. to make me see why it resulted in that way. If I were to continue this project, I think I would add more categories and different ways to analyze it. Maybe I could look at it, not in a per day perspective, but in a per category way. In the end, I found this very useful for self-analysis as it showed my life in a completely different way. I also enjoyed collecting the data and looking at how my day reflected onto a spreadsheet as numbers and data points.
I wanted to answer the question of whether or not my productivity as well as social interaction effected my happiness. In order to answer this, I tracked my sleep quality, energy level, productivity, happiness, and social interaction, leading me to find small patterns across my data. I noticed how not only productivity and social interaction effected my happiness, but as well as sleep quality. As my sleep quality increased, so did the other categories. My sleep is essential for my day and I need to be prioritizing my sleep more in order for me to have a successful day.
A successful day to me means being happy while also balancing my productivity. Based on the data, I can conclude that social interaction boosts my happiness, however school productivity has an inverse relationship with my happiness. This is due to the fact I dislike doing homework, which I already knew. But, I did not know that my happiness would increase along with physical productivity, such as swim. The social interaction aspect of swim can also be a factor, but it’s good to know I enjoy all the time I put into my sport. I chose to visualize my data as a type of bar graph to easily see the patterns of my categories. If I were to do this project in the future, I would most likely track something food related to see how my energy or mood changes due to my nutrition.
Conceptual Issue/Question tracked I tracked my daily sleep from Oct 29 to Nov 8 to see which events made me sleep more or less than usual. The pattern is clear: I sleep less on Mondays and Wednesdays and more on the weekend. This lines up with my course schedule—ENG 101, HIST 100, and ANT 190 are stacked on Tuesdays and Thursdays—so I compress heavy reading into Monday and Wednesday nights and stay up late to meet deadlines. Morning classes on those same days remove any chance to recover. The result is a deadline-driven cycle of short sleep followed by weekend rebound, which can hurt attention, memory, mood, and long-term health.
Reflection To change this, I’ll spread readings earlier across Friday–Sunday, set an 11:30 p.m. cutoff on school nights, and add Sunday and Tuesday “buffer blocks” for spillover work. I’ll keep wake time consistent within ±30 minutes to stabilize my sleep drive, and I’ll track contributors alongside hours slept—caffeine, evening screen time, late meals, and major deadlines. If I repeat the sketch over four weeks and annotate big assignments, I can test which factors most strongly predict short nights and adjust my schedule with evidence rather than guesswork.
Graph 1:
Graph 2:
Graph 3:
Graph 4:
The conceptual issue I was tracking was my energy flow: how productive I am at different times of the day. I wanted to figure out when I should be studying (such as writing and doing assignments) and when I should be doing enjoyable activities (such as drawing). My goal is to use the results from this sketch assignment to help build my schedule for next semester. I used four categories to measure my energy flow: energy level, caffeine intake (cups), the time of day I exercise, and sleep quality. I measured the first three categories across six general time blocks: breakfast, morning, lunch, afternoon, dinner, and bedtime. I chose broader time blocks instead of specific hours because I wanted to track overall patterns rather than exact fluctuations. I also believed that my energy flow varies day to day, so focusing on general trends would be a better fit for this sketch assignment.
I organized my data primarily with bar graphs and included a trendline graph. Because my measurements for energy level, caffeine intake, and exercise time were divided into categories, bar graphs were the most effective way to represent them. I used a trendline graph for sleep quality because it was only measured once per day, and visualizing the fluctuations across days made the pattern easier to see. Graph 1 (Energy Level) shows that I am most productive in the morning and generally more productive from breakfast through lunchtime. I included error bars (± standard deviation) to show how consistent the average values were. The error bars are roughly similar across time blocks, with some slightly larger due to one day when I slept poorly and felt low energy. This supports my reasoning for focusing on general trends instead of assuming my energy is identical at the same exact time every day. I also realized that I did not drink as much caffeine as I expected; I only had coffee or tea on days when I had an assignment to finish or when I felt particularly tired. My exercise tended to happen in the afternoon, which aligns with the decrease in my energy during that time, possibly due to a lot of physical exercise. My sleep quality was generally between 7 and 9 (on a scale of 1–10), with a single drop to 2 on a night when I had trouble falling asleep. I do think the graphs could have been more visually creative (for example, adding pie charts), but since most of my data was categorical, bar graphs were still the most effective choice.
Based on these findings, I seem to be more productive (with assignments and schoolwork) in the first half of the day (from breakfast to lunch) than in the second half (from afternoon to bedtime). When building my schedule for next semester, I can prioritize morning classes and aim to complete academic tasks earlier in the day. I can then dedicate afternoons to activities that require less mental focus, like exercising or drawing, to give myself a break. If I were to continue this project, I would keep most of the categories the same because they provided useful data for self-reflection. Even though I had a general sense of when I am most productive, it was interesting to see the trend supported by actual measurements. However, I realized that my caffeine data was too limited and did not appear to strongly influence my energy flow. In the future, I might instead track screen time before bed to better understand why my sleep quality fluctuates and what I could do (such as reducing nighttime screen use) to make it more stable and consistent.
Conceptual Issue / Question Tracked
For this assignment, I was tracking how my daily energy, focus, mood, sense of purpose, and progress changed throughout the week. I wanted to see whether my mornings or evenings tended to be stronger in certain areas and to identify patterns in my productivity and wellbeing. Overall, I was asking myself: How do my internal states change throughout the day and across the week, and what factors seem to influence them?
Conclusions from the Data
From the data, I noticed clear patterns in my energy and focus levels. Mornings often started stronger, though not consistently, and evenings could be either productive or draining depending on what I had done that day. For example, on days when I had work or social commitments, my mood sometimes improved at night even if my energy was low, reflecting the impact of engagement with others. Conversely, on days when I felt physically unwell or overextended, both energy and progress scores were lower, suggesting that physical wellbeing strongly influences my mental state.
Reflection on Process and Visualization
While gathering the data, I had to make judgment calls about how to rate myself objectively, especially since categories like “purpose” and “progress” depend on how I felt in the moment. Including qualitative notes each day helped me better understand the numbers. My reflections—like mentioning when I had work, hung out with friends, or felt physically drained—gave context to my ratings and revealed connections that the data alone couldn’t show. I chose to visualize the data through daily averages of morning and night scores because it made patterns in my week more visible and allowed me to see how external events shaped my internal states. If I were to continue this project, I’d keep combining both quantitative and qualitative tracking since together they make self-analysis much more meaningful.
Screenshot of my data collection
