Sketch 10: Data Visualization From Everyday Life

Throughout this project, I was concerned that the act of consciously collecting data would actually alter the outcome of the data. The fact that I knew I was recording my actions would probably lead me to increase or decrease that behavior based upon my preconceived notions of whether that activity was positive or negative. Instead, I looked for data that had already essentially been collected and logged. This led me down a little bit of a rabbit hole where I became more mindful not only of the abundance of data that I create, but the amount that companies like Google and Facebook are able to take advantage of that data. I decided to focus on my browser history since I utilize the internet almost constantly. I also believe in being somewhat unbiased with my hypothesis when first starting out with research. I didn’t want initial interests blocking me from finding something intriguing that I wouldn’t have expected. In the course of my searching for a program that could go through my internet history and collect some numbers, I found a browser extension called Web Historian.

These are some heat maps describing my clicking activity versus dates and times. 

April 2-8


April 9-15


April 16-23


These are some word clouds constructed from my unique search terms.

April 2-8

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April 9-15

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April 16-23

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This graph describes the amount of time I spend on certain webpages.

April 3-23

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As a result of seeing this data, I am most concerned about the times of day I am using the internet. Most of the heat maps show that I was active online in the late evening to early morning. I know that looking at a screen will make it harder for me to fall asleep. An erratic sleeping schedule has also been linked to numerous health issues. I will probably look into another browser extension that limits the websites I can visit during a set window (when I should probably be sleeping). I will probably be looking into other ways to monitor the data I am sending out into the internet in the hopes of being more cautious about companies monitoring my information.

In the future, it will become exponentially easier to track almost every data point you could ever want about your life due to better technology. There is something paradoxical about using technology, something that is definitely not human, to gain a closer understanding of humans / ourselves. I have been thinking about these things more so than I would usually due to this project. Another question I have been thinking about is how technology has affected our lives. I cannot really be a subject for this because I realize I have never really existed in an age where knowledge and computing power have been scarce.

Link to Sketch 10: Data Visualization From Everyday Life Assignment

SK 10

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In this sketch I tried to make two list of schedule graph. One for Monday and Wednesday and one for Tuesday and Thursday. I believed that this would be good idea for this sketch since the schedule is different for two pairs. I don’t think this is the accurate data, but it is good enough for estimate. Doing this project was interesting since I was able to see the pattern of my schedule on weekdays.

Data Viz: A Running Joke

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It took awhile to come up with a decent idea for this assignment. When brainstorming, I instinctively tried to select two variables that would be likely to show a correlation. And because the media seems to associate physical exercise almost exclusively with positive outcomes, I figured that exercise may be positively correlated with happiness. Measuring daily exercise output was simple: I would use the health app on my iPhone to track distance (in miles) travelled per day. Deciding how to measure happiness was the tricky part, but I eventually figured that frequency of laughter would serve as a relative indicator for happiness. Recording laughter was by far the most difficult part of this process. In the beginning especially, I constantly forgot to record laughs throughout the day. It was difficult to consciously draw my attention to something so habitual. Though, overtime it became routine and easier to record.

My immediate prediction for this experiment was that miles travelled would be positively correlated laughter. In other words, the more I exercised the happier I would be that day.  Though, interestingly enough, the results of my data analysis (more or less) displayed the opposite trend. While I must acknowledge there were a number of outliers, such as day 3, 10, 12 and 15, the results displayed a slight negative correlation between exercise and happiness. In other words the lazier I was that day the more I laughed!

I also envisioned that laughter would be lowest during the weekdays and would gradually increase in proximity to the weekends: ultimately peaking on Fridays and Saturdays. However, several days where frequent laughter was recorded, including the day I laughed the most, actually took place in the beginning or middle of the week. I laughed the most on a Tuesday, which is surprising because Tuesday is my busiest workday, and far from the weekend. I laughed the least on a Sunday, which is less surprising because Sunday marks the end of the weekend and I’ve never really liked Sundays in general. Circling back to the primary finding of this experiment, the negative correlation between miles travelled and laughter, I have wondered what aspects of my lifestyle may explain this trend. Maybe on the days I exercised less I was spending more time with friends (which can often take place around a TV or a dinner table): resulting in more laughter. Or maybe the days I exercised more I was too busy and preoccupied to laugh, or was too tired and therefore needed to sleep more. Nonetheless, I think most of my friends would report that I am extremely lazy and laugh at pretty much everything.

When composing my visual, I compiled the data I had been tracking on an Excel document and created a line graph. I then used Patina and drew in some aesthetic details to make it more visually appealing and to accentuate notable peaks and valleys in my graph. After looking at the final product I came to the conclusion that I don’t exercise as often as I should, which is bad. But I laugh very frequently, which is good!

Tracking me

What I decided to look at what how my sleep and Tv watching were associated. I did this mainly by trying to calculate how much tv or other forms of tv I watched approximately compared to how much sleep i was getting. I wanted to see this data as I was interested to see how my TV habit effected my sleep. This was a interesting thing to look at as I would assume that the more Tv I watched the less sleep I would get. When I was deciding how to depict this data I thought that a ratio was best as it highlights the how the two would be associated.

From the data I noticed that there was not as much of an association as I thought there would be. This was interesting as I assumed that I would be able to see a distinct pattern. from this information I can see that something else may be the real determining factor in my sleep patterns. I also think that if it doesn’t effect my sleep it may actually help as it relaxes me from all my other work.


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Research question: How good I am in improving myself?

Results: Not good.

For this assignment I decided to track how many times I do the things that I promised myself to stop doing or, on the contrary, to do more often. For example, I know that I rarely get at least 9 hours of sleep per night and this was one aspect that I tried to improve. However, the tracking showed that in ten days I had only three days when I slept at least 9 hours. Another thing that I wanted to improve was to make it a tradition to read for myself, not only for my classes. I tried to read every day before going to sleep; however, as you can see, I had only four days of following this “tradition” due to being tired and sleepy at the end of almost every day. And finally, the last aspect that I wanted to improve is to facetime my family more often. I know that because of the big time difference it is almost impossible to talk to them every day but I feel that I call them via facetime only once a week, which is not good. I tried to call them at least every two days but I failed to do so due to various reasons. Therefore, nothing changed and I talked to them only twice during these ten days of tracking.

The two habits that I wanted to make myself stop doing were drinking more than five cups of tea per day and listening to music every day for at least an hour. The first habit, which is drinking too much tea, is extremely hard for me to stop because I am a huge tea fan. During these ten days, I had eight days of drinking 5+ cups tea, which is not good. The second thing which is listening to music is the most difficult to improve. I listen to music. Every day. A LOT. And sometimes it is very distracting. Therefore, I tried to limit myself to only 20 mins of music every day, however, I failed to do so.

This assignment was extremely helpful to see how bad I am in improving myself, which, as I think, will force me to actually start doing something to achieve my goals, that I failed to achieve during these ten days of tracking.

For this assignment I used Infogram. You can also access my graph here.

Habit Tracking

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My approach to this assignment was to select things that people commonly choose for a New Years Resolution, that we all of course, know are going to fail. The funny thing is I have been doing these resolutions for months unconsciously and thus simply graphing them is reminding me of what I normally do and how much I do it.

The reasons that I missed some of the days I was supposed to do my habit were either because I wasn’t supposed to (in the case of exercise), didn’t have the time, or was just too tired at night when I usually do them. I mostly wanted to test how plausible it is for someone to be consistent at keeping these habits every single day. It is plausible in the short term, but maybe not in the long run.

I chose a bar graph as it would show each habit compared to each other as they are obviously different in difficulty and time engagement.

It was certainly a good self-assessment that keeps me accountable for the days that I miss and force myself to improve the following day.

Data Visualization

For this data visualization project, I decided to track my phone usage and specifically compare the average amount of time I’m on my phone during the week versus the weekend. In the beginning of the year, I downloaded an app called “moment”. The app tracks how much I use my phone each day and when and where I use it. The app also sends reminders indicating whether I’ve spent more or less time on my phone compared to the previous day. I find that it’s very useful and definitely gives me an idea of how to balance screen time and real time. The reason I downloaded the app in the first place is because while I was at the airport, I looked around noticed that the majority of people’s heads were down looking at their screen; parents neglecting their kids, spouses sitting in silence, siblings in their own little worlds. I was horrified by what I saw, but also aware of the fact that I’m guilty of the exact same thing. Then I wondered just how much time I spend on phone.  I wanted to make myself aware of exactly how often I was on my phone and not in the present “moment”.   For this project, I was hoping to track which apps I used most often, however I had to upgrade the app and pay so instead, I decided to see whether I use my phone more during the week or on the weekends.

During the week, I mainly use my phone to check e-mails, listen to music on my way to class, read the news, and text friends to meet up for lunch. I am guilty of using my phone in some classes, despite my best efforts to leave it in my backpack. I’m also in a long-distance relationship, so maintaining communication with my boyfriend definitely plays a role in how much time I’m on my phone. On the weekends, I use my phone to make plans, Facetime family and friends and take pictures of whatever it is I’m doing. I’m typically with my friends so I tend to use my phone less often when I’m with people.  I hate when people are on their phones when I’m with them so I try not to be hypocritical. I also don’t have Friday classes so I included Fridays in the average weekend usage. On average, I use my phone 186 minutes on the weekend and approximately 208 minutes during the week.  This isn’t what I expected the results to be, considering I’m in class during the week and know that I’m (for the most part) good about leaving my phone in my bag.  However, I realized through this process that the time I have in between my classes is brief, and there isn’t enough time to do my homework so I typically end up using my phone to pass the time. I’m definitely going to continue to use this app and try to decrease my phone usage overall. I personally feel that our time on earth is so valuable, so why should I waste it on a fake reality.



For my data visualization, I tried to track how healthy I was on a normal day. Although health is comprised of many many different factors, I chose 5 categories that would be relatively easy to measure:

  • Minutes asleep (I have a fitbit which measures this for me)
  • Active minutes (my fitbit also records this)
  • Number of hugs
  • Minutes outside
  • Happiness (/100 so that the results were visible). Being healthy is quite important to me, and so that is why I chose to track it.

From the data I have collected, I can conclude that my everyday healthiness varies A LOT! Sleep plays a large factor, and it is easy to see that college has well and truly messed up my sleep schedule. Some other interesting conclusions are that on some days I spend very little time outside. This was surprising to me because I have always enjoyed the outdoors and just spending time in the sun, but on days when I have a lot of work, or I nap in the middle of the day,  I spend a lot of time indoors. I also hug a lot, but it doesn’t appear to be that significant because of the scale I used on this category compared to the rest of the data.

In general, I think I was partially able to answer the question that I had posed for myself. Although my investigation wasn’t entirely all-encompassing, it gave me a rough idea and allowed me to realize some important things about my everyday life.

While gathering the data, I had to make several judgement calls about the scale of each data set collected. For example, I rated my happiness / 100 instead of /10 so that it was visible on the chart, I also rounded the number of hugs and minutes outside to the nearest 5 hugs/ 5minutes so that I had easier numbers to work with.

If I were to do this project again in the future, I would go about it in a similar way. If there was a tool that I could use to more accurately measure how long I spend outside, or how many hugs I gave, my data may be marginally more accurate, but in general, I think that I was able to collect my data with a reasonable standard of accuracy. I may also change the scales that I use, so that the number of hugs I give in a day features more prominently amongst the other data.

I chose a stacked bar chart to present my data because I think it gives the clearest representation. All of the individual pieces of data give a cumulative representation of my health on that day, and the bar graph certainly represents this clearly.

I have found this to be a pretty good tool of self analysis because it made it me realize new things about myself. However, in order to get a more comprehensive view of my health, I would record more data that is involved with health, such as calories eaten, sneezes, coughs, etc or number of breakdowns a day. Overall, I am pleased with this project.


When I decided what to track, I easily was able to pick something that I do every day and reflect upon. I always play Fortnite and I always reflect how I am feeling. I realize that in order to feel better and too be more productive during the day, an early win will fuel that. So I decided to track my wins and my satisfaction with the day.


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Data Viz

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My phone tracks the distance that I travel during the day. I wanted to categorize the distance I traveled into categories of leisure, exercise, and walking to classes. I was particularly curious to see how frequently I moved around during my free time. Basically, I discovered that my most consistent distance that I travel in a day is from walking to class, which isn’t surprising.

My main takeaway from this sketch is that I noticed that I barely move during my free time which isn’t great. After reflecting over the data, I realized that I spend most of my free time just sitting around and doing nothing. I feel like it would be better for my health if I spent more of my leisure time walking around or doing something more active.


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