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.


Sunday Sketch: Data Visualization

For my data viz project, I chose to track how many times I spoke to my mother over the course of a few weeks. Before I go on, I just want to say that I love my mother with all of my heart, she is a wonderful woman, and an outstanding role model. I also know that she does want the best for me (no matter what I think in my head when I’m angry sometimes) and she is willing to push me until I realize my full potential. Reasons why I chose this project:

  1. My mom can be a little overbearing (she knows this)
  2. I’m pretty sure she has a serious case of FOMO, as she wants to know Every. Single. Detail. of my life. I say this jokingly (kinda).
  3. I was actually really curious how often I talk to my mom!

I decided on tracking how may cell phone calls, FaceTime calls, and text messages exchanged between my mother and me. As you can see in the graphs below, there is a lot of fluctuation within some of the days. April 16th, for example, was the day I got an exam grade back while April 18th was the day she had breast reconstructive surgery following her breast cancer diagnosis in June of 2017. On the average, I think we FaceTime at LEAST one a day, and average about 40 texts. Here are the graphs. (I included pictures as the background because my mom and I are kind of the same person. My dad says the apple didn’t fall far from the tree with us!)


Sketch 10: Food Satisfaction (Data Visualization)

Screen Shot 2018-04-22 at 8.27.38 PMThroughout the span of almost two weeks, I recorded my level of food satisfaction on a subjective scale of 0-100. This tracking assignment yielded some interesting results, which after developing a visual representation of the data, revealed key information about my eating habits throughout the week.

Firstly, a noticeable trend appears in the weekends, where my levels of food satisfaction increase dramatically compared to weekdays. This is because during the weekend, I tend to go out and eat with my friends or my girlfriend, in an attempt to escape the brutal grip of the duc-ling; its merciless grasp on my palate is accurately reflected by the low values presented during the week days, where values oscillate in the lower bound of the graph. Any strong outliers during the week day, such as the second Tuesday in my tracking assignment, are attributed to my ordering Uber Eats. As a general rule, if the value of food satisfaction exceeds 30 during a week day, it could be due to three factors: either I ate outside of Emory, went to Cox, or ordered food through Uber Eats.

Data Viz From Everyday Life- Sketch 10

Coming to college and being away from home I have made a conscious effort not to abandon healthy habits throughout my everyday life.  The five factors of this category I decided to track are how many of my 75mL camelbak water bottles I drink per day, if I eat breakfast, how many times I take the stairs up to my rooms instead of the elevator, how many hours of sleep I get, and how many miles I do on the treadmill that day at the gym.  As I was mapping out this data visually, it struck me how dependent these habits have to do with my schedule for that day. For example, some days I have time for the gym while others it is impossible to fit it in between classes and homework. I know that I had a large chemistry exam on April 11th at night so knew the gym would not fit in that day.  In addition, It seems as though the days I miss breakfast, the most important meal of the day, falls on Tuesdays or Thursdays. These days of the week I have an 8:30am class that I am usually rushing to. Significantly less hours of sleep took place the nights before the two exams I had during the two week time period I tracked. Finally, more hours of sleep take place over the weekend because I am able to sleep in later instead of waking up for my morning classes.  

A flaw within my data is that there is no basis I could compare it to.  When I tracked how many times I took the stairs up to my room on the fourth floor of my dorm, I did not track how many times I took the elevator up.  Days that I only took the stairs four times could just be because I only went up to my room four times that day. It does not necessarily mean I took the elevator much more those days.  I am in class from 10a.m. to 5 p.m. on mondays so do not go back to my room till late in the afternoon. It is inevitable for me to take the stairs less these days.

Since I was tracking data that took place so frequently throughout the day, there were definitely times where I had to estimate different factors.  I know there are times where I quickly went downstairs in my dorm to print something and did not have my phone with me to track the stairs I took back up.  In general, however, I hope I got a good estimate of the healthy habits I have put into my everyday life here at Emory. Screen Shot 2018-04-22 at 8.03.43 PM

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