Data Selfie App – Snow Day

Drawing Connections

I compiled a bunch of selfies during the week using Joey’s selfie app starter code. In the    beginning, it was fairly simple, and I eagerly took many selfies, as planned daily. However, after the third day, I either totally forgot or the task became monotonous. I don’t take selfies regularly so I can’t say I had much fun with this one. Until Monday, March 4th — New York City snow day; NYC public schools were closed for the day along with universities and many businesses.

Side Note:

This is a picture of NYC “snow days” when I attended public school. Seriously.

Image result for nyc blizzard 1996

I decided to snap pictures throughout the day, when my dog barked but that was REALLY excessive because Magic barks every 30 seconds. I’ve been thinking about the gym lately and it’s been on my to-do list so I decided to take a selfie every time I thought about anything related to the gym like going to the gym, food plans to compliment my workout regimen, my workout routines etc. I collected 50 pictures during the day – and of course I needed to spice the selfies up because my face in every picture isn’t that exciting.

I also spent a lot of time working on the API Review and Simple Express API Demo which was pretty exciting and challenging. I did pretty well but ran into trouble making a put request.

I tried customizing my photo app a bit a came up with a few different shots. I also removed the geo location because its pretty accurate and that’s concerning once its shared publicly.








At the end of it all, I thought about the main question from the readings last week, “What are we going to do with all this data?”I know what I did, I deleted it.

My Data Feelings

ASSIGNMENT 3 (DUE: WEEK 04, 25 FEB 2019)

For this weeks assignment, I decided to build my data feelings app  around my daily goals. I love to do lists and task management apps. Lists allow me to hold myself accountable and I also enjoy the feeling of completing a goal and marking it done. In Making Sense of Data, the author mentioned “cultivating a habit” as one of the five common styles or purposes for self-tracking. Although habit tracking techniques are more about forming habits that support a desired outcome, I believe to do lists also relate (indirectly )to developing habits and/or identifying motivators. I create to do lists with the intention of completing a task (desired outcome) and I’m highly motivated when items are closed…  Habit tracking, yes?!

I followed the extremely helpful “how to video” Joey emailed last week and updated the data model with my own parameters using the default visuals. I ran into trouble modifying the event listener for a new range; the example code was identical but I believe I saved the modified code outside of a loop somewhere in another file. I figured things out after several hours and a quick  suggestion from Joey.

Update: I decided to track my emotions during this homework assignment because I experienced a range of different feelings while troubleshooting errors and breaking things along the way. The final output is a generative data visualization of my emotions  while working on assignment #3. Interestingly enough, I noticed similarities in certain shapes when I adjusted the range for “frustrated” vs “happy”.


A week of Dawgs


About the Data:  I tracked the number of dogs I spotted for a week and it was pretty fascinating.  Collecting the data was exciting at first —  I spoke with the owners to learn more about  their dogs. Eventually, it became exhausting and I decided to take pictures and guess the breed or search online for a match using . What Dog classifies dog breeds using machine learning. Most of the photos I uploaded were  close-ups and the site returned  pretty accurate results for  each photo. I began tracking by using an application on my phone but there were too many steps involved and it was distracting so I decided to track with a notepad and pen. I recorded the breed type, color and size of each dog during my commute to and from Brooklyn and Manhattan each day/night. I transferred the data from my notepad to excel and  calculated the total count for the week along with the total count of different colors and the total count based on dog size. The data viz starts from the top of the postcard (day 1) and ends at day 5, the last day of tracking. I stayed in on Friday, Saturday and Sunday but managed to spot a dog while looking out of my living room window .  My excitement is clear and obvious in the first few days of data collecting; I started on the morning of Tuesday, Feb 5th and ran around the city like a mad person looking for data (dogs). By Thursday night, I only recorded dogs within walking distance or close enough to determine the breed. Here are the final numbers:

Total # of Dogs: 53

Tuesday: 15

Wednesday: 27

Thursday: 9

Friday: 1

Saturday: 1

Side note: I forgot to include my dog!!!, Magic Voilà Reggler (He’s the last dog on this page — sorry Magic!)

Any thoughts about how your self-tracking ideas might be refined as you gain more exposure to the discussions about self-tracking…

I learn something new with each self-tracking/tracking assignment, particularly getting into the habit of paying closer attention and  making a deliberate effort to change my perspective to see more around me.








A1.1: Personal Data Download


My Data Double

Tracking down the digital breadcrumbs of my life.


  • Requested: ✅
  • Downloaded: Available Feb 5th
  • Notes: My secondary messaging app
  • Some notable features or things worth exploring include:
    • Photos
    • Conversations
    • Frequency and time messages were sent and received


  • Requested: ✅
  • Downloaded: Yes
  • Notes: I use Yelp from a browser often and rarely from my personal account
  • Some notable features or things worth exploring include:
    • My reviews
    • Places I’ve checked in
    • Bookmarked places – places to visit

Youtube – Search & watch history

  • Requested: ✅
  • Downloaded: Yes
  • Notes: I YouTube everything. Diseases and cures, horoscopes,  fitness, hobbies, fashion, advice, self-help. Everything.
  • Some notable features or things worth exploring include:
    • What I care about
    • Most watched videos
    • Search history


  • Requested: ✅
  • Downloaded: Yes
  • Notes: I spend a substantial amount of time on Instagram. I use  an iPhone feature called screentime for alerts when I reach my daily limit.
  • Some notable features or things worth exploring include:
    • Photos shared
    • Content I’ve posted
    • Tone of content / Identify mood and emotions
    • Photos I like


  • Requested: ✅
  • Downloaded: Yes
  • Notes: Primary car service
  • Some notable features or things worth exploring include:
    • Times and locations of trips
    • How often I use this service
    • Fares


  • Requested: ✅
  • Downloaded: Yes
  • Notes: I’m an online shopper
  • Some notable features or things worth exploring include:
    • Purchases
    • Shopping frequency

A1.3: Reflection

Week 1

I don’t have much of a relationship with self-tracking. I tend to stay away from tools and applications used for tracking activity. I guess I’m uncomfortable with the amount of information accessible and readily available online. My Facebook account has been inactive for almost 3 years and I try to limit the amount of information I share on other social media platforms. However, I’m not against the quantified self movement and encourage people to use the tools available to increase self-awareness and make constructive life adjustments.

I have a completely open mind for this course and I’m willing to test some self-tracking tools with limitations. I’m interested in emotion tracking, specifically how music impacts my mood and concentration. I’d like to know what music makes me happy/sad, how my mood evolves throughout the duration of a particular song, what part of the song triggers emotions, my productivity levels while listening to music, and how the data could be used for better decisions. At this point, I’m not entirely sure how I plan to track the variables of interest and I expect to face challenges with consistency.

Other topics to discover:

Pain management and food consumption: How my body reacts to certain foods . Why does my X hurt on X day? What foods trigger symptoms?

Sleep patterns and mood

Relationships – my connections with family and friends

I got a Fitbit as a birthday gift a few years ago and it collected dust after only a few weeks. The data was always inaccurate  and I was discouraged after a few bad readings. I’d like to take a different approach with activity tracking this time around and have patience for possible glitches and data inaccuracies.

A1.2: Self-Tracking Projects Review

Week 1

Review of a few projects relating to self-tracking and the quantified self

Rocio Chongtay: “Quantified Brain and Music for Self-Tuning”

Description: While studying for a Masters in Artificial Intelligence, Rocio Chongtay noticed she had trouble concentrating with background noise , total silence or music with lyrics or catchy beats playing. She also realized specific music helped her focus and cancelled out background distraction. Chongtay experimented different types of music while quantifying her levels of concentration and relaxation using  a BCI (brain computer interface) visualizer by Neurosky.  The EEG (electroencephalography) sensor  monitors 8 different brain waves and integrates with iTunes to measure brain electrical activity while listening to different music. After quantifying her own brain, Chongtay was able to identify high productivity levels and create an ideal playlist based on self-tuning.

Source for graphic: Vimeo 1:00

Broader Significance: Music can be used as a tool for motivation and inspiration. Research suggests that pleasurable music releases dopamine in the brain.  Playlist could be  created and used specifically for depression and productivity.

Why it’s interesting to me: Music is a huge part of my life and I usually try to integrate it into everything I do.  I also have concentration issues based on the type of music playing, similar to Rocio Chongtay. “Self-tuning” would be a great tool to find and monitor motivators while listening to different music.

Peter Torelli: “The Quickening. Narratives Hidden in 20 Years of Personal Financial Data”

Description: Peter Torelli tracked his financial data for 20 years, starting with manually logging transactions in Quattro Pro spreadsheets and  saving the data on floppy disks to data residing on Quicken servers. Torelli’s spending trends revealed more than his financial transactions; the data also displayed visceral memories and pivotal moments in his life.

Source for graphic: hidden-stories-20-years-financial-data

Source for graphic: Vimeo 4:48

Broader Significance: Collecting financial data is a representation of spending habits but data also has the potential to show specific patterns and highlight changes in habits caused by life events. This could be helpful for making decisions indirectly related to spending.

Why it’s interesting to me: In a perfect world, I would track my spending, monitor my habits and make better decisions based on data available. Torelli ‘s story is instructive and motivates me to become more conscious of my spending decisions.

Steven Jonas: Spaced Listening

Description: Steven Jonas has a short attention span for listening to albums the first time around. He believes his brain rejects unfamiliar music and prevents him from listening a second time. Jonas developed a “spaced listening” system using a tool called Anki to remind him to play new music and google forms to track his listening experience. The tools helped him appreciate new music and overcome rejection of novelty.


Source for graphic: Steven Jonas: Spaced Listening

Broader Significance: Opens up a listeners ability to experience and appreciate  different genres of music.

Why it’s interesting to me: I love late 90’s hip hop so naturally I tend to listen to music from that era the most. Jonas provides great suggestions and ways to broaden my musical palette.