week #9

D3: Hello again

This week I continued my D3 explorations. Last week I basically followed a tutorial and made some adjustments, so that went fairly smoothly. But this week my biggest challenge was starting — everything I could think of felt either not interesting enough or too complex to achieve (and I don't know enough about D3 yet to even know what's possible). I like dealing with topics that affect how people make up their minds on things — that's kind of broad, but I figured I could do something with the NYT API. So I started by signing up and getting all kinds of data.

NYT's Top Stories API logs the current homepage articles with all their metadata and media

The problem was it was all textual data, and D3 mostly works with numerical data — I would need to count something, or compare things — ideally over a long(er) period of time for the data to be meaningful. Might get back to this at some point but for now I decided to pivot because I'm still just learning the ropes of D3.

One of the first ideas I had when I wanted to learn D3 were about looking at keyboard characters' frequency in all kinds of things (from passwords to baby names to stock market acronyms). I decided to give it a try and looked for databases of compromised passwords that I could use for analysis. I found an overwhelming amount of such databases. I started with a short list just to get going but ended up using a 100,000 line list. The idea was to show the keyboards' keys on the screen, and have them colored differently to represent their occurrence in a given dataset. I started with just making one simple greyscale keyboard where each key gets darker the more it's used in passwords. I then added a second keyboard, visualizing the occurrence of each character used for the first letter of a given password. My main goal was to set it in a way that is scalable and that would easily let me visualize different character occurrences.

The next thing I would like to do is figure out a better way to scale the data. Most keys don't get used so often, but a few keys get used a lot — visualizing this is quite difficult because the gap between the two extremes wash out most of the keys. Last week I used a quantized scale because that made sense with the data, but I'm not sure it makes sense here. A logarithmic scale either didn't work because I did something wrong or wasn't a good solution anyway. I'd like to read more and understand these things better so I can make informed decisions regarding visualizing the data in a meaningful way. So I'll start from that next week.

GitHub repo, live version

User testing

I'll try to have very open ended questions that I could use to infer whether my goals are being met or not, like:

  1. Do you find this information interesting? 
  2. What do you see?
  3. What does it make you think?
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Jasmine Nackash is a multidisciplinary designer and developer intereseted in creating unique and innovative experiences.