What I made
My very first visualization was a static one:
This visualization is a scatter plot of 2012 Olympic countries’ medal count vs their per capita income. The radius of the data points is proportionate to the number of people that country sent, and the pie chart is the break down of gold, silver and bronze medals.
path = "M" + startPoint.x + " " + startPoint.y + " L" + (startPoint.x + radius) + " " + (startPoint.y) + " A" + radius + " " + radius + " 0 " + longArc + " 0 " + endX + " " + endY + " Z"
In addition, you can even see on the yaxis that the label is vertical text facing the wrong direction. I was a sad man and couldn’t rotate the text the other way without it ending up somewhere way off the page. This poor fellow ran into my same problem, but had the sense to ask on stackoverflow.
For my next project, I decided to try making an interactive map. I found a bunch of data on this UN site about where refugees originate from and where they are seeking asylum. This seemed to be screaming for some sort of map visualization. So I made this:
This was a great exercise for my first interactive visualization. D3 makes it extremely easy to do mapping. All in all, the project took around 10 hours to complete and I learned loads more about SVG.
I made this one with a partner. We chose to visualize a huge chunk of student survey data. The students were in 8th grade when the survey started and there was a second round conducted when the students were in 10th grade. Our main purpose for this visualization was to tell a story. We ended up deciding that parallel coordinates was the best way to communicate the information we found most intriguing.
Our visualization was built on top of one of parellel coordinate examples in the D3 gallery. This was great because we got to really understand a well made D3 example and then extend it to fit our needs. It also introduced me to brushing and linking, which is a great tool for visualizing huge datasets.
African refugee populations
After digging deeper into the UNHCR (United Nations High Commissioner for Refugees) database, I found even more interesting data albeit in a worse format. I found Data Wrangler an invaluable tool when formatting the data.
For my final visualization, I worked with a partner again to visualize 4 years worth blood glucose levels, sampled every 5 minutes, from an actual diabetic patient. We worked closely with a clinician and a father of a diabetic patient to construct a useful representation of the data.