I recently visited the Takashi Murakami exhibit at the Chicago Museum of Contemporary Art and was inspired to do a deeper diver into Kanye as an artist. The data was pulled via Sort Your Music. The individual charts were created through R’s ggplot2 library, and the entire graphic was laid out in Sketch.
I made the above chart as both an art project and an exercise in creating more aesthetic short charts. All short charts were made via Python’s matplotlib library and the overall visualization was made through Sketch.
R • d3 • Data Analysis • Data Visualization
A triple double is when a basketball averages 10 points, rebounds, and assists. The only player to accomplish that over the course of an NBA season was Oscar Robertson, roughly 50 years ago. Russell Westbrook, currently a point guard for the Oklahoma City Thunder, has a been close to that milestone all season — a feat that has been talked about greatly in the media this year.
I wanted to create an interactive dashboard that would allow users to explore and analyze Westbrook’s historic season. This intially entailed using R to query data off NBA’s API and generate a probabilistic model for Westbrook’s chances of averaging a triple-double. From there I used Javacsript’s d3 library to deliver an information rich, interactive interface.
The project can be found here, and will continue to live update through the rest of the NBA season. A screenshot is below.
About the Project
Comparallel is a project in conjunction with Northwestern’s Knight Lab.
Our team was tasked with making “open data” (e.g. the data the US Government freely releases) more accessible and actionable for journalists.
Personal Impact
My main impact was in the initial human-centered design research and product development. Being the only person on my team with a design-thinking background, I designed and conducted most user tests/interviews. That research was used by our team to design features that would resolve the tensions uncovered. At the tail end of the project, I developed the script to find sub-correlations in data (via R). Additionally, I and another teammate developed the actual concept in JavaScript.
Process
User Research
Our team first reached to journalists of varying levels of data literacy — from FiveThirtyEight journalists that do deeper data dives for their stories to local economic reporters that report basic economic indices.
We were able to map out a journey point and identify a clear tension point for newer journalists.
Design & Prototyping
Based on the tensions identified, we created an ideal journey that we wanted our product to afford.
Using that information we tested different ways to display data. First starting with a paper prototype to get initial formative feedback from users, then later moving to a static digital screen to get more UI feedback.
A second series of prototypes (via Tableau) were used to test interactive features with users before moving to development.
Final Design
Our final design was made via JavaScript’s d3 library in conjunction with R.
