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Exploring datasets with JavaScript and D3.js visualizations.
These projects showcase another tool for storytelling: D3.js. D3.js streamlines the process of transforming large, complex datasets into intelligible, graphical charts. This allows us to convert previously cryptic information into forms that are readily understandable for users.
D3.js
TypeScript
React
An illustration of why visualizing data is important. The summary statistics (regression line) can be the same, while the data distributions can be very different.
A contemporary recreation of the train schedule designed by Charles Ibry and published by Étienne-Jules Marey in his book on data visualizations: La Méthode Graphique Dans les Sciences Expérimentales et Principalement en Physiologie et en Médecine — brought to our attention in Edward Tufte's book The Visual Display of Quantitative Information. This version is the elucidation of the Long Island Railroad schedule.
This project is a visualization of the classic insertion sort algorithm, an O(n2) complexity method. Due to the challenges of sequencing and updating asynchronous operations, this implementation primarily uses callbacks. This approach ensures the animations of bar positions is accurately sequenced, which keeps the bars and the array they represent in sync with each other.
A plotting of the precipitous decline in subway ridership following the discovery of COVID-19 in New York City. This chart is a combination of a daily bar graph with pandemic-related touchstones, which furnish context to the variance in subway ridership.
A dual axis, time series chart plotting the daily temperature range and the corresponding cumulative precipitation for New York City.