A list of the top 10 notebooks that were most liked by the Observable community.

Yong-Yeol Ahn

COVID-19 Fatality Rate

Kaho Cheung

Introducing D3 Render: Truly Declarative and Reusable D3

Lionel Radisson

Observable & creative coding

Ben Kuhn

Yuri Vishnevsky

Quarantine Now

Shirley Wu

Introduction to SVG and D3.js

Ricky Reusser

GPU Boids

Jeffrey Heer

UW Interactive Data Lab

Introducing Arquero

Aaron Williams

Racial demographic dot-density map

Jiazhe Wang

Introduction to AntV

Matt Dzugan

Generative Art using Wind Turbine data

Observable

2020 Year in Review

Top 10 Liked notebooks created by the community in 2020

Observable is committed to helping and contributing to social justice and equality. We will enable data exploration for these efforts. We value prioritizing marginalized people’s safety over privileged people’s comfort.  

Equality

Washington Post journalists explain how they used U.S. census data, d3.js and streams to generate dot-density maps and render them to canvas.

Staff pick: Racial demographic dot-density map

I remember this story! It was in my mind when I recently wrote these two notebooks: 
https://observablehq.com/@mcmcclur/trivariate-choropleth-for-racial-composition-in-the-us 
and 
https://observablehq.com/@mcmcclur/racial-distribution-in-nc

Mark McClure

Very dope! I really enjoy trivariate scales for projects like these. I'll be sure to try your approach when I get a chance.

dc-borders-topo.json

dc-census.ndjson