data analyst • mediapart
import {graph as graphDraw} from '@d3/d3-hierarchy'
import { raw as candidates, lists } from '@taniki/regionales2021-donnees-minint'
graph = {
let nodes = [... new Set(candidates.map(c => c['Nuance candidat']))]
let pairs = (l) => {
let p = []
l.sort((a,b) => a.localeCompare(b))
l.forEach((vi,i)=>{
l.slice(i+1).forEach((vj,j)=>{
p.push([vi, vj])
})
})
return p
}
let links = lists
.map(l => l.candidatesNuances)
.map(pairs)
.flat()
.reduce((acc, p)=>{
let link = acc.find(l => l.source == p[0] && l.target == p[1])
if (!link) acc.push({ source: p[0], target: p[1], count:1 })
else link.count += 1
return acc
}, [])
return {nodes, links}
}
netClustering = require("netclustering")
communities = {
let n = graph.nodes.map(n => ({id: n}))
let l = graph.links.map(l=>({
'source': graph.nodes.indexOf(l.source),
'target': graph.nodes.indexOf(l.target),
'count': l.count,
}))
let clusters = netClustering.cluster(n, l, undefined, 'count')
return ({n, l, clusters})
}
import { candidateColors as nuanceColors } from '@taniki/regionales2021-colors'
color = d3.scaleOrdinal()
.domain(Object.keys(nuanceColors))
.range(Object.values(nuanceColors))
swatches({ color, columns: '180px' })
import { swatches } from '@d3/color-legend'
h = width
m = ({ t:50, b: 50, l: 50, r: 50 })
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.