data analyst • mediapart
Intentions de vote dans les sondagesSuivi des sondagesDonnées candidatures au premier tour du Ministère de l'IntérieurDonnées des élections régionales 2021Données NspPollsCouleurs wikipedia des candidat·e·sRéseau d'alliances à partir des données du ministère de l'intérieurRégion, liste et âgeAlliances aux élections régionales 2021Régionales 2021 - Nuances - Matrice d'adjacence
Alliances de liste pour les élections régionales 2021 - arc diagram
current = radial.update(permutations[sorting]())
permutations = orders(graph)
orders = ({ nodes, links }) => {
const n = nodes.length;
const matrix = Array.from(nodes, (_, i) =>
d3.range(n).map(j => ({ x: j, y: i, z: 0 }))
);
const index = nodes.map((d, i) => ("id" in d ? d.id : i));
const l = [];
for (const node in nodes) nodes.count = 0;
links.forEach(link => {
const i = index.indexOf(link.source),
j = index.indexOf(link.target);
if (!("value" in link)) link.value = 1;
matrix[i][j].z += link.value;
matrix[j][i].z += link.value;
matrix[i][j].z += link.value;
matrix[j][i].z += link.value;
nodes[i].count += link.value;
nodes[j].count += link.value;
l.push({ source: i, target: j, value: link.value });
});
const adjacency = matrix.map(row => row.map(c => c.z));
const graph = reorder
.graph()
.nodes(nodes)
.links(l)
.init();
let dist_adjacency;
const leafOrder = reorder.optimal_leaf_order();
//.distance(science.stats.distance.manhattan);
function computeLeaforder() {
const order = leafOrder(adjacency);
order.forEach((lo, i) => (nodes[i].leafOrder = lo));
return nodes.map(n => n.leafOrder);
}
function computeLeaforderDist() {
if (!dist_adjacency) dist_adjacency = reorder.graph2valuemats(graph);
const order = reorder.valuemats_reorder(dist_adjacency, leafOrder);
order.forEach((lo, i) => (nodes[i].leafOrderDist = lo));
return nodes.map(n => n.leafOrderDist);
}
function computeBarycenter() {
const barycenter = reorder.barycenter_order(graph);
const improved = reorder.adjacent_exchange(graph, ...barycenter);
improved[0].forEach((lo, i) => (nodes[i].barycenter = lo));
return nodes.map(n => n.barycenter);
}
function computeRCM() {
const rcm = reorder.reverse_cuthill_mckee_order(graph);
rcm.forEach((lo, i) => (nodes[i].rcm = lo));
return nodes.map(n => n.rcm);
}
function computeSpectral() {
const spectral = reorder.spectral_order(graph);
spectral.forEach((lo, i) => (nodes[i].spectral = lo));
return nodes.map(n => n.spectral);
}
const orders = {
none: () => d3.range(n),
name: () =>
d3.range(n).sort((a, b) => d3.ascending(nodes[a].id, nodes[b].id)),
// count: () => d3.range(n).sort((a, b) => nodes[b].count - nodes[a].count),
// cluster: () =>
// d3
// .range(n)
// .sort(
// (a, b) =>
// d3.ascending(nodes[a].cluster, nodes[b].cluster) ||
// d3.ascending(nodes[a].name, nodes[b].name)
// ),
leafOrder: computeLeaforder,
leafOrderDist: computeLeaforderDist,
barycenter: computeBarycenter,
rcm: computeRCM,
spectral: computeSpectral
};
return orders;
}
opacity = (link) => {
return (Math.floor(link.value / 3) + 1) * 0.25
}
Inputs.table(nodes)
Inputs.table(links)
graph = ({ nodes, links })
import { nodes, links } from '@taniki/regionales-2021-data'
import { select } from "@jashkenas/inputs"
reorder = require('reorder.js@^2.0.2')
d3 = require('d3')
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
