2d:null 2e:["$","title",null,{"children":"BFMTV tweets présidentielles. | stéphane Wharton | Observable"}] 33:Tf95,bfm = Plot.line( defdatas, // compute the rolling average, show values in the brush selection Plot.windowY({ x: d => (!limits3 || d.date >= limits3[0] && d.date <= limits3[1]) ? d.date : NaN, y: d => d.occurrences.zemmour, //k: smooth2, k: 1, // fill: "lightgray", // stroke: "#111", }) ).plot({ x: { nice: true, label: "↑ date", domain: limits3 || undefined }, y: { grid: true, nice: true, label: "↑ Occurences journalières", transform: d => d }, marks: [ //Plot.ruleY([0]), [Plot.ruleY([0])], // draw a line at x=0 [Plot.ruleY([5])], // draw a line at x=0 //[Plot.ruleX(["2021-09-01"])], // draw a line at x=0 // ZEMMOUR Plot.line(defdatas, { x: d => d.date, y: zemmour_occurences , stroke: "#0000CC", strokeWidth:1, fill :"#0000CC", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.zemmour, // text: ["zemmour"], // fill: "#0000CC", // dy: "-0.5em" // }), // MACRON Plot.line(defdatas, { x: d => d.date, y: macron_occurences, stroke: "#ffeb00", strokeWidth:1, fill :"#ffeb00", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.macron, // text: ["macron"], // fill: "#ff4d89", // dy: "-0.5em" // }), // MELENCHON Plot.line(defdatas, { x: d => d.date, y: melenchon_occurences, stroke: "#cc2443", strokeWidth:1, fill :"#cc2443", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.bertrand, // text: ["macron"], // fill: "#FFFF00", // dy: "-0.5em" // }), // HIDALGO Plot.line(defdatas, { x: d => d.date, y: hidalgo_occurences, stroke: "#FF8080", strokeWidth:1, fill :"#FF8080", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.hidalgo, // text: ["macron"], // fill: "#ff4d89", // dy: "-0.5em" // }), // BERTRAND Plot.line(defdatas, { x: d => d.date, y: bertrand_occurences, stroke: "#0066CC", strokeWidth:1, fill :"#0066CC", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.bertrand, // text: ["macron"], // fill: "#FFF", // dy: "-0.5em" // }), // JADOT Plot.line(defdatas, { x: d => d.date, y: jadot_occurences, stroke: "#77ff77", strokeWidth:1, fill :"#77ff77", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.bertrand, // text: ["macron"], // fill: "#FFF", // dy: "-0.5em" // }), // LEPEN Plot.line(defdatas, { x: d => d.date, y: lepen_occurences, stroke: "#0D378A", strokeWidth:1, fill :"#0D378A", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.bertrand, // text: ["macron"], // fill: "#FFF", // dy: "-0.5em" // }), // MONTEBOURG Plot.line(defdatas, { x: d => d.date, y: montebourg_occurences, stroke: "#FF8080", strokeWidth:1, fill :"#FF8080", fillOpacity:0.5, curve }), // Plot.text(defdatas, { // x: "date", // y: d => d.occurrences.bertrand, // text: ["macron"], // fill: "#FFF", // dy: "-0.5em" // }), // END, stylize this shit... ], caption: md`Fréquence des occurences sur 30k tweets de @BFMTV - Un repère est posé à 5 tweets / jour...`, width, // height:600, style: { background: "#2A2A2A", color: "#FFF", padding:10, } })34:Te99,# Quelques `petits mots` **Il vaut mieux regarder cette expérience sur desktop vs mobile... ** Un gros #bigup à **@observablehq**. Cet outil est un vrai gouffre à temps tellement il est **puissant** et **passionnant**. Beaucoup de partages et une vision open... Ca faisait longtemps que je n'avais pas pris une claque comme celle ci avec un **tool**. On peur comparer ça à un **Notion** *pour les développeurs qui en veulent* - ça va vite, les logiques sont puissantes, on est vraiment focus sur ce qu'on veut exprimer - rien que ça ! Ma **idée première** pour tester cet outil est de **scraper des tweets de medias** et de poser des analyses basées sur des occurrences. Envie de savoir comment ils *twittent*, même si on le sait déjà a priori, mais on peut toujours avoir quelques surprises - et j'en ai eu. Pour faire vite, je me suis mis à **D3**. Je connaissais bien sûr, mais je n'avais trop jamais joué avec. Et puis je suis parti en mode 🥷. J'ai découvert [arquero](https://uwdata.github.io/), [natural](https://github.com/NaturalNode/natural), [compromise](https://github.com/spencermountain/compromise), la puissance de [Observablehq](https://observablehq.com/), et pleins d'autres trucs. De quoi se **perdre** pendant quelques jours. :) Pour gérer ce notebook, j'ai bien galéré, surtout sur la manipulation des objets, des tableaux... Je ne suis pas ingénieur, j'ai toujours eu du mal avec ça. / **Le worflow** 1. Je scrape tous les jours les TW grâce aux actions GH et l'excellent [Minet du Medialab de ScPo](https://github.com/medialab/minet). Le nom du [git](https://github.com/stefw/zemmourINmediastweets) n'est pas super, mais à la base je voulais focuser sur *zemmour*. 2. Je récupère donc les tweets de @bfmtv depuis le 01/09/2021 3. Ensuite je parse le fichier récupéré (il y a clairement trop d'infos, je n'ai pas **RTFM** la doc de **minet** pour récupérer uniquement ce donc j'ai besoin, c'est pas super **GREEN** tout ça, mais bon....). 4. Je prep les datas, et je les affiche dans une vue [Plot.line / Obervablehq](https://observablehq.com/@observablehq/plot-line) 5. J'ai vraiment eu du mal pour filter les occurrences sur le graph. Afficher telle ou telle data... Le "viewoff" de @obervablehq. Le code n'est pas propre à ce moment là.... Houuuuuuuu :) 6. L'inspiration sur les couleurs vient de [@taniki](https://observablehq.com/@taniki/regionales2021-color). Je n'ai pas trouvé Zemmour, je l'aurais bien mis en "**EXD**" mais comme j'ai un fond foncé, ça ne passe pas. Je l'ai donc mis sur un bleu "patate"- le travail qu'à fait Taniki concerne les régionales... 7. Un truc un peu *fou-fou*, la couleur **LAREM** est une jaune, un peu moins fluo que celui des **gilets jaunes**. **Encore d'autres datas, comme si on n'en avait pas assez :** - le tweet le + liké est [Le cercueil de Jean-Paul Belmondo sort sous les applaudissements et au son de "Chi Mai" du film "Le Professionnel"](https://twitter.com/BFMTV/status/1435986567605194760) - Le scrap des 30k tweets BFM s'étale sur 6 mois. Je vais me servir de arquero pour faire quelques calculs. Les biais de ce **notebook** : - Les couleurs. Je dois me poser là dessus... - La précision du comptage des occurrences. A ce stade je suis plutôt **crevé** et j'ai envie de passer à autre chose. Je n'arrive pas à gérer des "checkboxs" et à afficher des graphs par défaut... Bonne nuit. Un gros bisous à mon ami [@taniki](https://observablehq.com/@taniki) de @datagouv pour toute l'inspiration qu'il donne et le code qu'il ouvre depuis toutes ces années... **Next STEPS /** bien entendu, on compare plusieurs medias - Franceinfo - Cnews - TF1 - LeParisien - etc.... 35:T5a4,Inputs.table(defdatas, { columns: [ "occurrences"], //rows:100, format: { // PageName:x => x.toLowerCase(), date:x => htl.html`${x}`, //occurrences:x => x.zemmour, //AmountSpent: sparkbar(d3.max(fbads, d => d.AmountSpent)), occurrences: x => htl.html`

`, } }) //

zemmour ${x.zemmour}

macron ${x.macron}

36:T447,htl.html` Macron : ${d3.sum(defdatas_counts, d => d.macron)} tweets (${(d3.sum(defdatas_counts, d => d.macron)*100/total).toFixed(2)}%)
Zemmour : ${d3.sum(defdatas_counts, d => d.zemmour)} tweets (${(d3.sum(defdatas_counts, d => d.zemmour)*100/total).toFixed(2)}%)
Bertrand : ${d3.sum(defdatas_counts, d => d.bertrand)} tweets (${(d3.sum(defdatas_counts, d => d.bertrand)*100/total).toFixed(2)}%)
Lepen : ${d3.sum(defdatas_counts, d => d.lepen)} tweets (${(d3.sum(defdatas_counts, d => d.lepen)*100/total).toFixed(2)}%)
Hidalgo : ${d3.sum(defdatas_counts, d => d.hidalgo)} tweets (${(d3.sum(defdatas_counts, d => d.hidalgo)*100/total).toFixed(2)}%)
Jadot : ${d3.sum(defdatas_counts, d => d.jadot)} tweets (${(d3.sum(defdatas_counts, d => d.jadot)*100/total).toFixed(2)}%)
Montebourg : ${d3.sum(defdatas_counts, d => d.montebourg)} tweets (${(d3.sum(defdatas_counts, d => d.montebourg)*100/total).toFixed(2)}%)
Melenchon : ${d3.sum(defdatas_counts, d => d.melenchon)} tweets (${(d3.sum(defdatas_counts, d => d.melenchon)*100/total).toFixed(2)}%)
`37:[] 31:{"id":"f1d57cac3dcd13b3","slug":"bfmtv-tweets-presidentielles","title":"BFMTV tweets présidentielles.","thumbnail":"1b5142af7f8f03aadad2f71fca03957389ca02a1bedd4598cde733f58c257fc5","access_level":"public","create_time":"$D2021-10-28T19:15:11.083Z","update_time":"$D2021-12-03T20:19:13.695Z","view_time":null,"first_public_time":"$D2021-11-07T19:32:14.298Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"d7a54507b364837e","login":"stefw","name":"stéphane Wharton","avatar_url":"https://avatars-next.observableusercontent.com/2f2849a7b2cf81b942b2902c2a6930bf2b5fe71fff038936ef6893773610d1be","tier":"starter_2024"},"version":5025,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"437c79feb6205de2","login":"stefw","name":"stéphane Wharton","avatar_url":"https://avatars-next.observableusercontent.com/2f2849a7b2cf81b942b2902c2a6930bf2b5fe71fff038936ef6893773610d1be","tier":null}],"members":[{"type":"individual","id":"437c79feb6205de2","login":"stefw","name":"stéphane Wharton","avatar_url":"https://avatars-next.observableusercontent.com/2f2849a7b2cf81b942b2902c2a6930bf2b5fe71fff038936ef6893773610d1be","tier":null,"role":"editor"},{"type":"individual","id":"043b76eec3017fff","login":"taniki","name":"duong tam kien","avatar_url":"https://avatars-next.observableusercontent.com/c36ffc09c10b04541407cfdcb63a4b49932d84cfacabc2b0c2e3a26a19f4cfd8","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"fc2806c91d463617","slug":"twitter","title":"TwitterPOL","owner":{"id":"d7a54507b364837e","login":"stefw","name":"stéphane Wharton"}},"type":"notebook","body":{"title":"BFMTV tweets présidentielles.","cells":[{"id":0,"value":"# BFMTV `tweets` présidentielles.\n\nWIP\n\nOccurrences journalières des noms des candidats à l'élection présidentielle 2022, depuis le 1er septembre 2021\n\n\n-------- ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3330,"value":"swatches({\n color: d3.scaleOrdinal()\n .domain(Object.keys(candidateColors))\n .range(Object.values(candidateColors)),\n columns: '180px'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3442,"value":"viewof curve = Inputs.select([\n \"linear\",\n \"step\",\n \"step-after\",\n \"step-before\",\n // \"linear-closed\",\n \"basis\",\n // \"basis-closed\",\n // \"basis-open\",\n // \"bundle\",\n \"cardinal\",\n // \"cardinal-closed\",\n // \"cardinal-open\",\n \"catmull-rom\",\n // \"catmull-rom-closed\",\n // \"catmull-rom-open\",\n \"monotone-x\",\n // \"monotone-y\",\n \"bump-x\",\n // \"bump-y\",\n \"natural\",\n], {label: \"Curve\", value: \"step\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2995,"value":"viewof limits3 = brushFilterX([d3.utcYear.offset(d3.utcDay(), -1), d3.utcDay()], {\n defaultExtent: [ d3.utcParse(\"%Y-%m-%d\")(\"2021-08-30\"), d3.utcParse(\"%Y-%m-%d\")(\"2021-12-01\") ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4725,"value":"Fais ton choix ici... 👇","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4382,"value":"viewof watching = Inputs.checkbox(choix, {key: choix[0].location, value: x => x.name, label: \"🧐 Candidats\", format: x => x.name})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2965,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3302,"value":"-------------\n\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4691,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4496,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4481,"value":"# Prep `datas`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4387,"value":"macron_occurences = watching.reduce((amt, t) => t.name === \"Macron\" ? t.location : amt, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4433,"value":"zemmour_occurences = watching.reduce((amt, t) => t.name === \"Zemmour\" ? t.location : amt, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4436,"value":"hidalgo_occurences = watching.reduce((amt, t) => t.name === \"Hidalgo\" ? t.location : amt, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4438,"value":"bertrand_occurences = watching.reduce((amt, t) => t.name === \"Bertrand\" ? t.location : amt, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4434,"value":"melenchon_occurences = watching.reduce((amt, t) => t.name === \"Melenchon\" ? t.location : amt, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4511,"value":"jadot_occurences = watching.reduce((amt, t) => t.name === \"Jadot\" ? t.location : amt, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4521,"value":"lepen_occurences = watching.reduce((amt, t) => t.name === \"LePen\" ? t.location : amt, 0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4661,"value":"montebourg_occurences = watching.reduce((amt, t) => t.name === \"Montebourg\" ? t.location : amt, 0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4375,"value":"choix = [\n {name: \"Macron\", location: d => d.occurrences.macron},\n {name: \"Bertrand\", location: d => d.occurrences.bertrand},\n {name: \"Zemmour\", location: d => d.occurrences.zemmour},\n {name: \"LePen\", location: d => d.occurrences.lepen},\n {name: \"Hidalgo\", location: d => d.occurrences.hidalgo},\n {name: \"Jadot\", location: d => d.occurrences.jadot},\n {name: \"Melenchon\", location: d => d.occurrences.melenchon},\n {name: \"Montebourg\", location: d => d.occurrences.montebourg},\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4793,"value":"swatches({\n color: d3.scaleOrdinal()\n .domain(Object.keys(candidateColors))\n .range(Object.values(candidateColors)),\n columns: '180px'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4731,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4958,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4986,"value":"total = d3.sum(defdatas_counts, d => d.macron)+d3.sum(defdatas_counts, d => d.zemmour)+d3.sum(defdatas_counts, d => d.bertrand)+d3.sum(defdatas_counts, d => d.lepen)+d3.sum(defdatas_counts, d => d.hidalgo)+d3.sum(defdatas_counts, d => d.jadot)+d3.sum(defdatas_counts, d => d.montebourg)+d3.sum(defdatas_counts, d => d.melenchon)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4867,"value":"viewof defdatas_counts = aq // viewof shows the table view, but assigns the table value\n .from(defdatas)\n .select('date','occurrences')\n .derive({ macron: d => d.occurrences.macron })\n .derive({ zemmour: d => d.occurrences.zemmour })\n .derive({ lepen: d => d.occurrences.lepen })\n .derive({ bertrand: d => d.occurrences.bertrand })\n .derive({ hidalgo: d => d.occurrences.hidalgo })\n .derive({ jadot: d => d.occurrences.jadot })\n .derive({ montebourg: d => d.occurrences.montebourg })\n .derive({ melenchon: d => d.occurrences.melenchon })\n .derive({ total: d => d.occurrences.macron + d.occurrences.zemmour + d.occurrences.bertrand + d.occurrences.lepen+ d.occurrences.hidalgo + d.occurrences.jadot + d.occurrences.montebourg + d.occurrences.melenchon })\n .select(aq.not('occurrences'))\n .orderby(aq.desc('date'))\n .view(30000) // shorthand for view({ limit: 5 })\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3002,"value":"// ccc = d3.rollups(\n// cooldatas, \n// v => {return{zemmour:d3.sum(v, d => d.motsfinded.zemmour), macron:d3.sum(v, d => d.motsfinded.macron), melenchon:d3.sum(v, d => d.motsfinded.melenchon), hidalgo:d3.sum(v, d => d.motsfinded.hidalgo), bertrand:d3.sum(v, d => d.motsfinded.bertrand), jadot:d3.sum(v, d => d.motsfinded.jadot), lepen:d3.sum(v, d => d.motsfinded.marine)} },\n// d => {return d.date},\n// )\n\n// GOOD\ndefdatas = Array.from(d3.rollups(cooldatas, \n v => {return{zemmour:d3.sum(v, d => d.motsfinded.zemmour), macron:d3.sum(v, d => d.motsfinded.macron), melenchon:d3.sum(v, d => d.motsfinded.melenchon), hidalgo:d3.sum(v, d => d.motsfinded.hidalgo), bertrand:d3.sum(v, d => d.motsfinded.bertrand), jadot:d3.sum(v, d => d.motsfinded.jadot), lepen:d3.sum(v, d => d.motsfinded.marine), montebourg:d3.sum(v, d => d.motsfinded.montebourg) } },\n d => {return d.date},\n ), ([date, occurrences]) => ({date, occurrences}))\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":426,"value":"cooldatas = rawdatas.map((d) => {\n const date = d.local_time.substring(0, d.local_time.length-9).toLocaleString();\n const month = d.local_time.substring(0, d.local_time.length-12); \n const like_count = d.like_count;\n const motsfinded=[];\n\n // motsfinded[mots[i]] = countOccurences(d.text.normalize(\"NFD\").replace(/[\\u0300-\\u036f]/g, \"\").toLowerCase(),' '+mots[i]);\n\n for (let i = 0; i < mots.length; ++i) {\n motsfinded[mots[i].normalize(\"NFD\").replace(/[\\u0300-\\u036f]/g, \"\")] = countOccurences(d.text.toLowerCase(),' '+mots[i]);\n }\n return {\n date,\n month,\n motsfinded,\n like_count \n };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"rawdatas = d3.csv(\"https://raw.githubusercontent.com/stefw/mediastweets/main/bfmtv-tweets.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"// les mots clés à chercher\nmots = [\"zemmour\", \"macron\", \"bertrand\", \"mélenchon\", \"hidalgo\",\"montebourg\",\"jadot\",\"marine\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"// Je dois grave revoir cette fonction. C'est un peuu le coeur du truc car elle va chercher les occurences. J'aimerais bien avoir un pre traitement python, j'ai un peu de mal à faire ce que je veux en JS. Il faut faire attention aux \"espace\", aux occurences type @jeanlucmelenchon, à la présence des mots dans les urls etc...\n\nfunction countOccurences(string, word) {\n return string.split(word).length - 1;\n // return (string.match(/word/g) || []).length\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4563,"value":"# What we `need`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1551,"value":"import { Table } from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1153,"value":"import {brushFilterX} from \"@observablehq/brush-filter-x\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4494,"value":"candidateColors = ({\n MACRON: '#ffeb00',\n BERTRAND: '#0066CC',\n ZEMMOUR: '#0000CC',\n LEPEN: '#0D378A',\n MELENCHON: '#cc2443',\n MONTEBOURG: '#cc2443',\n HIDALGO: '#FF8080',\n JADOT: '#77ff77',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2900,"value":"import {swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4593,"value":"import { aq, op } from '@uwdata/arquero'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"d3 = require('d3-array@2','d3','d3-dsv@2')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3366,"value":"\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4671,"value":"# TBC 🎸","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4696,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 32:"$31:body"