31:[] 2f:{"title":"Laid off: 6 million jobs and counting","cells":[{"id":0,"value":"

Laid off: ${Math.floor(total/1000000)} million jobs and counting

","pinned":false,"hidden":false,"mode":"html"},{"id":313,"value":"

An open source project from Big Local News is collecting reports of large layoffs, temporary furloughs, closures and other job cuts. So far, it includes data from ${\n states\n} states and D.C. with potentially more than ${Math.floor(total/1000000)} million jobs affected.

","pinned":false,"hidden":false,"mode":"html"},{"id":1192,"value":"

* This is a work-in-progress. The data isn't yet complete in several states and isn't fully reliable in others (consider helping to improve it).

","pinned":false,"hidden":false,"mode":"html"},{"id":734,"value":"circleLegend({\n scale: d3.scaleSqrt(domain, range),\n tickValues,\n tickFormat: (d, i, g) =>\n i === g.length - 1 ? `${d.toLocaleString()} jobs` : d.toLocaleString(),\n marginRight: 120,\n stroke: color\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":989,"value":"viewof search = Inputs.search(data, {\n columns: [\"location\", \"company\"],\n placeholder: \"Search by location or company name\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":107,"value":"chart = Plot.plot({\n width: width,\n height: layoffsYears.objects().length * 50,\n grid: true,\n x: {\n axis: \"top\",\n label: null,\n tickSize: 0,\n tickRotate: width < 800 ? 90 : 0,\n domain: maxLayoffsPerYearByState.array(\"postal_code\")\n },\n y: {\n tickSize: 0\n },\n r: {\n type: \"sqrt\",\n domain,\n range\n },\n marks: [\n Plot.dot(layoffsByDateAndState, {\n y: \"date\",\n x: \"postal_code\",\n r: \"total\",\n stroke: color,\n strokeWidth: 0.9\n }),\n Plot.tip(\n layoffsByDateAndState,\n Plot.pointer({\n x: \"postal_code\",\n y: \"date\",\n title: (d) => `${d.total.toLocaleString()} jobs`\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1058,"value":"search.length < 500\n ? Inputs.table(search, {\n columns: [\"location\", \"postal_code\", \"effective_date\", \"company\", \"jobs\"]\n })\n : html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":704,"value":"html`Source: ${\n states\n} state governments and D.C., compiled by Big Local News`","pinned":false,"hidden":false,"mode":"ojs"},{"id":434,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":428,"value":"### Data and code","pinned":false,"hidden":false,"mode":"md"},{"id":1009,"value":"domain = [\n 0,\n Math.min(\n Math.max(\n 10000,\n search.length > 0 ? d3.max(layoffsByDateAndState, (d) => d.total) : 0\n ),\n 100000\n )\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1014,"value":"range = [0, width * 0.02]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1024,"value":"tickValues = [\n Math.round((domain[1] * 0.3 * 0.3) / 100) * 100,\n Math.round((domain[1] * 0.3) / 1000) * 1000,\n Math.round(domain[1] / 10000) * 10000\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":728,"value":"import { circleLegend } from \"@bayre/circle-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":37,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":747,"value":"color = \"#ef0056\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"url = \"https://raw.githubusercontent.com/biglocalnews/warn-github-flow/transformer/data/warn-transformer/processed/consolidated.csv\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"data = d3.csv(url, d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":942,"value":"layoffs = aq.from(search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"layoffsByDateAndState = search.length > 0\n ? layoffs\n .filter((d) => !(d.postal_code == \"NJ\" && d.jobs > 10000)) // nationwide United layoff\n .filter((d) => !(d.postal_code == \"OR\" && d.jobs > 10000))\n .derive({\n date: (d) =>\n d.effective_date\n ? op.datetime(op.year(d.effective_date), op.month(d.effective_date))\n : op.datetime(op.year(d.notice_date), op.month(d.notice_date))\n })\n .derive({\n month: (d) => op.month(d.date),\n year: (d) => op.year(d.date),\n jobs: (d) => op.greatest(d.jobs, 1)\n })\n .filter((d) => d.year > 2001)\n .groupby([\"year\", \"month\", \"date\", \"postal_code\"])\n .rollup({\n total: (d) => op.sum(d.jobs)\n })\n .orderby(aq.desc(\"total\"))\n : aq.from([])","pinned":false,"hidden":false,"mode":"ojs"},{"id":995,"value":"layoffsYears = search.length > 0\n ? layoffsByDateAndState\n .groupby([\"year\"])\n .rollup({\n total: (d) => op.sum(d.total)\n })\n .orderby(aq.desc(\"total\"))\n : aq.from([])","pinned":false,"hidden":false,"mode":"ojs"},{"id":90,"value":"layoffsState = search.length > 0\n ? layoffsByDateAndState\n .groupby([\"postal_code\"])\n .rollup({\n total: (d) => op.sum(d.total)\n })\n .orderby(aq.desc(\"total\"))\n : aq.from([])","pinned":false,"hidden":false,"mode":"ojs"},{"id":301,"value":"maxLayoffsPerYearByState = search.length > 0\n ? layoffsByDateAndState\n .groupby([\"postal_code\", \"year\"])\n .rollup({ total: op.sum(\"total\") })\n .groupby(\"postal_code\")\n .rollup({ median: op.max(\"total\") })\n .orderby(aq.desc(\"median\"))\n : aq.from([])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1178,"value":"total = aq\n .from(data)\n .rollup({\n total: (d) => op.sum(d.jobs)\n })\n .array(\"total\")[0]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1327,"value":"states = layoffsState\n .array(\"postal_code\")\n .filter((postal_code) => postal_code != \"DC\").length","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}