2d:null 2e:["$","title",null,{"children":"COVID-19 in NYC by Zip Code & Income | Dominic Toth | Observable"}] 33:T56e,geojson = { const agiFields = ['agi_1_lt25', 'agi_25_lt50', 'agi_50_lt75', 'agi_75_lt100', 'agi_100_lt200', 'agi_gte200']; const testDataByZip = raw_testing_data .reduce((acc, d) => Object.assign(acc, { [d.MODZCTA || d.modzcta]: d }), {}); const geometries = raw_topojson_data.objects.zipcode.geometries .map((geo) => { const { zipcode, population, agi_total, area } = geo.properties; const testData = testDataByZip[zipcode] || {}; const tested = testData.Total; const positive = testData.Positive; const sqmi = (area / 2.788e+7); const properties = Object.assign({ zipcode, sqmi, density: population / sqmi, tested, positive, positive_ratio: tested && positive / tested, positive_per_1k: tested && (positive * 1e3) / population, tested_per_1k: tested && (tested * 1e3) / population, }, geo.properties); let sum = 0; agiFields.forEach((key) => { sum += geo.properties[key] || 0; properties[`agi${key.match(/_[^_]+$/)}`] = agi_total && sum / agi_total; }); return Object.assign({}, geo, { properties }); }); const topojson_data = Object.assign({}, raw_topojson_data, { objects: { zipcode: { type: 'GeometryCollection', geometries } }, }); return topojson.feature(topojson_data, topojson_data.objects.zipcode); }34:T53e,set_infobox = (data) => { const infobox = document.getElementById('graphic-infobox'); if (!infobox) return; if (data) infobox.innerHTML = `

${data.zipcode}

${new RegExp(`${data.borough}$`).test(data.neighborhood) ? data.neighborhood || data.po_name : `${data.neighborhood}, ${data.borough}`}

Population	${format_int(data.population)} (${format_int(data.density)} people per sq mi)
Tests	${format_int(data.tested \|\| 0)}${data.tested && ` (${format_int(data.tested_per_1k)} of every 1k residents)` \|\| ''}
Positives	${format_int(data.positive \|\| 0)}${data.positive && ` (${format_percent(data.positive_ratio)}, ${format_int(data.positive_per_1k)} of every 1k residents)` \|\| ''}
Income	${data.agi_total && ` ${[25, 50, 75, 100, 200] .map(agi => [`$${agi}k`, format_percent(data[`agi_lt${agi}`])]) .map(([key, value]) => ` ${value} made under ${key} in 2017 `) .join('')} ` \|\| '—' }

`; else infobox.innerHTML = ''; }35:T1dda,graphic = { const svg = d3.select(DOM.svg(width, height)); const chart = svg.append('g') .attr('transform', `translate(${chart_margin.left}, ${chart_margin.top})`); const axes = chart.append('g').attr('class', 'axes'); let lastXFormat = '.0%'; let lastYFormat = '.0%'; let xAxis = axes.append('g').call(x_axis(lastXFormat)); let yAxis = axes.append('g').call(y_axis(lastYFormat)); const xLabel = chart.append('text') .attr('class', 'direction-label direction-label-x') .attr('x', chart_width / 2) .attr('y', chart_height + 5); const yLabel = chart.append('text') .attr('class', 'direction-label direction-label-y') .attr('x', -chart_height / 2) .attr('y', 6) .attr('dy', '-2em') .attr('transform', 'rotate(-90)'); const cells = chart .append('g') .attr('class', 'cells') .selectAll('g') .data(voronoi(data).polygons(), d => d && d.data && d.data.zipcode) .join('g') .attr('class', 'cell'); const circles = cells.append('circle'); const labels = cells .append('text') .attr('class', 'label') .text(d => d.data.zipcode); const line = chart.append('line') .attr('class', 'regression') .datum(linear_regression(data)); const map = svg.append('g') .attr('transform', `translate(${(width * 0.18) - map_margin.right}, ${map_margin.top})`); const tracts = map .selectAll('g') .data(geojson.features) .join('g') .attr('class', 'tract'); const geoPath = d3.geoPath(projection); const polygons = tracts .append('path') .attr('class', 'polygon') .attr('fill', d => color(d.properties[encoding_defaults.color])) .attr('d', geoPath); tracts .append('text') .attr('class', 'label') .text(d => d.properties.zipcode) .attr('x', d => geoPath.centroid(d)[0]) .attr('y', d => geoPath.bounds(d)[0][1] - 3); let mouseoutTimeout; [[cells, d => d.data], [tracts, d => d.properties]] .forEach(([els, accessor]) => { els .on('mouseover', (data) => { const zip = accessor(data); // if (zip.population) { if (zip.tested) { clearTimeout(mouseoutTimeout); mutable active_zip = zip; } }) .on('mouseout', () => { mouseoutTimeout = setTimeout(() => { mutable active_zip = undefined; }, 100); }); }); return { svg, rendered: false, update(encoding, duration = 300) { const t = svg.transition().duration(this.rendered ? duration : 0); x.domain(d3.extent(data, d => d[encoding.x])); y.domain(d3.extent(data, d => d[encoding.y])); color.domain(d3.extent(data, d => d[encoding.c])); size.domain(d3.extent(data, d => d[encoding.s])); voronoi.x(d => x(d[encoding.x])).y(d => y(d[encoding.y])); linear_regression.x(d => d[encoding.x]).y(d => d[encoding.y]); const regression = linear_regression(data); const agi = (Object.values(encoding).find(k => /^agi/.test(k)) || '').replace('agi_lt', ''); const [ [xFormat, xDesc, xLabelText], [yFormat, yDesc, yLabelText], [cFormat, cDesc], [sFormat, sDesc], ] = ['x', 'y', 'c', 's'].map((k) => { const def = encoding_options.find(o => o.value === encoding[k]) || {}; return [ def.format || '.0%', def.suffix || `made under $${agi}k in 2017`, def.right || 'Less wealthy', ]; }); const colorFormat = (d, i, e) => d3.format(cFormat)(d) + (i === e.length - 1 ? ` ${cDesc}` : ''); const sizeFormat = (d, i, e) => d3.format(sFormat)(d) + (i === e.length - 1 ? ` ${sDesc}` : ''); const appendDesc = (axis, selection, desc) => { axis.select('.axis-desc').remove(); return axis.select(selection) .append('text') .attr('class', 'axis-desc') .attr('fill', 'currentColor') .text(desc); }; const appendXDesc = () => appendDesc(xAxis, '.tick:first-of-type', xDesc) .attr('y', '2.3em'); const appendYDesc = () => appendDesc(yAxis, '.tick:last-of-type', yDesc) .attr('x', '-3') .attr('y', '1.7em'); // Cross-fade axis if format changes, otherwise slide if (xFormat === lastXFormat) { xAxis.select('.axis-desc').text(xDesc); xAxis.transition(t).call(x_axis(xFormat)).on('end', () => setTimeout(appendXDesc, 0)); } else { xAxis.transition(t).attr('opacity', 0).remove(); xAxis = axes.append('g').attr('opacity', 0).call(x_axis(xFormat)); appendXDesc(); xAxis.transition(t).attr('opacity', 1); } if (yFormat === lastYFormat) { yAxis.select('.axis-desc').text(yDesc); yAxis.transition(t).call(y_axis(yFormat)).on('end', () => setTimeout(appendYDesc, 0)); } else { yAxis.transition(t).attr('opacity', 0).remove(); yAxis = axes.append('g').attr('opacity', 0).call(y_axis(yFormat)); appendYDesc(); yAxis.transition(t).attr('opacity', 1); } lastXFormat = xFormat; lastYFormat = yFormat; xLabel.text(`${xLabelText} ⟶`); yLabel.text(`${yLabelText} ⟶`); cells .data(voronoi(data).polygons(), d => d && d.data && d.data.zipcode) .join('g'); cells.selectAll('path').remove(); cells .append('path') // .attr('stroke', 'red') .attr('d', d => d && d.join && `M${d.join('L')}Z`); labels .transition(t) .attr('x', d => d && d.data && x(d.data[encoding.x])) .attr('y', d => d && d.data && y(d.data[encoding.y])); circles .transition(t) .attr('cx', d => d && d.data && x(d.data[encoding.x])) .attr('cy', d => d && d.data && y(d.data[encoding.y])) .attr('r', d => d && d.data && size(d.data[encoding.s])) .attr('color', d => d && d.data && color(d.data[encoding.c])); line .datum(regression) .transition(t) .attr('x1', d => x(d[0][0])) .attr('x2', d => x(d[1][0])) .attr('y1', d => y(d[0][1])) .attr('y2', d => y(d[1][1])); polygons .transition(t) .attr('fill', d => color(d.properties[encoding.c])); mutable legend_bar = html`

${legend({ color, tickFormat: colorFormat, width: 280 })}

${d3.select(DOM.svg(210, 40)) .attr('class', 'legend-circle') .append('g') .call(legendCircle() .tickValues(d3.extent(data, d => d[encoding.s])) .tickSize(20) .tickFormat(sizeFormat) .scale(size)).node().parentNode }

`; mutable regression_desc = md`Line of best fit: ${tex`y = ${regression.a.toFixed(3)}x ${regression.b > 0 ? '+' : '-'} ${Math.abs(regression.b.toFixed(3))}`} ${tex`R^2 = ${regression.rSquared.toFixed(3)}`}`; this.rendered = true; }, select(datum) { tracts.attr('class', 'tract'); cells.attr('class', 'cell'); if (datum) { [[cells, 'cell', x => x.data.zipcode], [tracts, 'tract', x => x.properties.zipcode]] .forEach(([els, className, accessor]) => els .filter(d => accessor(d) === datum.zipcode) .attr('class', `${className} active`) .raise() .filter((_, i) => !i) .attr('class', `${className} active active-label`)); } set_infobox(datum); }, }; }36:Tb5c,style = html``37:[{"id":"b219f76bd60919ce98f833c2f3f7e73ca49be413466e75771749cb62a4db8e627ab9a6084db3c373f5f94e07c313f2ce1cd38ca5ff6df66353d68a033f212abb","href":"https://static.observableusercontent.com/files/b219f76bd60919ce98f833c2f3f7e73ca49be413466e75771749cb62a4db8e627ab9a6084db3c373f5f94e07c313f2ce1cd38ca5ff6df66353d68a033f212abb","name":"nyc-zip-data-topo.json","type":"application/json","lastModified":1603893181124.504,"size":428482}] 31:{"id":"c14064f882f98636","slug":"covid-19-in-nyc-by-zip-code-income","title":"COVID-19 in NYC by Zip Code & Income","thumbnail":"23f43edb8d2bbcfc25ea288b65ea2f13987ea813eecf44cc23f60a344c5ec5d0","access_level":"public","create_time":"$D2020-10-28T13:53:01.124Z","update_time":"$D2020-10-28T13:53:01.124Z","view_time":null,"first_public_time":"$D2020-10-28T13:53:43.288Z","has_unpublished_edits":null,"fork":{"id":"2a31bc4d2914b05b","slug":"covid-19-in-nyc-by-zip-code-income","title":"COVID-19 in NYC by Zip Code & Income","owner":{"id":"71e001d943b9a4ca","login":"autopaideia","name":"Nick Bosman"}},"stars":0,"starred":false,"owner":{"type":"team","id":"6a65839ad407ce2b","login":"domwom","name":"Dominic Toth","avatar_url":"https://avatars-next.observableusercontent.com/07f2b1de208afada26655e2de4d36cd379c8d42f2c3faaac94d7b058d1fd957e","tier":"starter_2024"},"version":2952,"publish_version":2952,"role":null,"authored":false,"authors":[{"type":"individual","id":"583b7bcb9f7e5807","login":"domwom","name":"Dominic Toth","avatar_url":"https://avatars-next.observableusercontent.com/07f2b1de208afada26655e2de4d36cd379c8d42f2c3faaac94d7b058d1fd957e","tier":null}],"members":[{"type":"individual","id":"583b7bcb9f7e5807","login":"domwom","name":"Dominic Toth","avatar_url":"https://avatars-next.observableusercontent.com/07f2b1de208afada26655e2de4d36cd379c8d42f2c3faaac94d7b058d1fd957e","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"COVID-19 in NYC by Zip Code & Income","cells":[{"id":0,"value":"md`# COVID-19 in NYC by Zip Code & Income\n\nCompare testing and positive rates in NYC with income levels across zip codes. Each bubble represents a zip code, mouseover them to view details and locate on the map (or likewise, mouseover them on the map to locate on the chart).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":399,"value":"viewof options = {\n const encodings = Object.keys(encoding_defaults).map(k => [\n k.charAt(0).toUpperCase() + k.slice(1),\n encoding_defaults[k].match(/^agi/) ? 'income' : encoding_defaults[k],\n ]);\n\n const agiOptions = [25, 50, 75, 100, 200];\n\n const fields = encodings.map(([title, value]) => select({ title, value, options: encoding_options }));\n const keys = encodings.map(([title]) => title.toLowerCase()[0]);\n\n fields.push(slider({\n title: 'Income Threshold',\n min: 0,\n max: agiOptions.length - 1,\n step: 1,\n value: 2,\n format: i => `Under $${i}k/yr`,\n getValue: i => agiOptions[i.value],\n }));\n\n keys.push('agi');\n\n return inputsGroup([fields], [keys]);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2151,"value":"mutable legend_bar = undefined","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1629,"value":"html`

\n ${graphic.svg.node()}\n

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2927,"value":"mutable regression_desc = undefined","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2540,"value":"md`## Sources\n\n* Map and population data from [NYC OpenData](https://data.cityofnewyork.us/Business/Zip-Code-Boundaries/i8iw-xf4u).\n* Income data from [the IRS](https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-2017-zip-code-data-soi).\n* Zip code naming from [United Hospital Fund](https://www.health.ny.gov/statistics/cancer/registry/appendix/neighborhoods.htm).\n* (Live updating) COVID testing data from [NYC Department of Health](https://github.com/nychealth/coronavirus-data/blob/master/tests-by-zcta.csv).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2561,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"raw_testing_data = d3.csv('https://raw.githubusercontent.com/nychealth/coronavirus-data/master/tests-by-zcta.csv', d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":444,"value":"raw_topojson_data = {\n const data = await FileAttachment('nyc-zip-data-topo.json').json();\n\n // Merge 11249 into 11211 as apparently neither the tax nor testing\n // datasets use their zip code, which as far as I can tell was\n // split off from 11211 in 2011:\n // https://about.usps.com/news/state-releases/ny/2011/ny_2011_0603.htm\n const zip11249 = data.objects.zipcode.geometries.find(geo => geo.properties.zipcode === 11249);\n const zip11211 = data.objects.zipcode.geometries.find(geo => geo.properties.zipcode === 11211);\n zip11211.properties.population += zip11249.properties.population;\n zip11211.properties.neighborhood = 'Williamsburg and Greenpoint';\n Object.assign(zip11249.properties, zip11211.properties);\n\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":932,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1781,"value":"// Use only unique zip codes with both testing and income data\ndata = d3.groups(geojson.features, d => d.properties.zipcode)\n .map(([, [{ properties }]]) => properties)\n .filter(d => d.tested && d.agi_total)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2553,"value":"md`## Implementation`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"height = Math.round(width / 1.618)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":912,"value":"chart_width = Math.round(width * 0.58)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":909,"value":"chart_height = Math.round(chart_width / 1.618)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"chart_margin = ({ top: 20, right: 0, bottom: 0, left: 30 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2566,"value":"map_margin = ({ top: 14, right: 0, bottom: 2, left: 0 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1446,"value":"encoding_options = [\n { label: 'Income', value: 'income', format: '.0%', suffix: 'income', right: 'Less wealthy' },\n { label: 'Population', value: 'population', format: '.0s', suffix: 'residents', right: 'More people' },\n { label: 'Population density', value: 'density', format: '.0s', suffix: 'people per sq mi', right: 'More dense' },\n { label: 'Positives (total)', value: 'positive', format: ',d', suffix: 'total positives', right: 'More positives' },\n { label: 'Positives relative to population', value: 'positive_per_1k', format: 'd', suffix: 'positives per 1k residents', right: 'More infected' },\n { label: 'Positives relative to tests', value: 'positive_ratio', format: '.0%', suffix: 'of tests came back positive', right: 'More infected' },\n { label: 'Tests (total)', value: 'tested', format: ',d', suffix: 'total tests', right: 'More tests' },\n { label: 'Tests relative to population', value: 'tested_per_1k', format: 'd', suffix: 'tests per 1k residents', right: 'More tested' },\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1586,"value":"encoding_defaults = ({\n x: 'agi_lt25',\n y: 'positive_ratio',\n color: 'positive_ratio',\n size: 'population',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":282,"value":"encoding = ['x', 'y', 'c', 's']\n .map(key => [key, options[key] === 'income' ? `agi_lt${options.agi}` : options[key]])\n .reduce((acc, [k, v]) => Object.assign(acc, { [k]: v }), {})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"x = d3.scaleLinear()\n .range([0, chart_width - chart_margin.left - chart_margin.right])\n .domain(d3.extent(data, d => d[encoding_defaults.x]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"y = d3.scaleLinear()\n .range([chart_height - chart_margin.top - chart_margin.bottom, 0])\n .domain(d3.extent(data, d => d[encoding_defaults.y]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"color = d3.scaleSequential(d3.interpolateOrRd)\n .domain(d3.extent(data, d => d[encoding_defaults.color]))\n .unknown('#eee')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"size = d3.scaleSqrt()\n .range([chart_width * 0.008, chart_width * 0.02])\n .domain(d3.extent(data, d => d[encoding_defaults.size]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"linear_regression = d3.regressionLinear()\n .x(d => d[encoding_defaults.x])\n .y(d => d[encoding_defaults.y])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"voronoi = d3.voronoi()\n .extent([\n [-chart_margin.left, -chart_margin.top],\n [chart_width + chart_margin.right, chart_height + chart_margin.bottom],\n ])\n .x(d => x(d[encoding_defaults.x]))\n .y(d => y(d[encoding_defaults.y]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"x_axis = format => g => g\n .attr('transform', `translate(0, ${chart_height - chart_margin.top - chart_margin.bottom})`)\n .attr('class', 'axis x')\n .call(d3.axisBottom(x).ticks(5, format).tickSize(-chart_height))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"y_axis = format => g => g\n .attr('class', 'axis y')\n .call(d3.axisLeft(y).ticks(5, format).tickSize(-chart_width))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"format_percent = d3.format('.2%')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"format_int = d3.format(',d')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":940,"value":"projection = d3.geoIdentity()\n .reflectY(true)\n .fitSize([width, height - map_margin.top - map_margin.bottom], geojson)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1654,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1142,"value":"mutable active_zip = undefined","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":460,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1281,"value":"graphic.update(encoding)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1745,"value":"graphic.select(active_zip)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2557,"value":"md`## Dependencies`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"d3 = require('d3@5', 'd3-array@2', 'd3-regression@1')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":465,"value":"topojson = require('topojson-client@3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"import { select, slider } from '@jashkenas/inputs'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":396,"value":"import { inputsGroup } from '@bumbeishvili/input-groups'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":461,"value":"import { legend } from '@d3/color-legend'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2075,"value":"import { legendCircle } from '@harrystevens/circle-legend';","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 32:"$31:body"