2e:null 2f:["$","title",null,{"children":"Nuclear Accidents Visualized | Dynamic Data Visualization 373 | Observable"}] 34:T11b7,{ let w = 1000; let h = w / 2; let svg = d3 .create("svg") .attr("viewBox", [0, 0, w, h]) .attr("shape-rendering", "optimizeQuality"); let proj = d3.geoPatterson(); let path = d3.geoPath().projection(proj); let zoom = d3 .zoom() .on("zoom", (event) => { let scale = Math.max(1, event.transform.k); if (scale - 1 > 0.025) { scale = map.attr( "transform", `translate(${event.transform.x},${event.transform.y}) scale(${scale})` ); } }) .filter((event) => { let scale = d3.zoomTransform(svg.node()).k; if ( scale > 1.0001 || event.type === "drag" || (event.type === "wheel" && event.deltaY < 0) ) { return true; } scale = map.attr("transform", `translate(${0},${0}) scale(${1})`); return false; }); svg.call(zoom); let map = svg.append("g"); map.attr("transform", `translate(${-200},${0}) scale(${1.35})`); let country_group = map.append("g"); country_group .selectAll("path.country") .data(worldmap.features) .join("path") .attr("d", path) .attr("fill", "darkgray") .attr("stroke-width", "0.5") .attr("stroke", "#fff"); const sitesPrep = dataMatchedLocations; sitesPrep.forEach((o) => { let [x, y] = proj([o[0].Longitude, o[0].Latitude]); o.x = x; o.y = y; }); const sites = Array.from(sitesPrep, ([key, val]) => { val.name = key; return val; }).sort((a, b) => a[a.length - 1].Date > b[b.length - 1].Date); const maxAccidentAtSite = sites.reduce( (max, arr) => Math.max(max, arr.length), 0 ); let circle_group = map.append("g"); circle_group .selectAll("g") .data(sites) .join("g") .attr("transform", (d) => `translate(${d.x},${d.y})`) .each(function (d, i) { // create the static circle for each site const radius = lerpClamped( 1, 10, easeOutSine(d.length / maxAccidentAtSite) ); const avgINES = d.reduce((acc, o) => (acc += parseFloat(o.INES) ?? 0), 0) / d.reduce((acc, o) => (acc += parseFloat(o.INES) ? 1 : 0), 0); const totalCost = d.reduce( (acc, o) => (acc += parseFloat(o["Cost (millions 2013US$)"]) ?? 0), 0 ); const unreportedCosts = d.reduce( (acc, o) => (acc += parseFloat(o["Cost (millions 2013US$)"]) ? 0 : 1), 0 ); const costText = () => { if (isNaN(totalCost)) { return "Unreported"; } else { return `$${totalCost.toFixed(1)} million 2013USD ${ unreportedCosts ? `(${unreportedCosts} unreported)` : "" }`; } }; d3.select(this) .append("circle") .attr("class", "site") .attr("r", 0) .attr("fill", "#ff8a28") .attr("fill-opacity", 0.5) .attr("stroke-width", 0) .on("pointerenter", function () { d3.select(this) .attr("stroke", "#ff8a28") .attr("stroke-width", 0.5) .attr("fill", "#ff8a28"); }) .on("pointerleave", function () { d3.select(this) .attr("stroke", "#ff8a28") .attr("stroke-width", 0) .attr("fill", "black"); }) .each(function (d) { tippy(this, { theme: "dark-border", allowHTML: true, content: `

${d.name}

Total accident count: ${d.length}

Total cost: ${costText()}

Average INES: ${isNaN(avgINES) ? "Unreported" : avgINES}

` }); }) .transition() .delay((d) => lerp(100, 3000, easeOutSine(i / sites.length))) .duration(1000) .ease(d3.easeBounceOut) .attr("r", radius) .attr("fill", "black"); // create an animated pulse effect with a delay based on the index d3.select(this) .append("circle") .attr("class", "pulse") .attr("r", 0) .attr("fill", "none") .attr("stroke-width", 1.5) .attr("stroke", "black") .style("opacity", 1) .transition() .delay((d) => lerp(100, 3000, easeOutSine(i / sites.length))) .duration(1500) .ease(d3.easeLinear) .attr("r", radius * 6) .style("opacity", 0) .remove(); }); return svg.node(); }35:T4ea,{ const attrs = { "Cost (2013 USD)": { label: "Cost (millions 2013USD)", variable: "cost" }, Fatalities: { label: "Fatalities", variable: "fatalities" } }; const data = dataMatchedCountries .filter((o) => filter0s ? o[attrs[groupedCountryYVariable].variable] : true ) .sort( (a, b) => parseInt(a[attrs[groupedCountryYVariable].variable]) > parseInt(b[attrs[groupedCountryYVariable].variable]) ); const idMin = Math.min(entryIDMin, entryIDMax); const idMax = Math.max(entryIDMin, entryIDMax); const trimmed = data.slice(idMin, idMax); return Plot.plot({ width: width, autoWidth: true, height: 500, marginLeft: 50, marginRight: 50, marginBottom: 80, title: "My Chart Title", x: { ticks: 20, tickRotate: lerpClamped(0, -50, (trimmed.length - 10) / data.length) }, y: { transform: (d) => d, label: `↑ ${attrs[groupedCountryYVariable].label}`, grid: true }, marks: [ Plot.ruleY([0], {}), Plot.barY(trimmed, { x: "country", y: attrs[groupedCountryYVariable].variable, fill: "gray", stroke: "black", strokeWidth: 3, sort: { x: "y" } }) ] }); }36:T8a2,{ const inesCounts = d3 .rollups( rawData, (o) => o.length, (o) => o.INES ) .sort((a, b) => a[0] > b[0]) .slice(1); const total = inesCounts.reduce((a, o) => (a += o[1]), 0); let inesPercentages = inesCounts.map(([ines, count]) => ({ INES: parseInt(ines), count: count, percentage: (count / total) * 100 })); // inesPercentages = d3.sort(inesPercentages, (o) => o.INES); const w = 1000; const h = w / 10; const svg = d3.create("svg").attr("viewBox", [0, 0, w, h]); const x = d3.scaleLinear().domain([0, 100]).range([0, width]); const y = d3 .scaleBand() .domain(inesPercentages.map((d) => d.INES)) .range([0, h]) .padding(0.1); let currentPosition = 0; inesPercentages.forEach((d) => { svg .append("rect") .attr("x", x(currentPosition)) .attr("y", 0) .attr("width", x(d.percentage) - x(0)) .attr("height", h) .attr("fill", d3.interpolateSpectral(1 - d.INES / 7)) .attr("stroke", "black") .on("pointerenter", function () { const fill = d3 .color(d3.interpolateSpectral(1 - d.INES / 7)) .brighter(0.4); d3.select(this) .attr("fill", fill) // .attr("stroke-opacity", 0.5) // .attr("stroke-width", 5) .raise(); }) .on("pointerleave", function () { d3.select(this).attr("fill", d3.interpolateSpectral(1 - d.INES / 7)); }) .each(function () { tippy(this, { theme: "dark-border", allowHTML: true, content: `

INES: ${d.INES} ${ inesToDescription(d.INES).name }

Accident count: ${d.count}

Percentage: ${d.percentage.toFixed(2)}%

` }); }); currentPosition += d.percentage; }); svg .append("g") .attr("transform", `translate(0, ${h})`) .call(d3.axisBottom(x).tickFormat((d) => d + "%")); svg.append("g").call(d3.axisLeft(y).tickFormat((d) => `INES ${d}`)); return svg.node(); }37:T4c0,md` ## Conclusions Through working with this dataset and searching for similar datasets, it is clear that information on nuclear accidents is lacking. From just a quick glance of the dataset for ${rawData.length} accidents: - ${rawData.reduce( (a, o) => (a += parseInt(o.Fatalities) === 0), 0 )} have either 0 immediate fatalities or unreported data - ${rawData.reduce( (a, o) => (a += isNaN(parseInt(o["Cost (millions 2013US$)"]))), 0 )} accidents do not have a reported cost - ${rawData.reduce( (a, o) => (a += isNaN(parseInt(o.INES))), 0 )} accidents do not have a severity INES score - ${rawData.reduce( (a, o) => (a += isNaN(parseInt(o["Smyth Magnitude"]))), 0 )} accidents do not have a severity NAMS score. ^{Note: Due to only ${
rawData.length -
rawData.reduce((a, o) => (a += isNaN(parseInt(o["Smyth Magnitude"]))), 0)
} accidents having a Smyth Magnitude, it was omitted for this visualization.} Other notable improvements to the dataset would to be a casuality attribute to account for those injured directly from a nuclear accident. It is also difficult to determine if an fatality way later in life (i.e.: cancer, radiation sickness, etc) is accounted for in the data. `38:[{"id":"0889f71cd1fcf307dff6178414acfa6ebece0ab5ed6b68701718f674bb607b0928a86f9538d2d02ed4a8535cbde1b75492ced495f6ee197c0899d4733889ab0e","href":"https://static.observableusercontent.com/files/0889f71cd1fcf307dff6178414acfa6ebece0ab5ed6b68701718f674bb607b0928a86f9538d2d02ed4a8535cbde1b75492ced495f6ee197c0899d4733889ab0e","name":"worldmap.json","type":"application/json","lastModified":1682691633859.255,"size":270088},{"id":"d152b0a2bfd6874dbbd4a2beb05d63109076f1834a2a4d3a4b493248ee7b6df3e232f06eafd9e02c571769587d88544378a6646ebfaedaed0e35da4939257f90","href":"https://static.observableusercontent.com/files/d152b0a2bfd6874dbbd4a2beb05d63109076f1834a2a4d3a4b493248ee7b6df3e232f06eafd9e02c571769587d88544378a6646ebfaedaed0e35da4939257f90","name":"nuclear-power-accidents-2016-3.csv","type":"text/csv","lastModified":1682998674808.62,"size":42235}] 32:{"id":"647d8e10bbe535f2","slug":"nuclear-accidents-visualized","title":"Nuclear Accidents Visualized","thumbnail":"4c74233f4d91646dd5e01d4f88669f1743e089e25b871e56fdded5baa9e7ed59","access_level":"public","create_time":"$D2023-04-27T17:35:31.614Z","update_time":"$D2023-05-02T15:23:02.108Z","view_time":null,"first_public_time":"$D2023-05-02T15:22:15.579Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"a07c87acf1b22f86","login":"ddv373","name":"Dynamic Data Visualization 373","avatar_url":"https://avatars-next.observableusercontent.com/aa07fb823a47852e43ceca20a9030290c7b3befd6ad2b87a17c17f4b47176d35","tier":"pro_2024"},"version":1160,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"8d00c6fd9bdb9709","login":"maddhattpatt","name":"Patt","avatar_url":"https://avatars-next.observableusercontent.com/0cc0fdc72aa5b98f222be876103e97d3f4b440b5d5b766f1fba4d10e7cfd6413","tier":null}],"members":[{"type":"individual","id":"8d00c6fd9bdb9709","login":"maddhattpatt","name":"Patt","avatar_url":"https://avatars-next.observableusercontent.com/0cc0fdc72aa5b98f222be876103e97d3f4b440b5d5b766f1fba4d10e7cfd6413","tier":null,"role":"editor"}],"comments":[{"id":"616618d45523ce65","reply_to_id":null,"content":"Most Awesome! 20/20\n\nI love the circle animation effect on load!","create_time":"$D2023-05-02T18:44:17.210Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"c3301e515dd08a41","login":"mcmcclur","name":"Mark McClure","avatar_url":"https://avatars-next.observableusercontent.com/e615e819ce83d43faef607e48c53c5ff63943aa0ba9f5848499bd02fef7396f4","tier":null}}],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Nuclear Accidents Visualized","cells":[{"id":0,"value":"# Nuclear Accidents Visualized\n## By Patt Martin","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":377,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"^{The radius of each site corresponds with the accident count for each site.}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":760,"value":"### Map Controls\n- Scroll wheel to zoom\n- Left-click drag to pan\n- Hover over a site to get more information","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":934,"value":"## Description\nWith a dataset of 216 nuclear accidents from 2016 and prior, I wished to visualize the distribution of where nuclear accidents were more common and more severe.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":859,"value":"### Key Terms\n- INES: \"The International Nuclear and Radiological Event Scale (INES) is a tool for communicating the safety significance of nuclear and radiological events to the public.\" [[International Atomical Energy Agency]](https://www.iaea.org/resources/databases/international-nuclear-and-radiological-event-scale)\n\n 7\\. Major Accident \n 6\\. Serious Accident \n 5\\. Accident with Wider Consequences \n 4\\. Accident with Local Consequences \n 3\\. Serious Incident \n 2\\. Incident \n 1\\. Anomaly \n 0\\. Deviation\n\n- NAMS (Smyth Magnitude): An alternative to the INES system to account for the severity of Chernobyl Disaster that does not have a maximum limit. [[David Smythe - Nuclear Accidents]](https://www.davidsmythe.org/nuclear/accidents.htm) \n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":831,"value":"md`## ${groupedCountryYVariable} of Nuclear Accidents by Country`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":342,"value":"viewof entryIDMin = Inputs.range(\n [\n 0,\n dataMatchedCountries.filter((o) =>\n filter0s ? o[attrs[groupedCountryYVariable].variable] : true >= 0\n ).length - 2\n ],\n {\n step: 1,\n label: \"Country Lower cutoff\",\n value: 0\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":347,"value":"viewof entryIDMax = Inputs.range(\n [\n 1,\n dataMatchedCountries.filter((o) =>\n filter0s ? o[attrs[groupedCountryYVariable].variable] : true >= 0\n ).length\n ],\n {\n step: 1,\n label: \"Country upper cutoff\",\n value: dataMatchedCountries.filter((o) =>\n filter0s ? o[attrs[groupedCountryYVariable].variable] : true >= 0\n ).length\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":568,"value":"viewof filter0s = Inputs.toggle({\n label: \"Filter out 0 and unreported values\",\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"viewof groupedCountryYVariable = Inputs.select(\n [\"Cost (2013 USD)\", \"Fatalities\"],\n {\n value: \"Cost (2013 USD)\",\n label: \" Y Variable\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":843,"value":"md`## ${groupedCountryYVariable} of Nuclear Accidents by Region`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":562,"value":"{\n const data = dataMatchedRegions.sort(\n (a, b) =>\n parseInt(a[attrs[groupedCountryYVariable].variable]) >\n parseInt(b[attrs[groupedCountryYVariable].variable])\n );\n\n return Plot.plot({\n width: width,\n autoWidth: true,\n height: 500,\n marginLeft: 50,\n marginRight: 50,\n marginBottom: 80,\n x: {\n ticks: 20,\n tickRotate: lerpClamped(0, -50, (data.length - 10) / data.length)\n },\n y: {\n transform: (d) => d,\n label: `↑ ${attrs[groupedCountryYVariable].label}`,\n grid: true\n },\n marks: [\n Plot.ruleY([0]),\n Plot.barY(data, {\n x: \"country\",\n y: attrs[groupedCountryYVariable].variable,\n fill: \"gray\",\n stroke: \"black\",\n strokeWidth: 3,\n sort: { x: \"y\" }\n })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":925,"value":"## Percentage of ${knownINESCount} Nuclear Accidents's INES Scores\n^{${rawData.length - knownINESCount} accidents omitted due to unknown INES Score} ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1008,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1148,"value":"^{Hover over each bar more information}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":927,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":904,"value":"## Raw Dataset\nJournal: [Reassessing the safety of nuclear power](https://www.sciencedirect.com/science/article/pii/S2214629615301067) \nSource: https://innovwiki.ethz.ch/index.php/Nuclear_events_database \nAlternative: https://data.world/rebeccaclay/nuclear-power-accidents\n\nDataset licensed under the Creative Common License 4.0","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":895,"value":"rawData","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"#### Data Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"rawData = FileAttachment(\"nuclear-power-accidents-2016-3.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"worldmap = {\n let mapData = await FileAttachment(\"worldmap.json\").json();\n let countries = topojson.feature(\n mapData,\n mapData.objects.ne_10m_admin_0_countries\n );\n return countries;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"#### Utilities","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"function inesToDescription(num) {\n const descriptions = {\n 7: { name: \"Major accident\" },\n 6: { name: \"Serious accident\" },\n 5: { name: \"Accident with off-site risk\" },\n 4: { name: \"Accident without off-site risk\" },\n 3: { name: \"Serious incident\" },\n 2: { name: \"Incident\" },\n 1: { name: \"Anomaly\" },\n 0: { name: \"No safety significance\" }\n };\n const unknown = { name: \"Unknown status\" };\n\n return descriptions[num] ?? unknown;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"function regionToDescription(name) {\n const regions = {\n A: { name: \"Asia\" },\n EE: { name: \"Eastern Europe\" },\n NA: { name: \"North America\" },\n SA: { name: \"South America\" },\n WE: { name: \"Western Europe\" }\n };\n const unknown = { name: `Unknown Region ${name}` };\n\n return (regions[name] ?? unknown).name;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"function costFormatter(str) {\n if (str === \"\" || str === undefined) {\n return \"Unknown Cost\";\n }\n\n return `$${parseInt(str) * 1_000_000} (2013 USD)`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":363,"value":"function lerp(start, end, t) {\n return start * (1 - t) + end * t;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":515,"value":"function clamp(min, max, value) {\n return Math.min(Math.max(value, min), max);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":365,"value":"function lerpClamped(start, end, t) {\n t = clamp(0, 1, t);\n return lerp(start, end, t);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"function easeOutSine(x) {\n return Math.sin((x * Math.PI) / 2);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":389,"value":"d3 = require(\"d3\", \"d3-geo-projection\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":407,"value":"import { map } from \"@d3/world-map\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"tippy = require(\"tippy.js@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":470,"value":"tippy_style = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"### Processed Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":55,"value":"dataMatchedLocations = d3.group(rawData, (o) => o.Location)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"dataMatchedCountries = processCountries(rawData)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":554,"value":"dataMatchedRegions = processRegions(rawData)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"unknownCountryCostsData = dataMatchedCountries.filter((o) => o.cost === 0)\n .length","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1135,"value":"knownINESCount = {\n const inesCounts = d3\n .rollups(\n rawData,\n (o) => o.length,\n (o) => o.INES\n )\n .sort((a, b) => a[0] > b[0])\n .slice(1);\n\n const total = inesCounts.reduce((a, o) => (a += o[1]), 0);\n return total;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"function processCountries(data) {\n const grouped = d3.group(data, (o) => {\n const arr = o.Location.split(\",\");\n let name = arr[arr.length - 1].trim();\n\n if (name === \"Illinois\" || name === \"United States\") {\n name = \"USA\";\n }\n\n return name;\n });\n\n let output = [];\n const keys = [...grouped.keys()];\n keys.forEach((k) => {\n let o = {\n country: k,\n data: grouped.get(k),\n fatalities: grouped\n .get(k)\n .reduce((a, o) => a + parseInt(o[\"Fatalities\"] ?? 0), 0),\n cost: grouped\n .get(k)\n .reduce((a, o) => a + parseInt(o[\"Cost (millions 2013US$)\"]) || 0, 0),\n lat: grouped.get(k)[0].Latitude,\n lon: grouped.get(k)[0].Longitude\n };\n output.push(o);\n });\n\n return output;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"function processRegions(data) {\n const grouped = d3.group(data, (o) => o.Region);\n\n let output = [];\n const keys = [...grouped.keys()];\n keys.forEach((k) => {\n let o = {\n country: regionToDescription(k),\n data: grouped.get(k),\n fatalities: grouped\n .get(k)\n .reduce((a, o) => a + parseInt(o[\"Fatalities\"] ?? 0), 0),\n cost: grouped\n .get(k)\n .reduce((a, o) => a + parseInt(o[\"Cost (millions 2013US$)\"]) || 0, 0)\n };\n output.push(o);\n });\n\n return output;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":576,"value":"attrs = {\n return {\n \"Cost (2013 USD)\": { label: \"Cost (millions 2013USD)\", variable: \"cost\" },\n Fatalities: { label: \"Fatalities\", variable: \"fatalities\" }\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 33:"$32:body"