Gráfico de Muertes por COVID-19

2f:T5ce,---------------- ## Intro Carbon dioxide traps atmosphere heat and contributes to global warming, sea level rise, ocean acidification and pollution. We will explore the historical accumulation of CO₂ emissions in Europe since 1969, when industry 3.0 started until the emergence of the aforementioned virus that paralyzed non-essential industries, so it would be possible to us to get a regional-specific analysis. CO₂ represents a severe climate problem that makes it necessary to understand long-term trends and contributions, as well as the challenges and opportunities for reducing them. Despite of the current policies and initiatives that aim to mitigate this problem at local, since the pandemic hit, some european countries kept the levels of greenhouse gas emissions below what they were before COVID-19 but is well known also that a significant amount of countries increased them otherwise. A)**The question we intend to answer is: if the coronavirus outbreak had not existed to date, what would be the current metrics of carbon dioxide in the countries of the continent?** B) **The question we intend to answer is which countries in Europe decreased carbon dioxide emissions following the coronavirus outbreak and which countries increased it?** ### Project's Focus Assess the impact of COVID-19 quarantine on various CO2 emissions in Europe between 2019 and 2021, by measuring total emission variables provided in a dataset and deriving insights from these findings. 30:T581,worldmap = { const svg = d3 .create("svg") .style("display", "block") .attr("viewBox", [0, 0, width, height]) .attr("transform", "translate(0, 20) scale(1.15)"); // move down, make bigger const defs = svg.append("defs"); // set up outline, clipping and background of map defs.append("path").attr("id", "outline").attr("d", path(outline)); defs .append("clipPath") .attr("id", "clip") .append("use") .attr("xlink:href", new URL("#outline", location)); const g = svg .append("g") .attr("clip-path", `url(${new URL("#clip", location)})`); g.append("use") .attr("xlink:href", new URL("#outline", location)) .attr("fill", "white"); // fill entities according to values g.append("g") .selectAll("path") .data(countries.features) .join("path") .filter(function (d) { return d.properties.name != "Antarctica"; }) .attr("fill", (d) => color(yearData.get(d.properties.name))) .attr("d", path) // tooltip .append("title") .text( (d) => `${d.properties.name} ${yearData.has(d.properties.name) ? yearData.get(d.properties.name) : "N/A"}` ); // draw borders g.append("path") .datum(topojson.mesh(world, world.objects.countries, (a, b) => a !== b)) .attr("fill", "none") .attr("stroke", "white") .attr("stroke-linejoin", "round") .attr("d", path); return svg.node(); }31:T4be, Gráfico de Muertes por COVID-19

32:[] 2d:{"title":"Europe's COVID-19 Impact on CO₂ Emissions","cells":[{"id":0,"value":"# Europe's COVID-19 Impact on CO₂ Emissions\n### For better or for worse","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":904,"value":"## Figure 1. World Wide CO₂ Emissions","pinned":false,"hidden":false,"mode":"md"},{"id":611,"value":"industrialization process changed its nature and economic growth changed its model (in this period, new energy techniques such as gas or electricity, new materials such as steel and oil and new transportation systems were introduced, as a consequence of the globalization of the market)\n\n temporal analysis of Industry 3.0 and its impact on climate change. Industry 3.0 is characterized by the use of automation and computing in production processes. It is considered that this stage began in the late 1950s and extended to the present day. During this period, several experts warned about the harmful effects of greenhouse gases on the climate, such as rising temperatures, melting ice at the poles, and disruptions to ecosystems.","pinned":false,"hidden":false,"mode":"md"},{"id":669,"value":"viewof date1 = Scrubber(years1, {\n delay: 724,\n loopDelay: 2000,\n autoplay: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":735,"value":"md`In ${date1}, the average CO₂ emissions worldwide was **${yearMean}\\**.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":702,"value":"viewof colorInterpolator = {\n const picker = colorInterpolatorPicker({\n title: \"Color scheme\",\n value: \"YlPu\",\n w: 200,\n interpolators: [\n {\n // Sequential multi-hue\n name: \"YlPu\",\n value: d3.interpolateHcl(\"#f4e153\", \"#362142\")\n },\n {\n // Sequential single hue\n name: \"Orange\",\n value: d3.interpolateHslLong(\"#fee6ce\", \"#7f2704\")\n },\n {\n // Diverging\n name: \"Diverging\",\n value: d3.interpolateRgbBasis([\n \"rgb(54, 99, 170)\",\n \"rgb(112, 165, 196)\",\n \"rgb(232, 222, 192)\",\n \"rgb(238, 156, 100)\",\n \"rgb(209, 94, 66)\",\n \"rgb(197, 46, 43)\",\n \"#a50026\"\n ])\n }\n ]\n });\n return picker;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":745,"value":"legend({ color, title: \"CO₂ Emissions\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":726,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":915,"value":"## Figure 2. The most CO2 countries","pinned":false,"hidden":false,"mode":"md"},{"id":803,"value":"viewof co2_filter = Inputs.select(Object.keys(co2_fields), {\n value: \"co2\",\n label: \"Pick a co2 filter:\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":854,"value":"#### Top 5 most ${co2_filter} countries: ${most_european_total_co2_country_top5}","pinned":false,"hidden":false,"mode":"md"},{"id":805,"value":"Plot.plot({\n width: 1250, \n inset: 7,\n color: {\n legend: true,\n scheme: \"ylorrd\",\n },\n y: {\n grid: true,\n },\n marks: [\n Plot.frame(),\n Plot.areaY(most_european_total_co2_country_data.filter(d => d.year > 2018 && d.year <= 2022), {\n x: \"year\",\n y: co2_filter,\n fx: \"country\",\n stroke: \"population\",\n z: null,\n fill: \"population\",\n sort: {fx: \"-y\"}\n }),\n Plot.line(most_european_total_co2_country_data.filter(d => d.year > 2018 && d.year <= 2022), {x: \"year\", y: co2_filter, fx: \"country\", marker: true, tip: true}),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1122,"value":"$31","pinned":true,"hidden":false,"mode":"html"},{"id":1074,"value":"Plot.plot({\n title:'Top 10 Countries with Highest CO2 Emissions before and during the pandemic)',\n width: 1000,\n marks: [\n Plot.ruleY([0]),\n Plot.barY(most_european_total_co2_country_data.filter(d => d.year > 2018 && d.year <= 2020), {\n x: \"year\",\n y: \"co2\",\n fx: \"country\",\n stroke: \"population\",\n fill: \"population\",\n labels:{\"year\": \"CO2 Emissions (tons)\"},\n sort: {x: \"x\", fx: \"-y\"}}), \n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1114,"value":"viewof co2_filter_fields = Inputs.select(Object.keys(co2_fields), {\n value: \"co2\",\n label: \"Selection:\"\n})\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":1117,"value":"Plot.plot({\n y: {\n grid: true,\n },\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(most_european_total_co2_country_data.filter(d => d.year > 2018 && d.year <= 2020), {x: \"year\", y: co2_filter_fields, z: \"country\", stroke: \"population\", marker: true, tip: true})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":493,"value":"## Figure 3. Heat map for countries","pinned":false,"hidden":false,"mode":"md"},{"id":464,"value":"viewof countrySelected = Inputs.select(Object.keys(european_co2), {\n label: \"Pick a country\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":995,"value":"viewof fields = Inputs.checkbox(Object.keys(country_co2[0]), {\n label: \"Select fields:\",\n value: co2_fields_country\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":501,"value":"Plot.plot({\n width: 1250,\n marginLeft: 100,\n label: null,\n color: { scheme: \"rdylbu\", pivot: 0, legend: true, label: \"correlation\" },\n marks: [\n Plot.cell(correlations, { x: \"a\", y: \"b\", fill: \"correlation\" }),\n\n Plot.text(correlations, {\n x: \"a\",\n y: \"b\",\n text: (d) => d.correlation.toFixed(2),\n fill: (d) => (Math.abs(d.correlation) > 0.6 ? \"white\" : \"black\")\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1168,"value":"\n\n\n Top 10 European Countries with Highest CO2 Emissions in 2019\n \n\n\n

\n \n\n\n\n\n","pinned":true,"hidden":false,"mode":"html"},{"id":446,"value":"---\n## European countries\n### Definitions\nGet countries data and filter co2 data","pinned":false,"hidden":false,"mode":"md"},{"id":448,"value":"european_countries = [\n \"Albania\", \"Andorra\", \"Armenia\", \"Austria\", \"Azerbaijan\", \n \"Belarus\", \"Belgium\", \"Bosnia and Herzegovina\", \"Bulgaria\", \n \"Croatia\", \"Cyprus\", \"Czechia\", \"Denmark\", \"Estonia\", \n \"Finland\", \"France\", \"Georgia\", \"Germany\", \"Greece\", \n \"Hungary\", \"Iceland\", \"Ireland\", \"Italy\", \"Kazakhstan\", \n \"Kosovo\", \"Latvia\", \"Liechtenstein\", \"Lithuania\", \n \"Luxembourg\", \"Malta\", \"Moldova\", \"Monaco\", \"Montenegro\", \n \"Netherlands\", \"North Macedonia\", \"Norway\", \"Poland\", \n \"Portugal\", \"Romania\", \"Russia\", \"San Marino\", \"Serbia\", \n \"Slovakia\", \"Slovenia\", \"Spain\", \"Sweden\", \"Switzerland\", \n \"Turkey\", \"Ukraine\", \"United Kingdom\", \"Vatican\"\n];","pinned":false,"hidden":false,"mode":"ojs"},{"id":460,"value":"european_co2 = {\n const co2_list = {};\n \n european_countries.forEach(european_country => {\n if (emissions[european_country] !== undefined) {\n co2_list[european_country] = emissions[european_country].data;\n }\n })\n \n return co2_list;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":450,"value":"european_countries_co2 = {\n const co2_list = [];\n \n european_countries.forEach(european_country => {\n if (emissions[european_country] !== undefined) {\n co2_list.push({\"country\": european_country, \"data\": emissions[european_country].data});\n }\n })\n \n return co2_list;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":591,"value":"---\n## Other questions\nI haven't explored this ... but it might be something cool to do with this dataset.","pinned":false,"hidden":false,"mode":"md"},{"id":545,"value":"---\n#### Addendum: data, libraries, other ...","pinned":false,"hidden":false,"mode":"md"},{"id":554,"value":"emissions = (\n await fetch(\n \"https://nyc3.digitaloceanspaces.com/owid-public/data/co2/owid-co2-data.json\"\n )\n).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":561,"value":"import { toc } from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":567,"value":"###### Chart 1","pinned":false,"hidden":false,"mode":"md"},{"id":618,"value":"worldWideData = Object.entries(emissions).flatMap(([country, { iso_code, data }]) =>\n data\n .filter(entry => entry.year >= 1969 && entry.year <= 2022)\n .map(({ year, cumulative_co2 }) => ({\n year,\n entity: rename.get(country) || country,\n code: iso_code,\n cumulative_co2: cumulative_co2 ? Number(cumulative_co2.toFixed(4)) : undefined,\n }))\n);","pinned":false,"hidden":false,"mode":"ojs"},{"id":646,"value":"rename = new Map([\n [\"Bosnia and Herzegovina\", \"Bosnia and Herz.\"],\n [\"Central African Republic\", \"Central African Rep.\"],\n [\"Cote d'Ivoire\", \"Côte d'Ivoire\"],\n [\"Democratic Republic of Congo\", \"Dem. Rep. Congo\"],\n [\"Dominican Republic\", \"Dominican Rep.\"],\n [\"Equatorial Guinea\", \"Eq. Guinea\"],\n [\"Saint Vincent and the Grenadines\", \"St. Vin. and Gren.\"],\n [\"Sao Tome and Principe\", \"São Tomé and Principe\"],\n [\"Solomon Islands\", \"Solomon Is.\"],\n [\"South Sudan\", \"S. Sudan\"],\n [\"Syrian Arab Republic\", \"Syria\"],\n [\"North Macedonia\", \"Macedonia\"],\n [\"United States\", \"United States of America\"]\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":649,"value":"years1 = [...data1.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":659,"value":"yearData = data1.get(date1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":651,"value":"yearMean = d3.mean(yearData.values());","pinned":false,"hidden":false,"mode":"ojs"},{"id":656,"value":"data1 = new Map([\n ...d3.rollup(\n worldWideData,\n (v) => d3.sum(v, (d) => d.cumulative_co2),\n (d) => d.year,\n (d) => d.entity\n )\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":697,"value":"dataDomain = d3.extent(worldWideData, (d) => d.cumulative_co2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":864,"value":"maxCumulativeCO2 = 1,735,717.75","pinned":false,"hidden":false,"mode":"ojs"},{"id":872,"value":"customDomain = [0, maxCumulativeCO2 * 25];","pinned":false,"hidden":false,"mode":"ojs"},{"id":861,"value":"colorScale = d3.scaleQuantize()\n .domain([0, maxCumulativeCO2 * 1.5]) // Adjust multiplier as needed\n .range(['#ffcccc', '#ff9999', '#ff6666', '#ff3333', '#ff0000']);","pinned":false,"hidden":false,"mode":"ojs"},{"id":700,"value":"color = d3.scaleSequential()\n .domain(customDomain)\n .interpolator(colorInterpolator)\n .unknown(\"#ccc\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":706,"value":"projection = d3.geoEqualEarth() ","pinned":false,"hidden":false,"mode":"ojs"},{"id":709,"value":"path = d3.geoPath(projection)","pinned":false,"hidden":false,"mode":"ojs"},{"id":712,"value":"width = 975","pinned":false,"hidden":false,"mode":"ojs"},{"id":715,"value":"height = {\n const [[x0, y0], [x1, y1]] = d3\n .geoPath(projection.fitWidth(width, outline))\n .bounds(outline);\n const dy = Math.ceil(y1 - y0),\n l = Math.min(Math.ceil(x1 - x0), dy);\n projection.scale((projection.scale() * (l - 1)) / l).precision(0.2);\n return dy;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":717,"value":"outline = ({ type: \"Sphere\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":671,"value":"import { Scrubber } from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":683,"value":"import { countries, world } from \"@d3/world-choropleth\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":690,"value":"import { colorInterpolatorPicker } from \"@zechasault/color-schemes-and-interpolators-picker\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":694,"value":"import { legend } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":653,"value":"formatMean = d3.format(\",.2f\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":572,"value":"###### Chart 2","pinned":false,"hidden":false,"mode":"md"},{"id":524,"value":"// Function to get total of co2_filter of every european country between 2019 to 2020 \nfunction get_total_co2_country(european_co2) {\n const country_total_co2 = {};\n\n Object.keys(european_co2).forEach(function(country_name) {\n if (!country_total_co2[country_name]) {\n country_total_co2[country_name] = 0;\n }\n european_co2[country_name].filter(d => d.year > 2018 && d.year <= 2022).forEach(data => {\n if (data[co2_filter]) {\n country_total_co2[country_name] += data[co2_filter];\n }\n })\n });\n\n return Object.entries(country_total_co2).map(([name, totalCO2]) => ({ name, totalCO2 }));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":789,"value":"// Function to get all data of every european country\nfunction get_country_data(country_name) {\n const co2_list = [];\n\n european_co2[country_name].forEach(data => {\n data[\"country\"] = country_name;\n co2_list.push(data);\n });\n\n return co2_list;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":791,"value":"co2_fields = {\n const co2_set = new Set();\n\n european_countries_co2.forEach(object => {\n object.data.forEach(dict => {\n Object.keys(dict).forEach(key => {\n if (key.includes(\"co2\")) {\n co2_set.add(key);\n }\n })\n });\n });\n\n return Array.from(co2_set).sort().reduce((a, v) => ({ ...a, [v]: v}), {}) \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":793,"value":"european_total_co2_country = get_total_co2_country(european_co2);","pinned":false,"hidden":false,"mode":"ojs"},{"id":795,"value":"most_european_total_co2_country = european_total_co2_country.sort((a, b) => b.totalCO2 - a.totalCO2);","pinned":false,"hidden":false,"mode":"ojs"},{"id":797,"value":"most_european_total_co2_country_data = get_country_data(most_european_total_co2_country[0].name).concat(get_country_data(most_european_total_co2_country[1].name)).concat(get_country_data(most_european_total_co2_country[2].name)).concat(get_country_data(most_european_total_co2_country[3].name)).concat(get_country_data(most_european_total_co2_country[4].name)).concat(get_country_data(most_european_total_co2_country[5].name)).concat(get_country_data(most_european_total_co2_country[6].name)).concat(get_country_data(most_european_total_co2_country[7].name)).concat(get_country_data(most_european_total_co2_country[8].name)).concat(get_country_data(most_european_total_co2_country[9].name));","pinned":false,"hidden":false,"mode":"ojs"},{"id":848,"value":"most_european_total_co2_country_top5 = most_european_total_co2_country[0].name + \" with \" + most_european_total_co2_country[0].totalCO2 + \", \" + most_european_total_co2_country[1].name + \" with \" + most_european_total_co2_country[1].totalCO2 + \", \" + most_european_total_co2_country[2].name + \" with \" + most_european_total_co2_country[2].totalCO2 + \", \" + most_european_total_co2_country[3].name + \" with \" + most_european_total_co2_country[3].totalCO2 + \" and \" + most_european_total_co2_country[4].name + \" with \" + most_european_total_co2_country[4].totalCO2","pinned":false,"hidden":false,"mode":"ojs"},{"id":575,"value":"###### Chart 3","pinned":false,"hidden":false,"mode":"md"},{"id":998,"value":"function corr(x, y) {\n const n = x.length;\n if (y.length !== n)\n throw new Error(\"The two columns must have the same length.\");\n const x_ = d3.mean(x);\n const y_ = d3.mean(y);\n const XY = d3.sum(x, (_, i) => (x[i] - x_) * (y[i] - y_));\n const XX = d3.sum(x, (d) => (d - x_) ** 2);\n const YY = d3.sum(y, (d) => (d - y_) ** 2);\n return XY / Math.sqrt(XX * YY);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1001,"value":"correlations = d3.cross(fields, fields).map(([a, b]) => ({\n a,\n b,\n correlation: corr(Plot.valueof(country_co2, a), Plot.valueof(country_co2, b))\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1006,"value":"co2_fields_country = {\n const co2_set = new Set();\n\n country_co2.forEach(object => {\n Object.keys(object).forEach(key => {\n if (isNaN(object[key]) && co2_set.has(key)) {\n co2_set.delete(key);\n } \n else if (key != \"country\") {\n co2_set.add(key);\n }\n });\n });\n\n return Array.from(co2_set)\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":467,"value":"country_co2 = european_co2[countrySelected];","pinned":false,"hidden":false,"mode":"ojs"},{"id":943,"value":"SELECT * FROM \"country_co2\"","pinned":false,"hidden":false,"mode":"sql","output":"","database":"var:country_co2"},{"id":1119,"value":"european_countries_co2_data = {\n let data_list = [];\n\n european_countries.forEach(country_name => {\n data_list = data_list.concat(get_country_data(country_name).filter(d => d.year >= 2018 && d.year <= 2022));\n })\n\n return data_list;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":580,"value":"###### Chart 4","pinned":false,"hidden":false,"mode":"md"},{"id":582,"value":"###### Chart 5","pinned":false,"hidden":false,"mode":"md"},{"id":584,"value":"###### Chart 6","pinned":false,"hidden":false,"mode":"md"}],"files":"$W32","stdlib":"1","titling":"auto"}