29:null 2a:["$","title",null,{"children":"SP2025 European Energy Politics (plot w hacks) | Angie Hjort | Observable"}] 2f:T559,filledData = (() => { let result = []; let grouped = d3.group(EUcountries_filtered, d => d.country); // Process each country grouped.forEach((records, country) => { // Group records by election year (each group contains all parties for that election) let electionsByYear = d3.group(records, d => d.year); // Get all election years, sorted in ascending order let yearsWithElection = Array.from(electionsByYear.keys()).sort((a, b) => a - b); // Define the range of years from the first to the last election let startYear = yearsWithElection[0]; let endYear = yearsWithElection[yearsWithElection.length - 1]; // Variable to hold the most recent election records (all parties) let currentElection = null; // Loop through every year in the range for (let year = startYear; year <= endYear; year++) { // Update currentElection if there's an election that year if (electionsByYear.has(year)) { currentElection = electionsByYear.get(year); } // For years with no new election, carry forward the previous election's data if (currentElection) { currentElection.forEach(record => { // Spread the record and override the year with the current year result.push({ ...record, year }); }); } } }); return result; })()30:T66a,country_codes = [ {code:'EU7_2020]', name: "European Union-27countries (from 2020)"}, {code:'EA0]', name: "Euro area–20countries (from 2023)"}, {code:'EU7_2020]', name: "EuropeanU nion-27countries (from 2020)"}, {code:'BE', name: 'Belgium'}, {code:'BG', name: 'Bulgaria'}, {code:'CZ', name: 'Czechia'}, {code:'DK', name: 'Denmark'}, {code:'DE', name: 'Germany'}, {code:'EE', name: 'Estonia'}, {code:'IE', name: 'Ireland'}, {code:'EL', name: 'Greece'}, {code:'ES', name: 'Spain'}, {code:'FR', name: 'France'}, {code:'HR', name: 'Croatia'}, {code:'IT', name: 'Italy'}, {code:'CY', name: 'Cyprus'}, {code:'LV', name: 'Latvia'}, {code:'LT', name: 'Lithuania'}, {code:'LU', name: 'Luxembourg'}, {code:'HU', name: 'Hungary'}, {code:'MT', name: 'Malta'}, {code:'NL', name: 'Netherlands'}, {code:'AT', name: 'Austria'}, {code:'PL', name: 'Poland'}, {code:'PT', name: 'Portugal'}, {code:'RO', name: 'Romania'}, {code:'SI', name: 'Slovenia'}, {code:'SK', name: 'Slovakia'}, {code:'FI', name: 'Finland'}, {code:'SE', name: 'Sweden'}, {code:'IS', name: 'Iceland'}, {code:'LI', name: 'Liechtenstein'}, {code:'NO', name: 'Norway'}, {code:'UK', name: 'United Kingdom'}, {code:'BA', name: 'Bosnia and Herzegovina'}, {code:'ME', name: 'Montenegro'}, {code:'MD', name: 'Moldova'}, {code:'MK', name: 'North Macedonia'}, {code:'GE', name: 'Georgia'}, {code:'AL', name: 'Albania'}, {code:'RS', name: 'Serbia'}, {code:'TR', name: 'Türkiye'}, {code:'UA', name: 'Ukraine'}, {code:'XK', name: 'Kosovo*'} ] // country codes to translate from estat energy data to proper names31:T1eb4,nrg_balance_codes = [ {code: 'PPRD', name: 'Primary production'}, {code: 'RCV_RCY', name: 'Recovered and recycled products'}, {code: 'IMP', name: 'Imports'}, {code: 'EXP', name: 'Exports'}, {code: 'STK_CHG', name: 'Change in stock'}, {code: 'GAE', name: 'Gross available energy'}, {code: 'INTMARB', name: 'International maritime bunkers'}, {code: 'GIC', name: 'Gross inland consumption'}, {code: 'INTAVI', name: 'International aviation'}, {code: 'NRGSUP', name: 'Total energy supply'}, {code: 'GIC_EED', name: 'Gross inland consumption - Energy Efficiency Directive'}, {code: 'PEC_EED', name: 'Primary energy consumption - Energy Efficiency Directive'}, {code: 'FEC_EED', name: 'Final energy consumption - Energy Efficiency Directive'}, {code: 'GIC2020-2030', name: 'Gross inland consumption (Europe 2020-2030)'}, {code: 'PEC2020-2030', name: 'Primary energy consumption (Europe 2020-2030)'}, {code: 'FEC2020-2030', name: 'Final energy consumption (Europe 2020-2030)'}, {code: 'TI_E', name: 'Transformation input - energy use'}, {code: 'TI_EHG_E', name: 'Transformation input - electricity and heat generation - energy use'}, {code: 'TI_CO_E', name: 'Transformation input - coke ovens - energy use'}, {code: 'TI_BF_E', name: 'Transformation input - blast furnaces - energy use'}, {code: 'TI_GW_E', name: 'Transformation input - gas works - energy use'}, {code: 'TI_RPI_E', name: 'Transformation input - refineries and petrochemical industry - energy use'}, {code: 'TI_PF_E', name: 'Transformation input - patent fuel plants - energy use'}, {code: 'TI_BKBPB_E', name: 'Transformation input - brown coal briquettes and peat briquettes plants - energy use'}, {code: 'TI_CL_E', name: 'Transformation input - coal liquefaction plants - energy use'}, {code: 'TI_BNG_E', name: 'Transformation input - for blending with natural gas - energy use'}, {code: 'TI_LBB_E', name: 'Transformation input - liquid biofuels blended - energy use'}, {code: 'TI_CPP_E', name: 'Transformation input - charcoal production plants - energy use'}, {code: 'TI_GTL_E', name: 'Transformation input - gas-to-liquids plants - energy use'}, {code: 'TI_NSP_E', name: 'Transformation input - not elsewhere specified - energy use'}, {code: 'TO', name: 'Transformation output'}, {code: 'TO_EHG', name: 'Transformation output - electricity and heat generation'}, {code: 'TO_CO', name: 'Transformation output - coke ovens'}, {code: 'TO_BF', name: 'Transformation output - blast furnaces'}, {code: 'TO_GW', name: 'Transformation output - gas works'}, {code: 'TO_RPI', name: 'Transformation output - refineries and petrochemical industry'}, {code: 'TO_PF', name: 'Transformation output - patent fuel plants'}, {code: 'TO_BKBPB]', name: 'Transformation output - brown coal briquettes and peat briquettes plants'}, {code: 'TO_CL', name: 'Transformation output - coal liquefaction plants'}, {code: 'TO_BNG', name: 'Transformation output - blended in natural gas'}, {code: 'TO_LBB', name: 'Transformation output - liquid biofuels blended'}, {code: 'TO_CPP', name: 'Transformation output - charcoal production plants'}, {code: 'TO_GTL', name: 'Transformation output - gas-to-liquids plants'}, {code: 'TO_NSP', name: 'Transformation output - not elsewhere specified'}, {code: 'NRG_E', name: 'Energy sector - energy use'}, {code: 'NRG_EHG_E', name: 'Energy sector - electricity and heat generation - energy use'}, {code: 'NRG_CM_E', name: 'Energy sector - coal mines - energy use'}, {code: 'NRG_OIL_NG_E', name: 'Energy sector - oil and natural gas extraction plants - energy use'}, {code: 'NRG_PF_E', name: 'Energy sector - patent fuel plants - energy use'}, {code: 'NRG_CO_E', name: 'Energy sector - coke ovens - energy use'}, {code: 'NRG_BKBPB_E', name: 'Energy sector - brown coal briquettes and peat briquettes plants - energy use'}, {code: 'NRG_GW_E', name: 'Energy sector - gas works - energy use'}, {code: 'NRG_BF_E', name: 'Energy sector - blast furnaces - energy use'}, {code: 'NRG_PR_E', name: 'Energy sector - petroleum refineries (oil refineries) - energy use'}, {code: 'NRG_NI_E', name: 'Energy sector - nuclear industry - energy use'}, {code: 'NRG_CL_E', name: 'Energy sector - coal liquefaction plants - energy use'}, {code: 'NRG_LNG_E', name: 'Energy sector - liquefaction and regasification plants (LNG) - energy use'}, {code: 'NRG_BIOG_E', name: 'Energy sector - gasification plants for biogas - energy use'}, {code: 'NRG_GTL_E', name: 'Energy sector - gas-to-liquids plants - energy use'}, {code: 'NRG_CPP_E', name: 'Energy sector - charcoal production plants - energy use'}, {code: 'NRG_NSP_E', name: 'Energy sector - not elsewhere specified - energy use'}, {code: 'DL', name: 'Distribution losses'}, {code: 'AFC', name: 'Available for final consumption'}, {code: 'FC_NE', name: 'Final consumption - non-energy use'}, {code: 'FC_E', name: 'Final consumption - energy use'}, {code: 'FC_IND_E', name: 'Final consumption - industry sector - energy use'}, {code: 'FC_IND_IS_E', name: 'Final consumption - industry sector - iron and steel - energy use'}, {code: 'FC_IND_CPC_E', name: 'Final consumption - industry sector - chemical and petrochemical - energy use'}, {code: 'FC_IND_NFM_E', name: 'Final consumption - industry sector - non-ferrous metals - energy use'}, {code: 'FC_IND_NMM_E', name: 'Final consumption - industry sector - non-metallic minerals - energy use'}, {code: 'FC_IND_TE_E', name: 'Final consumption - industry sector - transport equipment - energy use'}, {code: 'FC_IND_MAC_E', name: 'Final consumption - industry sector - machinery - energy use'}, {code: 'FC_IND_MQ_E', name: 'Final consumption - industry sector - mining and quarrying - energy use'}, {code: 'FC_IND_FBT_E', name: 'Final consumption - industry sector - food, beverages and tobacco - energy use'}, {code: 'FC_IND_PPP_E', name: 'Final consumption - industry sector - paper, pulp and printing - energy use'}, {code: 'FC_IND_WP_E', name: 'Final consumption - industry sector - wood and wood products - energy use'}, {code: 'FC_IND_CON_E', name: 'Final consumption - industry sector - construction - energy use'}, {code: 'FC_IND_TL_E', name: 'Final consumption - industry sector - textile and leather - energy use'}, {code: 'FC_IND_NSP_E', name: 'Final consumption - industry sector - not elsewhere specified - energy use'}, {code: 'FC_TRA_E', name: 'Final consumption - transport sector - energy use'}, {code: 'FC_TRA_RAIL_E', name: 'Final consumption - transport sector - rail - energy use'}, {code: 'FC_TRA_ROAD_E', name: 'Final consumption - transport sector - road - energy use'}, {code: 'FC_TRA_DAVI_E', name: 'Final consumption - transport sector - domestic aviation - energy use'}, {code: 'FC_TRA_DNAVI_E', name: 'Final consumption - transport sector - domestic navigation - energy use'}, {code: 'FC_TRA_PIPE_E', name: 'Final consumption - transport sector - pipeline transport - energy use'}, {code: 'FC_TRA_NSP_E', name: 'Final consumption - transport sector - not elsewhere specified - energy use'}, {code: 'FC_OTH_E', name: 'Final consumption - other sectors - energy use'}, {code: 'FC_OTH_CP_E', name: 'Final consumption - other sectors - commercial and public services - energy use'}, {code: 'FC_OTH_HH_E', name: 'Final consumption - other sectors - households - energy use'}, {code: 'FC_OTH_AF_E', name: 'Final consumption - other sectors - agriculture and forestry - energy use'}, {code: 'FC_OTH_FISH_E', name: 'Final consumption - other sectors - fishing - energy use'}, {code: 'FC_OTH_NSP_E', name: 'Final consumption - other sectors - not elsewhere specified - energy use'}, {code: 'STATDIFF', name: 'Statistical differences'}, {code: 'GEP', name: 'Gross electricity production'}, {code: 'GHP', name: 'Gross heat production'} ]; // codes to translte from nrg bal codes to proper names32:T67b,function convertToGWH(data) { const seen = new Set(); return data .map(d => { // Clean and parse the numeric value const rawValue = typeof d["energy balance value"] === "string" ? d["energy balance value"].replace(/,/g, "") : d["energy balance value"]; const commalessValue = parseFloat(rawValue); // Convert to GWH let convertedValue; switch (d.unit) { case "KTOE": convertedValue = commalessValue * 11.63; break; case "TJ": convertedValue = commalessValue * 0.2777777; break; case "GWH": convertedValue = commalessValue; break; default: convertedValue = NaN; } return { ...d, "energy balance value": convertedValue, unit: "GWH" }; }) // Remove duplicates .filter(d => { const key = JSON.stringify([ d["country name"], d["year"], d["siec name"], d["nrg_bal name"], d["unit"], // note: we dont want to only remove "duplicates" where the energy balance value is exactly identical, but anything where it is within +- 5%, since there can be small rounding errors in conversion from different sources, and double-countings may not always look exactly identical. // instead of filtering out "close enough" energy balance values, we can simply specify that each row should be uniquely determined by all dimensions other than energy balance value ]); if (seen.has(key)) { return false; } else { seen.add(key); return true; } }); } 33:T5bf, 34:T717,

${europeMap}

${combinedChart}

${energyChart}

${ selectedCountry ? selectedCountry.includes(" | ") ? html`${selectedCountry.split(" | ")[0]} | ${selectedCountry.split(" | ")[1]}` : selectedCountry : "Pick a country" }

${ (() => { const country = selectedCountry?.split(" | ")[0]; if (!country) return ""; // Grab from the same emissions dataset used in the map const rec = emissionsData.find(d => d.Country === country && +d.Year === YearSlider); if (!rec) return ""; let countryColor = getCountryColor ? getCountryColor(country) : "#555"; let fontWeight = "bold"; // Changed 'const' to 'let' let fontSize = "1em"; // Changed 'const' to 'let' const emissionsValue = Math.round(parseFloat(rec.Emissions)); return html` ${emissionsValue} million tons of CO₂ equivalent `; })() // ← close the IIFE here }

35:Tecd,europeMap = { const width = 1000, height = 600; const svg = d3.create("svg") .attr("width", width) .attr("height", height); const geoData = await d3.json("https://raw.githubusercontent.com/leakyMirror/map-of-europe/master/GeoJSON/europe.geojson"); geoData.features = geoData.features.filter( d => !["Iceland", "Israel"].includes(d.properties.name || d.properties.NAME) ); const allEmissionsData = await FileAttachment("reshaped-emissions4.csv").csv(); const allVals = allEmissionsData .filter(d => d.Country !== "Russia") .map(d => +d.Emissions); const globalMin = d3.min(allVals); const globalMax = d3.max(allVals); const year = YearSlider; const yearData = allEmissionsData.filter(d => +d.Year === year); const emissionsByCountry = new Map(yearData.map(d => [d.Country, +d.Emissions])); const colorScale = d3.scaleSequentialPow() .domain([globalMin, globalMax]) .exponent(0.3) .interpolator(d3.interpolateYlOrRd) .clamp(true); const legendWidth = 15; const legendHeight = 200; const defs = svg.append("defs"); const gradientId = "color-gradient"; const linearGradient = defs.append("linearGradient") .attr("id", gradientId) .attr("x1", "0%").attr("y1", "100%") .attr("x2", "0%").attr("y2", "0%"); const stops = [0, 0.2, 0.4, 0.6, 0.8, 1]; stops.forEach(stop => { const value = globalMin + (globalMax - globalMin) * stop; linearGradient.append("stop") .attr("offset", `${stop * 100}%`) .attr("stop-color", colorScale(value)); }); const projection = d3.geoMercator().fitSize([width, height], geoData); const path = d3.geoPath().projection(projection); let selectedCountryItem = null; let lockedCountry = null; svg.append("g") .selectAll("path") .data(geoData.features) .join("path") .attr("d", path) .attr("stroke", "#333") .attr("stroke-width", 1) .attr("fill", d => { let name = d.properties.name || d.properties.NAME; if (name === "Czech Republic") name = "Czechia"; const val = emissionsByCountry.get(name); return val != null ? colorScale(val) : "#CCCCCC"; }) .on("mouseover", function (event, d) { let name = d.properties.name || d.properties.NAME; if (name === "Czech Republic") name = "Czechia"; if (selectedCountryItem !== this) { d3.select(this).attr("stroke-width", 2); } if (lockedCountry) { mutable selectedCountry = lockedCountry === name ? lockedCountry : `${lockedCountry} | ${name}`; } else { mutable selectedCountry = name; } }) .on("click", function (event, d) { let name = d.properties.name || d.properties.NAME; if (name === "Czech Republic") name = "Czechia"; if (selectedCountryItem === this) { selectedCountryItem = null; lockedCountry = null; d3.select(this).attr("stroke-width", 2); mutable selectedCountry = name; return; } if (selectedCountryItem) { d3.select(selectedCountryItem).attr("stroke-width", 1); } selectedCountryItem = this; lockedCountry = name; d3.select(this).attr("stroke-width", 3); mutable selectedCountry = name; }) .on("mouseout", function (event, d) { if (selectedCountryItem !== this) { d3.select(this).attr("stroke-width", 1); if (lockedCountry) { mutable selectedCountry = lockedCountry; } else { mutable selectedCountry = null; } } svg.selectAll(".hover-info").remove(); }); return svg.node(); }36:Td24,combinedChart = (() => { const classificationColors = { "ecological": "#00CC00", "socialist": "#FF0000", "social democratic": "#FF9500", "liberal": "#FFCC00", "christian democratic": "#99CCFF", "conservative": "#1E90FF", "nationalist": "#8B0000", "agrarian": "#F5DEB3", "ethnic/regional": "#FF8C00", "special issue": "#9400D3", "electoral alliances without a dominant party": "#00CCCC", "missing information": "#808080" }; //pretty self explanatory function function capitalizeWords(str) { if (!str) return ""; return str .split(/[\s/]+/) .map(word => word.charAt(0).toUpperCase() + word.slice(1)) .join(" "); } //another text parsing function, returns lowercase name function normalizeCountry(name) { return name ? name.trim().toLowerCase() : ""; } const selectedYear = YearSlider; const clickedCountry = (selectedCountry || "Austria").split(" | ")[0]; const normalizedSelectedCountry = normalizeCountry(clickedCountry); console.log("Normalized Selected Country:", normalizedSelectedCountry); console.log("Selected Year:", selectedYear); //filter political dataset by year and country selection to get party share-of-vote values const filteredData = ActualDataTwo.filter(d => normalizeCountry(d.Country) === normalizedSelectedCountry && +d.Year === selectedYear); console.log("Filtered Data:", filteredData); if (filteredData.length === 0) { return md`No election data for **${clickedCountry}** in **${selectedYear}**.`; }; const style = document.createElement('style'); style.textContent = ` .plot-tooltip { background: rgba(0, 0, 0, 0.8) !important; color: white !important; padding: 15px 20px !important; border-radius: 8px !important; font-size: 16px !important; box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3) !important; min-width: 200px !important; max-width: 300px !important; white-space: pre-wrap !important; font-family: sans-serif !important; line-height: 1.5 !important; } `; document.head.appendChild(style); const plotChart = Plot.plot({ width: 450, height: 350, marginTop: 20, marginRight: 20, marginBottom: 100, marginLeft: 40, x: { label: "Political Party", tickRotate: -45, tickSize: 0 }, y: { label: "Seats Won" }, marks: [ Plot.barY( filteredData, { x: "Party Initials", y: "Seats Won", sort: { x: "y", reverse: true }, fill: d => { if (!d || !d["Party Classification"]) return "#ccc"; return classificationColors[d["Party Classification"].toLowerCase()] || "#ccc"; }, tip: true, title: d => { if (!d) return "No data"; const initials = d["Party Initials"] || "N/A"; const partyName = d["Party Name (English)"] || "Unknown"; const seats = d["Seats Won"] || "0"; const classification = d["Party Classification"] ? capitalizeWords(d["Party Classification"]) : "Unknown"; return `${partyName}\n\n${classification}\n\nSeats Won: ${seats}\n\n `; } } ), Plot.ruleY([0]) ] }); return plotChart; })();37:Tc7e,energyChart = (() => { const energySourceColors = { "Renewables and biofuels": "#4CAF50", "Solid fossil fuels": "#795548", "Manufactured gases": "#9C27B0", "Peat and peat products": "#A1887F", "Oil shale and oil sands": "#5D4037", "Natural gas": "#03A9F4", "Oil and petroleum products (excluding biofuel portion)": "#FF9800", "Non-renewable waste": "#607D8B", "Nuclear Heat": "#E91E63", "Electricity": "#FFEB3B", "Heat": "#F44336" }; //pretty self explanatory function function capitalizeWords(str) { return str .split(/[\s/]+/) .map(word => word.charAt(0).toUpperCase() + word.slice(1)) .join(" "); } //another text parsing function, returns lowercase name function normalizeCountry(name) { return name ? name.trim().toLowerCase() : ""; } const selectedYear = YearSlider; const clickedCountry = (selectedCountry || "Austria").split(" | ")[0]; const normalizedSelectedCountry = normalizeCountry(clickedCountry); console.log("Normalized Selected Country:", normalizedSelectedCountry); console.log("Selected Year:", selectedYear); //filter energy dataset by year and country selection to get energy mix values const filteredEnergyData = nrg_bal_s_tidier_named_conv_no_total.filter(d => normalizeCountry(d["country name"]) === normalizedSelectedCountry && +d.year === selectedYear && d["nrg_bal name"] === selectedEnergyStat); // filtered data (w/out total), renaming fields to look nice //const filteredRenamedEnergyData = aq.from(filteredEnergyData).rename({"energy balance value":"Amount (GWH)", "siec name":"Type"}); console.log("Filtered Energy Data:", filteredEnergyData); if (filteredEnergyData.length === 0) { return md`No **${normalizeCountry(selectedEnergyStat)}** data for **${clickedCountry}** in **${selectedYear}**.`; } const plotEnergyChart = Plot.plot({ width: 450, height: 350, marginTop: 100, marginRight: 20, marginBottom: 40, marginLeft: 60, x: { label: "Energy Source", tickFormat: () => "" // <-- hide default tick label }, marks: [ Plot.barY(aq.from(filteredEnergyData).rename({"energy balance value":"Amount (GWH)", "siec name":"Type"}), { x: "Type", tickFormat: () => "", y: "Amount (GWH)", sort: { x: "y", reverse: true }, fill: d => energySourceColors[d["Type"]] || "#ccc", } ), Plot.ruleY([0]), Plot.text(aq.from(filteredEnergyData).rename({"energy balance value":"Amount (GWH)", "siec name":"Type"}), { x: "Type", y: d => d["Amount (GWH)"] + 1, // slightly above the bar text: d => d["Type"], //.toFixed(0) textAnchor: "start", // aligned with bar top (start) or bottom (end) rotate: -45, // rotate labels dy: -10, // negative shifts upward tickFormat: () => "", fontSize: 10 } ) ] }); return plotEnergyChart; })(); 38:T1521,possibleEnergyStats = [ 'Primary production', 'Recovered and recycled products', 'Imports', 'Exports', 'Gross available energy', 'Gross electricity production', 'Gross heat production', 'Total energy supply', 'Available for final consumption', 'Final consumption - non-energy use', 'Final consumption - energy use', 'Change in stock', 'Distribution losses', 'Gross inland consumption - Energy Efficiency Directive', 'Primary energy consumption - Energy Efficiency Directive', 'Final energy consumption - Energy Efficiency Directive', 'Gross inland consumption (Europe 2020-2030)', 'Primary energy consumption (Europe 2020-2030)', 'Final energy consumption (Europe 2020-2030)', 'Transformation input - energy use', 'Transformation input - electricity and heat generation - energy use', 'Transformation input - coke ovens - energy use', 'Transformation input - blast furnaces - energy use', 'Transformation input - gas works - energy use', 'Transformation input - refineries and petrochemical industry - energy use', 'Transformation input - patent fuel plants - energy use', 'Transformation input - brown coal briquettes and peat briquettes plants - energy use', 'Transformation input - coal liquefaction plants - energy use', 'Transformation input - for blending with natural gas - energy use', 'Transformation input - liquid biofuels blended - energy use', 'Transformation input - charcoal production plants - energy use', 'Transformation input - gas-to-liquids plants - energy use', 'Transformation input - not elsewhere specified - energy use', 'Transformation output', 'Transformation output - electricity and heat generation', 'Transformation output - coke ovens', 'Transformation output - blast furnaces', 'Transformation output - gas works', 'Transformation output - refineries and petrochemical industry', 'Transformation output - patent fuel plants', 'Transformation output - brown coal briquettes and peat briquettes plants', 'Transformation output - coal liquefaction plants', 'Transformation output - blended in natural gas', 'Transformation output - liquid biofuels blended', 'Transformation output - charcoal production plants', 'Transformation output - gas-to-liquids plants', 'Transformation output - not elsewhere specified', 'Energy sector - energy use', 'Energy sector - electricity and heat generation - energy use', 'Energy sector - coal mines - energy use', 'Energy sector - oil and natural gas extraction plants - energy use', 'Energy sector - patent fuel plants - energy use', 'Energy sector - coke ovens - energy use', 'Energy sector - brown coal briquettes and peat briquettes plants - energy use', 'Energy sector - gas works - energy use', 'Energy sector - blast furnaces - energy use', 'Energy sector - petroleum refineries (oil refineries) - energy use', 'Energy sector - nuclear industry - energy use', 'Energy sector - coal liquefaction plants - energy use', 'Energy sector - liquefaction and regasification plants (LNG) - energy use', 'Energy sector - gasification plants for biogas - energy use', 'Energy sector - gas-to-liquids plants - energy use', 'Energy sector - charcoal production plants - energy use', 'Energy sector - not elsewhere specified - energy use', 'Final consumption - industry sector - energy use', 'Final consumption - industry sector - iron and steel - energy use', 'Final consumption - industry sector - chemical and petrochemical - energy use', 'Final consumption - industry sector - non-ferrous metals - energy use', 'Final consumption - industry sector - non-metallic minerals - energy use', 'Final consumption - industry sector - transport equipment - energy use', 'Final consumption - industry sector - machinery - energy use', 'Final consumption - industry sector - mining and quarrying - energy use', 'Final consumption - industry sector - food, beverages and tobacco - energy use', 'Final consumption - industry sector - paper, pulp and printing - energy use', 'Final consumption - industry sector - wood and wood products - energy use', 'Final consumption - industry sector - construction - energy use', 'Final consumption - industry sector - textile and leather - energy use', 'Final consumption - industry sector - not elsewhere specified - energy use', 'Final consumption - transport sector - energy use', 'Final consumption - transport sector - rail - energy use', 'Final consumption - transport sector - road - energy use', 'Final consumption - transport sector - domestic aviation - energy use', 'Final consumption - transport sector - domestic navigation - energy use', 'Final consumption - transport sector - pipeline transport - energy use', 'Final consumption - transport sector - not elsewhere specified - energy use', 'Final consumption - other sectors - energy use', 'Final consumption - other sectors - commercial and public services - energy use', 'Final consumption - other sectors - households - energy use', 'Final consumption - other sectors - agriculture and forestry - energy use', 'Final consumption - other sectors - fishing - energy use', 'Final consumption - other sectors - not elsewhere specified - energy use', 'Statistical differences', 'International maritime bunkers', 'Gross inland consumption', 'International aviation' ]; 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2d:{"id":"a309884d1bd45af4","slug":null,"title":"SP2025 European Energy Politics (plot w hacks)","thumbnail":null,"access_level":"public","create_time":"$D2025-05-09T21:17:16.473Z","update_time":"$D2025-05-09T21:18:57.489Z","view_time":null,"first_public_time":"$D2025-05-09T21:17:27.674Z","fork":{"id":"957f607c14f5c6d2","slug":null,"title":"EU Election Results","owner":{"id":"50ad26f6f15e6ae6","login":"dv-design-2025s","name":"🪦 dv design 2025 spring"}},"stars":0,"starred":false,"owner":{"type":"team","id":"c555e02063f451b5","login":"angiehjort","name":"Angie Hjort","avatar_url":"https://avatars-next.observableusercontent.com/d3c0d6d22f1525b9f9f1d4c8187de1dc20adc65c5baeb538c15672fb523883fa","tier":"starter_2024"},"version":1398,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"1245953aabb247aa","login":"angiehjort","name":"Angie Hjort","avatar_url":"https://avatars-next.observableusercontent.com/d3c0d6d22f1525b9f9f1d4c8187de1dc20adc65c5baeb538c15672fb523883fa","tier":null}],"members":[{"type":"individual","id":"1245953aabb247aa","login":"angiehjort","name":"Angie Hjort","avatar_url":"https://avatars-next.observableusercontent.com/d3c0d6d22f1525b9f9f1d4c8187de1dc20adc65c5baeb538c15672fb523883fa","tier":null,"role":"editor"},{"type":"team","id":"c555e02063f451b5","login":"angiehjort","name":"Angie Hjort","avatar_url":"https://avatars-next.observableusercontent.com/d3c0d6d22f1525b9f9f1d4c8187de1dc20adc65c5baeb538c15672fb523883fa","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":{"id":"c2b96431d92a6fa1","slug":"dis-course-projects","title":"Course projects archive","owner":{"id":"c555e02063f451b5","login":"angiehjort","name":"Angie Hjort"}},"type":"notebook","body":{"title":"SP2025 European Energy Politics (plot w hacks)","cells":[{"id":0,"value":"# SP2025 European Energy Politics (plot w hacks)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":799,"value":"TODO: \n - come up with political compass mapping\n - make y axis of charts fixed (maybe?)\n - add sankey flows for input output (if time)\n\nask ryan:\n- shift color scale to exclude russia\n- redo naming of political datasets for readability\n- check where we actually need console log and other shit that chatgpt added","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":896,"value":"**Below is the initial wrangling for the political data**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"data = FileAttachment(\"ppeg_parl_2024v1.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"euIso3cCodes = [\n \"AUT\", \"BEL\", \"BGR\", \"HRV\", \"CYP\", \"CZE\", \"DNK\",\n \"EST\", \"FIN\", \"FRA\", \"DEU\", \"GRC\", \"HUN\", \"IRL\",\n \"ITA\", \"LVA\", \"LTU\", \"LUX\", \"MLT\", \"NLD\", \"POL\",\n \"PRT\", \"ROU\", \"SVK\", \"SVN\", \"ESP\", \"SWE\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"EUcountries = data.filter(d => euIso3cCodes.includes(d.iso3c))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"EUcountries_withyear = EUcountries.map(d => ({\n ...d,\n year: d.edate.getFullYear()\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"EUcountries_post1991 = EUcountries_withyear.filter(d => +d.year >= 1991)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"EUcountries_neworder = EUcountries_post1991.map(d => {\n const keys = Object.keys(d);\n const keysWithoutYear = keys.filter(key => key !== 'year');\n const reorderedKeys = [\n ...keysWithoutYear.slice(0, 4),\n 'year',\n ...keysWithoutYear.slice(4)\n ];\n const reorderedObj = {};\n reorderedObj[keysWithoutYear[0]] = d[keysWithoutYear[0]];\n reorderedObj[keysWithoutYear[1]] = d[keysWithoutYear[1]];\n reorderedObj[keysWithoutYear[2]] = d[keysWithoutYear[2]];\n reorderedObj[keysWithoutYear[3]] = d[keysWithoutYear[3]];\n reorderedObj['year'] = d.year;\n keysWithoutYear.slice(4).forEach(key => {\n reorderedObj[key] = d[key];\n });\n return reorderedObj;\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"EUcountries_subset = EUcountries_neworder.map(d => ({\n country: d.cname_en,\n year: d.year,\n \"party initials\": d.pinitials,\n \"party name(english)\": d.pname_en,\n \"seats won\": d.seats,\n \"share of vote\": d.s_share,\n \"party classification\": d.cmp_parfam\n }));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"EUcountries_filtered = EUcountries_subset.filter(d => d[\"share of vote\"] > 0);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"newelections = EUcountries_filtered.reduce((acc, d) => {\n const key = `${d.country}-${d.year}`;\n if (!acc[key]) {\n acc[key] = {\n country: d.country,\n year: d.year,\n results: [] \n }; }\n acc[key].results.push({\n partyInitials: d[\"party initials\"],\n seatsWon: d[\"seats won\"],\n shareOfVote: d[\"share of vote\"]\n });\n return acc;}, \n {});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"newelectionsArray = Object.values(newelections);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"tidyElections = newelectionsArray.flatMap(d => \n d.results.map(party => ({\n country: d.country,\n year: d.year,\n \"party initials\": d.partyInitials,\n \"party name (english)\": d.partyName,\n \"seats won\": d.seatsWon,\n \"share of vote\": d.shareOfVote,\n \"party classification\": d.partyClassification\n }))\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"newtidyElections = newelectionsArray.flatMap(d => \n d.results.map(party => ({\n country: d.country,\n year: d.year,\n \"party initials\": party.partyInitials, \n \"party name (english)\": party.partyName,\n \"seats won\": party.seatsWon,\n \"share of vote\": party.shareOfVote,\n \"party classification\": party.partyClassification\n }))\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":101,"value":"csvData = d3.csvFormat(EUcountries_filtered);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"html`\n Download CSV\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":111,"value":"jsonData = JSON.stringify(EUcountries_filtered, null, 2);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"html`\n Download JSON\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"// Create a lookup Map from EUcountries_neworder keyed by year\nmergedData = (function() {\n // Create a lookup Map from EUcountries_neworder keyed by year.\n var neworderMap = new Map(EUcountries_neworder.map(function(d) {\n return [d.year, d.v_share_wgt];\n }));\n\n // Map over EUcountries_filtered and add the v_share_wgt value based on the year.\n var mergedData = EUcountries_filtered.map(function(d) {\n // Return a new object that includes all properties of d and the matching v_share_wgt.\n return Object.assign({}, d, {\n v_share_wgt: neworderMap.get(d.year) || null\n });\n });\n\n return mergedData;\n})();\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":137,"value":"mappedData = (function() {\n // Create a mapping from numeric codes to text descriptions.\n var partyMap = {\n \"10\": \"Ecological\",\n \"20\": \"Socialist\",\n \"30\": \"Social Democratic\",\n \"40\": \"Liberal\",\n \"50\": \"Christian Democratic\",\n \"60\": \"Conservative\",\n \"70\": \"Nationalist\",\n \"80\": \"Agrarian\",\n \"90\": \"Ethnic/Regional\",\n \"95\": \"Special Issue\",\n \"98\": \"Electoral Alliances without a dominant party\",\n \"999\": \"Missing information\",\n \"NA\": \"Missing information\"\n };\n return mergedData.map(function(d) {\n var code = d[\"party classification\"].toString();\n return Object.assign({}, d, {\n \"party classification\": partyMap[code] || d[\"party classification\"]\n });\n });\n})();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"labeledData = (function() {\n return mappedData.map(function(row) {\n var newRow = Object.assign({}, row);\n newRow[\"weighted share of vote\"] = row[\"v_share_wgt\"];\n delete newRow[\"v_share_wgt\"];\n return newRow;\n });\n})();\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"reorderedData = (function() {\n var order = [\"Country\", \"Year\", \"Party Classification\", \"v_share_wgt\"];\n return labeledData.map(function(row) {\n var newRow = {};\n order.forEach(function(key) {\n newRow[key] = row[key];\n });\n return newRow;\n });\n})();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":152,"value":"newreorderedData = (function() {\n return labeledData.map(function(d) {\n return {\n \"Country\": d.country,\n \"Year\": d.year,\n \"Party Initials\": d[\"party initials\"],\n \"Party Name (English)\": d[\"party name(english)\"],\n \"Seats Won\": d[\"seats won\"],\n \"Share of Vote\": d[\"share of vote\"],\n \"Weighted Share of Vote\": d[\"weighted share of vote\"],\n \"Party Classification\": d[\"party classification\"]\n };\n });\n})();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":155,"value":"newfilledData = (function() {\n let result = [];\n let grouped = d3.group(newreorderedData, function(d) { return d.Country; });\n grouped.forEach(function(records, country) {\n let electionsByYear = d3.group(records, function(d) { return d.Year; });\n let yearsWithElection = Array.from(electionsByYear.keys()).sort(function(a, b) { return a - b; });\n let startYear = yearsWithElection[0];\n let endYear = yearsWithElection[yearsWithElection.length - 1];\n let currentElection = null;\n for (let year = startYear; year <= endYear; year++) {\n if (electionsByYear.has(year)) {\n currentElection = electionsByYear.get(year);\n }\n if (currentElection) {\n currentElection.forEach(function(record) {\n let newRecord = Object.assign({}, record, { Year: year });\n result.push(newRecord);\n });\n }\n }\n });\n \n return result;\n})();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":493,"value":"{\n const distinctCountries = Array.from(new Set(newfilledData.map(d => d.Country.trim().toLowerCase())));\n console.log(\"Distinct normalized countries in CSV:\", distinctCountries);\n return md`Distinct normalized countries in CSV: ${distinctCountries.join(\", \")}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":888,"value":"**Below is the initial wrangling for the energy data**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":885,"value":"estat_nrg_bal_s = await FileAttachment(\"estat_nrg_bal_s.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"estat_nrg_bal_s","database":"$undefined"},{"id":884,"value":"Given the simple EU energy balance data above, we can begin by filtering out the frequency column, as all reported data is annual here. We will also want to convert all units to GWH. 1 KTOE is conventionally equal to 0.01163 GWH, and 1 TJ is equal to 0.2777777 (repeating) GWH. We can also create keys for country, siec fuel type, and energy balance codes, as seen below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":883,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":882,"value":"siec_fuel_codes = [\n {code: 'TOTAL', name: 'Total'},\n {code: 'C0000X0350-0370', name: 'Solid fossil fuels'},\n {code: 'C0350-0370', name: 'Manufactured gases'},\n {code: 'P1000', name: 'Peat and peat products'},\n {code: 'S2000', name: 'Oil shale and oil sands'},\n {code: 'G3000', name: 'Natural gas'},\n {code: 'O4000XBIO', name: 'Oil and petroleum products (excluding biofuel portion)'},\n {code: 'RA000', name: 'Renewables and biofuels'},\n {code: 'W6100_6220', name: 'Non-renewable waste'},\n {code: 'N900H', name: 'Nuclear heat'},\n {code: 'E7000', name: 'Electricity'},\n {code: 'H8000', name: 'Heat'}\n];\n// codes to translte from siec fuel type codes to proper names (Electricity and Heat are direct imports, while the others are fuel types)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":881,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":880,"value":"Having created these reference arrays, we can now pivot the year data such that each year gets its own row, per country+nrg_bal+siec combo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":879,"value":"pivot_years_to_tidy = (data) => {\n return data.flatMap(({nrg_bal, siec, unit, 'geo\\\\TIME_PERIOD': country, ...years }) =>\n Object.entries(years).map(([year, value]) => ({nrg_bal, siec, unit, country, year, value}))\n );\n };\n// here we make the data \"tidy\" by shifting year info from a series of consecutive years in a single country row to unique rows for each country + year ( + siec balance) combo","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":878,"value":"nrg_bal_s_tidy = pivot_years_to_tidy(estat_nrg_bal_s)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":877,"value":"Finally, we apply the name arrays from above and convert all units to GWH","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":876,"value":"nrg_bal_s_tidy_named = aq.from(nrg_bal_s_tidy).rename({\"value\":\"energy balance value\", \"country\":\"country code\", \"siec\":\"siec code\", \"nrg_bal\":\"nrg_bal code\"})\n .join_full(aq.from(country_codes).rename({\"name\":\"country name\", \"code\":\"country code\"}))\n .join_full(aq.from(siec_fuel_codes).rename({\"name\":\"siec name\", \"code\":\"siec code\"}))\n .join_full(aq.from(nrg_balance_codes).rename({\"name\":\"nrg_bal name\", \"code\":\"nrg_bal code\"}))\n .select(\"country name\", \"year\", \"siec name\", \"nrg_bal name\", \"unit\", \"energy balance value\")\n .objects()\n\n//note: we need to remove all commas from the energy balance value column too","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":910,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1373,"value":"function zeroColons(data) {\n return data.map(d => {\n if (d[\"energy balance value\"] === \": \") {\n return { ...d, [\"energy balance value\"]: 0 };\n }\n return d;\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1385,"value":"nrg_bal_s_tidier_named_conv = convertToGWH(colonless);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":912,"value":"//nrg_bal_s_tidier_named_conv = convertToGWH(zeroColons(nrg_bal_s_tidy_named));\ncolonless = zeroColons(nrg_bal_s_tidy_named);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1004,"value":"nrg_bal_s_tidier_named_conv_no_total = nrg_bal_s_tidier_named_conv.filter(d => d[\"siec name\"] !== \"Total\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":915,"value":"colonless","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":279,"value":"$33","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":506,"value":"{\n function normalizeCountry(name) {\n return name ? name.trim().toLowerCase() : \"\";\n }\n const distinctCountries = Array.from(new Set(newfilledData.map(d => normalizeCountry(d.Country))));\n console.log(\"Distinct normalized countries in CSV:\", distinctCountries);\n const testCountry = \"austria\";\n const yearsForCountry = Array.from(new Set(newfilledData\n .filter(d => normalizeCountry(d.Country) === testCountry)\n .map(d => +d.Year)\n )).sort();\n console.log(`Available years for ${testCountry}:`, yearsForCountry);\n return md`Check the console for distinct country names and available years.`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"eurostatmap = require(\"eurostat-map@4.1.39\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":471,"value":"mutable selectedCountry = \"Austria\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":533,"value":"$34","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":391,"value":"viewof YearSlider = Inputs.range([1991, 2022], {label: \"Year\", step: 1})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":972,"value":"viewof selectedEnergyStat = Inputs.select(possibleEnergyStats, {value: \"Final consumption - energy use\", label: \"Energy Statistic\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"$36","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":774,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":975,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1057,"value":"ActualDataTwo = FileAttachment(\"ActualDataTwo.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1170,"value":"hoveredCountry = null\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1270,"value":"emissionsData = FileAttachment(\"reshaped-emissions4.csv\").csv()\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1309,"value":"getCountryColor = country => {\n const rec = emissionsData.find(\n d => d.Country === country && +d.Year === YearSlider\n );\n if (!rec) return \"#555\"; \n const vals = emissionsData\n .filter(d => +d.Year === YearSlider && d.Country !== \"Russia\")\n .map(d => +d.Emissions);\n const minVal = d3.min(vals), maxVal = d3.max(vals);\n const scale = d3.scaleSequentialPow()\n .domain([minVal, maxVal])\n .exponent(0.3)\n .interpolator(t => d3.interpolateInferno(1 - t))\n .clamp(true);\n\n return scale(+rec.Emissions);\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 2e:"$2d:body"