3e:null 3f:["$","title",null,{"children":"FORKED PERE WORKING COPY - Exploratory Data Analysis on Renewable Energy | pere roca ristol | Observable"}] 45:Tbfe,cluster = { const height = 300; const maxRadius = 40; const margin = 100; const xScale = d3.scaleBand() .domain([...new Set(joinedData.filter(d=>d.year>2010).map(d => d.renewablesConsPerCap))]) .range([margin , width - margin]); const radiusScale = d3.scaleSqrt() .domain([0,d3.max(joinedData, d => d.gdp)]) .range([0, maxRadius]); const colorScale = d3.scaleOrdinal() .domain([...new Set(joinedData.map(d => d.continent))]) .range(d3.schemeCategory10); const simulation = d3.forceSimulation(joinedData) .force('x', d3.forceX().x(d => xScale(d.renewablesConsPerCap))) .force('y', d3.forceY().y(height/2)) .force('collision', d3.forceCollide().radius( d =>radiusScale(d.gdp) + 2)); const tooltip = d3.select('body') .append('div') .attr('id', 'scatterplot-tooltip') .style('position', 'absolute') .style('z-index', '1') .style('visibility', 'hidden') .style('padding', '10px') .style('background', 'rgba(0,0,0,0.6)') .style('border-radius', '4px') .style('color', '#fff'); const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]); const node = svg.selectAll('circle') .data(joinedData) .join('circle') .attr('r', d => radiusScale(d.gdp)) .attr('fill', d => colorScale(d.continent)) .attr('fill-opacity', 0.4) .on("mouseover", function(e,d) { const tooltipWidth = tooltip.node().offsetWidth; const tooltipHeight = tooltip.node().offsetHeight; tooltip .style("left", e.pageX - tooltipWidth/2 +'px') .style("top", e.pageY-tooltipHeight - 10+'px') .style('visibility', 'visible') .html(`Country: ${d.country}
Year: ${d.year}
GDP per cap: ${d.gdp.toLocaleString('en-US', {maximumFractionDigits: 2})}
Renewable Consumption per cap: ${d.renewablesConsPerCap.toLocaleString('en-US', {maximumFractionDigits: 2})}`); d3.select(this).attr("fill", "gray").attr("stroke", "black").attr("stroke-width", 1);}) .on('mousemove', function(e) { const tooltipWidth = tooltip.node().offsetWidth; const tooltipHeight = tooltip.node().offsetHeight; tooltip .style("left", e.pageX - tooltipWidth/2 +'px') .style("top", e.pageY-tooltipHeight - 10+'px') }) .on("mouseout", function(e,d) { tooltip .style('visibility', 'hidden') d3.select(this).attr("fill", d => colorScale(d.continent)) .attr("stroke", "none");}) simulation.on("tick", () => { node .attr("cx", d => d.x) .attr("cy", d => d.y); }); return svg.node(); } 46:Td47,//PLOT MY BEESWARM - CONSUMPTION (Per capita primary energy consumption from renewables, measured in kilowatt-hours) exercise = { const height = 300; const maxRadius = 30; const margin = 100; const axismargin = 20; const xScale = d3.scaleLinear() .domain([0, d3.max(joinedData, d => d.renewablesConsPerCap)]) .range([margin , width - margin]); const radiusScale = d3.scaleSqrt() .domain([0,d3.max(joinedData, d => d.gdp)]) .range([0, maxRadius]); const colorScale = d3.scaleOrdinal() .domain([...new Set(joinedData.map(d => d.continent))]) .range(d3.schemeCategory10); const xAxis = g => g .attr("transform", `translate(0,${height - axismargin})`) .call(d3.axisBottom(xScale)); //TO DO!!! const simulation = d3.forceSimulation(joinedData) .force('x', d3.forceX().x(d => xScale(d.renewablesConsPerCap))) .force('y', d3.forceY().y(height/2)) .force('collision', d3.forceCollide().radius( d =>radiusScale(d.gdp) + 1).strength(1)) //TO DO!!! const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]); svg.append("g") .call(xAxis); const tooltip = d3.select('body') .append('div') .attr('id', 'scatterplot-tooltip') .style('position', 'absolute') .style('z-index', '1') .style('visibility', 'hidden') .style('padding', '10px') .style('background', 'rgba(0,0,0,0.6)') .style('border-radius', '4px') .style('color', '#fff'); const node = svg.selectAll('circle') .data(joinedData) .join('circle') .attr('r', d => radiusScale(d.gdp)) .attr('fill', d => colorScale(d.continent)) .attr('fill-opacity', 0.4) .attr('stroke', d => colorScale(d.continent)) .on("mouseover", function(e,d) { const tooltipWidth = tooltip.node().offsetWidth; const tooltipHeight = tooltip.node().offsetHeight; tooltip .style("left", e.pageX - tooltipWidth/2 +'px') .style("top", e.pageY-tooltipHeight - 10+'px') .style('visibility', 'visible') .html(`Country: ${d.country}
GDP per cap: ${d.gdp.toLocaleString('en-US', {maximumFractionDigits: 2})}
Renewable Consumption per cap: ${d.renewablesConsPerCap.toLocaleString('en-US', {maximumFractionDigits: 2})}`); d3.select(this).attr("fill", "gray").attr("stroke", "black").attr("stroke-width", 1);}) .on('mousemove', function(e) { const tooltipWidth = tooltip.node().offsetWidth; const tooltipHeight = tooltip.node().offsetHeight; tooltip .style("left", e.pageX - tooltipWidth/2 +'px') .style("top", e.pageY-tooltipHeight - 10+'px') }) .on("mouseout", function(e,d) { tooltip .style('visibility', 'hidden') d3.select(this).attr("fill", d => colorScale(d.continent)) .attr("stroke", "none");}) simulation.on("tick", () => { node .attr("cx", d => d.x) .attr("cy", d => d.y); }); return svg.node(); } 47:Tc01,chart = { const height = 1100 const margin = ({ left: 120, right: 120, top: 100, bottom: 0 }) const y = d3.scaleBand() .domain(edat) .range([height + margin.bottom -50, margin.top-50]) const x = d3.scaleLinear() .domain([0,1300]) .nice() .range([margin.left, width - margin.right]) const xAxis = g => g .attr('transform', `translate(0,${height +30 - margin.top})`) .call(d3.axisBottom(x)) const colorScale = d3.scaleOrdinal() .domain([...new Set(joinedData.map(d => d.continent))]) .range(d3.schemeCategory10); const popExtent = function() { var bounds = data.map((d) => d.visites).sort(d3.ascending); return [bounds[0], bounds[bounds.length - 1]]; } const size = d3.scaleLinear() .domain(popExtent()) .nice() .range([2, 15]) const dataExtent = function() { var bounds = data.map((d) => d.visites).sort(d3.ascending); return [bounds[0], bounds[bounds.length - 1]]; } const color = d3.scaleOrdinal() .domain(sexe) .range(d3.schemeTableau10) const svg = d3.create('svg') .attr('viewBox', [0, 0, width, height]); svg.append('g').call(xAxis); svg.selectAll('.line-visites') .data(x.ticks()) .join('line') .attr('class', 'line-visites') .attr('x1', d => x(d)) .attr('x2', d => x(d)) .attr('y1', 10) .attr('y2', height - margin.top) .attr('stroke-width', .6) .attr('stroke', 'lightgray'); svg.selectAll('.label-edat') .data(continentsArray) .join('text') .attr('class', 'label-edat') .attr('x', 0) .attr('y', d => y(d)) .attr('text-align', 'right') .attr('alignment-baseline', 'right') .text(d => d); const simulation = d3.forceSimulation(joinedData) .force('x', d3.forceX((d) => x(d.pacients)).strength(0.1)) .force('y', d3.forceY((d) => y(d.grups_edat)).strength(0.1)) .force('collide', d3.forceCollide(d => size(d.visites)/6 + padding).strength(1)) .stop(); for(let i = 0; i < 300; i++) { simulation.tick(); } simulation.stop(); svg.selectAll('circle') .data(joinedData) .join('circle') .attr('cx', d => d.x) .attr('cy', d => d.y) .attr('r', d => size(d.visites)/6) .attr('fill', d => color(d.sexe)) .attr('opacity', .8); // svg.selectAll("circle") // .on("mouseover", function(d) { // d3.select(this) // .attr('stroke-width', '1') // .attr("stroke", "black") // tooltip // .style("visibility", "visible") // .html(`Franja d'edat: ${d.grups_edat}
Sexe: ${d.sexe}
Centre: ${d.centre}
Pacients: ${d.pacients}
Visites: ${(d.visites)}`) // }) // .on("mousemove", function() { // tooltip // .style("top", d3.event.pageY - 10 + "px") // .style("left", d3.event.pageX + 10 + "px") // }) // .on("mouseout", function() { // d3.select(this).attr('stroke-width', '0'); // tooltip.style("visibility", "hidden"); // }); return svg.node(); }48:Tf60,scatterPlot = { const height = 500; const width = 600; const margin = ({top: 30, right: 10, bottom: 40, left: 50}); const yScale = d3.scaleLog() .domain(d3.extent(joinedData, d => d.renewablesGenPerCap)) .range([height - margin.bottom, margin.top]); const xScale = d3.scaleLog() .domain(d3.extent(joinedData, d => d.renewablesConsPerCap)) .range([margin.left, width - margin.right]); const rScale = d3.scaleSqrt() .domain([0, d3.max(joinedData, d => d.gdp)]) .range([0, 30]); const colorScale = d3.scaleOrdinal() .domain([...new Set(joinedData.map(d => d.continent))]) .range(d3.schemeCategory10); const xAxis = g => g .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(xScale) .ticks(width / 80) .tickSizeOuter(0) ) const yAxis = g => g .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(yScale)) .call(g => g.select(".domain").remove()) .call(g => g.select(".tick:last-of-type text").clone() .attr("x", 25) .attr("y", -25) .attr("text-anchor", "end") .text("Renewables Generation") ) const svg = d3.create("svg") .attr("width", width) .attr("height", height) .attr("viewBox",[0, 0, width, height]); svg.append("g") .call(xAxis) .call(g => g.append("text") .attr("x", width) .attr("y", margin.bottom - 10) .attr("fill", "currentColor") .attr("text-anchor", "end") .text("Renewables Consumption")); svg.append("g") .call(yAxis); const tooltip = d3.select('body') .append('div') .attr('id', 'scatterplot-tooltip') .style('position', 'absolute') .style('z-index', '1') .style('visibility', 'hidden') .style('padding', '10px') .style('background', 'rgba(0,0,0,0.6)') .style('border-radius', '4px') .style('color', '#fff'); svg.selectAll('circle') .data(joinedData) .join('circle') .attr('cx', d => xScale(d.renewablesConsPerCap)) .attr('cy', d => yScale(d.renewablesGenPerCap)) .attr('r', d => rScale(d.gdp)) .style('fill', d => colorScale(d.continent)) .style('opacity', 0.8) .on("mouseover", function(e,d) { const tooltipWidth = tooltip.node().offsetWidth; const tooltipHeight = tooltip.node().offsetHeight; tooltip .style("left", e.pageX - tooltipWidth/2 +'px') .style("top", e.pageY-tooltipHeight - 10+'px') .style('visibility', 'visible') .html(`Country: ${d.country}
Generation: ${d.renewablesGenPerCap.toLocaleString('en-US', {maximumFractionDigits: 2})}
Consumption: ${d.renewablesConsPerCap.toLocaleString('en-US', {maximumFractionDigits: 2})}
GDP: ${d.gdp.toLocaleString('en-US', {maximumFractionDigits: 2})}`); d3.select(this).attr("fill", "gray").attr("stroke", "black").attr("stroke-width", 1);}) .on('mousemove', function(e) { const tooltipWidth = tooltip.node().offsetWidth; const tooltipHeight = tooltip.node().offsetHeight; tooltip .style("left", e.pageX - tooltipWidth/2 +'px') .style("top", e.pageY-tooltipHeight - 10+'px') }) .on("mouseout", function(e,d) { tooltip .style('visibility', 'hidden') d3.select(this).attr("fill", d => colorScale(d.continent)) .attr("stroke", "none");}) return svg.node(); }49:[{"id":"b01523abb8e3fe07768519db641a0ac448801d0ec4c2c5f3138519f668f7341852c4fd03026feb8545fc4af0dda95df0cf51e7c3f399af353fc494593034c38f","href":"https://static.observableusercontent.com/files/b01523abb8e3fe07768519db641a0ac448801d0ec4c2c5f3138519f668f7341852c4fd03026feb8545fc4af0dda95df0cf51e7c3f399af353fc494593034c38f","name":"energyData.csv","type":"text/csv","lastModified":1672468696854.569,"size":11188945},{"id":"18f11075e53dd3d1cbd8b0fc805dba6f2df75ea494a5a75f10cfe553ef7809c2397b43ecba9be317834cc3c8d1073c16e7429a462969b3c448e49466e3cc2d10","href":"https://static.observableusercontent.com/files/18f11075e53dd3d1cbd8b0fc805dba6f2df75ea494a5a75f10cfe553ef7809c2397b43ecba9be317834cc3c8d1073c16e7429a462969b3c448e49466e3cc2d10","name":"continents.csv","type":"text/csv","lastModified":1672468696854.569,"size":8298}] 43:{"id":"12995786d3f18380","slug":null,"title":"FORKED PERE WORKING COPY - Exploratory Data Analysis on Renewable Energy","thumbnail":"bb378971cb4466467a8cc259f98b2b07401e6b9e1ba7216b9c731306f4b16d54","access_level":"public","create_time":"$D2022-12-31T06:38:16.854Z","update_time":"$D2023-01-05T14:34:13.434Z","view_time":null,"first_public_time":"$D2022-12-31T06:38:16.854Z","has_unpublished_edits":null,"fork":{"id":"b92e2526b9c85f8b","slug":null,"title":"WORKING COPY - Exploratory Data Analysis on Renewable Energy","owner":{"id":"05f6a23615a11f71","login":"charlenechua","name":"Charlene Chua"}},"stars":0,"starred":false,"owner":{"type":"team","id":"c5215dd716ed8e17","login":"perikut","name":"pere roca ristol","avatar_url":"https://avatars-next.observableusercontent.com/56ba36bd2e20eeb52df640d17ea1083f66526c5beef466fdb3f528cc3ea0e0e0","tier":"starter_2024","bio":"","following":false},"version":741,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"f5f56b36d10ccd0b","login":"perikut","name":"pere roca ristol","avatar_url":"https://avatars-next.observableusercontent.com/56ba36bd2e20eeb52df640d17ea1083f66526c5beef466fdb3f528cc3ea0e0e0","tier":null,"bio":"","following":false}],"members":[{"type":"individual","id":"f5f56b36d10ccd0b","login":"perikut","name":"pere roca ristol","avatar_url":"https://avatars-next.observableusercontent.com/56ba36bd2e20eeb52df640d17ea1083f66526c5beef466fdb3f528cc3ea0e0e0","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"FORKED PERE WORKING COPY - Exploratory Data Analysis on Renewable Energy","cells":[{"id":4,"value":"# FORKED PERE WORKING COPY - Exploratory Data Analysis on Renewable Energy","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":28,"value":"Milestones\n8 Dec | Week of 11-17 Dec | Week of 18-23 Dec | Week of 3-9 Jan\n- EDA / try out dataset(?) (*Start* today, due 14 Dec) Everyone\n- Observable notebook - rough working copy (8 - 14 Dec) Everyone \n- Regroup - confirm dataset & initial discussion on story angle **Done**\n- Initial Svelte project setup in github **Done** Link here: https://github.com/thedatacurious/renewable-energy-project\n- Test deployment to vercel **Done** Link here: https://renewable-energy-project.vercel.app/\n- Data processing for svelte.app (14 - 26 Dec) \n- Iterate/tweak story angle, viz mockups (14 - 26 Dec)\n- Start Dev (26 Dec)\n- Version 1 (By 3 Jan)\n- **Regroup & check in**\n- Version 2 (By 6 Jan)\n- Observable notebook - cleaned up version for submission (By 8 Jan)\n- Presentation flow (& slides?) (By 8 Jan)\n\n Need buffer for debugging + polishing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"### Possible questions / story angle\n\nSuggestions from project brief: \n- Is renewable energy related to economic indicators like GDP, income or inequality? Scatterplot of GDP vs % of renewable energy usage, split by region? OR Beeswarm @charlene\n \n- Biggest donors and biggest recipients over time? **Does that affect the % of renewable energy usage?** Sankey of Bilateral finance flows? @alex\n- Possible \"exploitation\"?\n\n- Types of renewable energy? @pere\n\n BONUS: Any global/national targets and are these targets being met?\n\n Started some research here: https://docs.google.com/document/d/1ivUh2zozsFeWJqYyaKns1QfxUu6Tos7l8DIowI98bp8/edit\n\n - https://visualstudio.microsoft.com/services/live-share/","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":253,"value":"## Visual inspo\nhttps://salaries.texastribune.org/departments/texas-department-of-criminal-justice/ \nhttps://www.straitstimes.com/multimedia/graphics/2022/04/travel-safe-covid19-vaccination/index.html?stextension ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"### Possible data sources","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":49,"value":"- https://energydata.info/dataset?q=renewable&sort=score+desc%2C+metadata_modified+desc\n- https://www.iea.org/data-and-statistics/data-product/sdg7-database#energy-intensity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":264,"value":"## TASK: \nIs renewable energy related to economic indicators like GDP, income or inequality? Scatterplot of GDP vs % of renewable energy usage, split by region? OR Beeswarm @charlene","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":718,"value":" d=function ()\n {\n //console.warn(continentsDataClean)\n //debugger\n \n }","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":723,"value":"d()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":275,"value":"energyData = FileAttachment(\"energyData.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":283,"value":"viewof table = Inputs.table(energyData)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"energyData","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":365,"value":"import { aq, op } from '@uwdata/arquero'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":359,"value":"energyClean = energyData.map(d => { \n var newd = {\n country : d.country,\n year : d.year,\n iso_code : d.iso_code,\n gdp : d.gdp,\n renewablesShare : d.renewables_share_energy,\n renewablesConsPerCap: d.renewables_energy_per_capita,\n renewablesGenPerCap: d.renewables_elec_per_capita,\n renewablesCons: d.renewables_consumption,\n renewablesConsChangePercPct: d.renewables_cons_change_pct,\n renewablesConsChangePercTwh: d.renewables_cons_change_twh \n}\n return newd;\n}).filter((d) =>\n d.gdp >= 0 && d.renewablesShare >= 0 &&\n d.year === 2018 &&\n d.country !== \"World\"\n )\n;\n\n\n//seems to only have latest data from 2018.","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":483,"value":"energyCleanArq = aq.from(energyClean)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":441,"value":"continentsData = FileAttachment(\"continents.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":445,"value":"continentsDataClean = continentsData.map(d => { \n var newd = {\n country : d.Entity,\n iso_code : d.Code,\n continent : d.Continent\n }\n return newd;\n}\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":533,"value":"continentsCleanArq = aq.from(continentsDataClean)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":516,"value":"joinedData = energyCleanArq.join_left(continentsCleanArq).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":738,"value":"[...new Set(joinedData.filter(d=>d.year>2010).map(d => d.renewablesConsPerCap))]\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":668,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":615,"value":"### Beeswarm with plotting renewable consumption\nIceland consumes the most per cap (It is an anomaly).
\nFollowed by Norway, Canada, Sweden, New Zealand. Mostly colder countries.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":590,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":626,"value":"### Beeswarm with plotting renewable consumption on different x-axis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":675,"value":"//create array of continents\ncontinents = new Set(joinedData.map(d => d.continent))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":682,"value":"continentsArray = Array.from(continents);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":692,"value":"gdpExtent = {\n const bounds = joinedData.map((d) => d.gdp).sort(d3.ascending);\n return [bounds[0], bounds[bounds.length - 1]];\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":686,"value":"$47","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"### Scatterplot with plotting renewable consumption (x axis) and renewable generation (y axis)
\nColor by continent.
\nHoping to see if there's any inequality in terms of generation vs consumption.
\nThe ones in the lower range are less developed countries.\n\nAdd how this has changed over time?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":631,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":462,"value":"import {serialize} from \"@palewire/saving-csv\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":465,"value":"DOM.download(serialize(energyClean), null, \"Download CSV\")\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":390,"value":"energyCleanContinent = energyData.map(d => { \n var newd = {\n country : d.country,\n year : d.year,\n iso_code : d.iso_code,\n gdp : d.gdp,\n renewablesShare : d.renewables_share_energy,\n renewablesSharePerCap: d.renewables_energy_per_capita,\n renewablesCons: d.renewables_consumption,\n renewablesConsChangePercPct: d.renewables_cons_change_pct,\n renewablesConsChangePercTwh: d.renewables_cons_change_twh \n}\n return newd;\n}).filter(\n (d) =>\n !d.country.includes(\"BP\") &&\n !d.country.includes(\"Shift\") && d.iso_code == \"NA\" && d.year >= 2000\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"## Analysis of Energy Dataset\n1900 - 2021\n1. Select/filter rows. Keep Country, year, iso code, pop, gdp, energy per capita, energy per gdp, primary energy consumption, renewables consumption, renewables_energy_per_capita\n2. GDP - remove invalid strings\n3. Remove anamolies from population dataset\n4. **Merge with continents dataset** -- Need to find another dataset with continents, then do a right join\n5. Plot beeswarm & color by continent [2019 data]\n6. Transition beewswarm into split by continents [2019 data]\n7. Plot scatterplot [GDP vs Renewables share per cap] > how it changes across the years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":20,"value":"### Sandbox for Dataviz / EDA","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":249,"value":"https://observablehq.com/d/d812db62ef54d6f3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":128,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":132,"value":"import {Swatches} from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"import {howto, altplot} from \"@d3/example-components\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":293,"value":"import {Pack} from \"@d3/pack\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W49","stdlib":"1"}} 44:"$43:body"