2b:null 2c:["$","title",null,{"children":"Analyzing African Socioeconomic Trends Through Data Visualization | Ahteshamuddin Mohammed's Workspace | Observable"}] 31:T5d6, This project, titled **"Understanding African Socioeconomic Trends Through Data Visualization",** focuses exclusively on the rich tapestry of African countries. Through extensive data visualization techniques, we have uncovered intriguing patterns and insights that shed light on the intricate relationships between health expenditure, population demographics, economic indicators and Agriculture sector. My exploration of health expenditure as a percentage of GDP revealed significant variations among countries, providing a nuanced understanding of how nations allocate resources to healthcare. The visualizations highlighted both global and regional trends, allowing for a comparative analysis of healthcare priorities. In parallel, my examination of population dynamics, including rural and urban growth, offered a detailed portrait of demographic shifts. The scatter plot comparing rural and urban population growth rates underscored the ongoing global urbanization trend, while the stacked bar graph depicting male and female labor force participation illuminated gender-specific patterns in economic engagement. These visualizations not only contribute to a richer comprehension of global socioeconomic trends but also serve as valuable tools for policymakers, researchers, and the general public to make informed decisions. As we navigate through the visual narratives, we invite the audience to engage with the diverse and dynamic stories uncovered by our data-driven exploration.32:T51e,{ // Filter data for the selected year and exclude specific countries const year = year3.toString(); const excludedCountries = ["Sub-Saharan Africa (developing only)", "Sub-Saharan Africa (all income levels)", "Sub-Saharan Africa excluding South Africa","Sub-Saharan Africa excluding South Africa and Nigeria"]; // Add the names of the countries to be excluded const filteredData = data.filter(d => d.Year === year && !excludedCountries.includes(d["Country Name"])); // Create data arrays for each category const electricityData = filteredData.map(d => +d["Urban population"]); const agricultureData = filteredData.map(d => +d["Rural population"]); // Create country labels const countries = filteredData.map(d => d["Country Name"]); // Create the grouped bar chart data const chartData = [ { x: countries, y: electricityData, type: "bar", name: "Urban population", }, { x: countries, y: agricultureData, type: "bar", name: "Rural population", }, ]; // Layout options const layout = { title: `Urban Vs Rural - ${year3}`, xaxis: { title: "Country" }, yaxis: { title: "Population(Millions)" }, barmode: "group", }; // Create the Plotly chart Plotly.newPlot("mybarChart", chartData, layout); } 33:T57e,The scatterplot analysis provides valuable insights into the contrasting trends in urban and rural population growth across African countries. By the end of time period in the year 2010, following are my observations: **Burkina Faso's** Urbanization Surge: The scatterplot indicates a substantial surge in urban population growth in Burkina Faso, particularly evident towards the year 2010. This suggests a significant urbanization trend, highlighting factors such as urban migration or development initiatives contributing to the rapid increase in urban population. **Liberia's** Unique Rural Population Growth: Liberia stands out as the only country in Africa with the highest recorded rural population growth. This distinctive trend may be influenced by various factors, including agricultural practices, economic activities, or demographic patterns specific to Liberia. **Mauritius'** Minimal Population Growth: Mauritius, in contrast, exhibits the lowest population growth in both rural and urban areas across the entire African continent. This could be attributed to factors such as stable demographic patterns, effective urban planning, or economic conditions impacting population dynamics. These observations underscore the diverse demographic landscapes within Africa, reflecting the complex interplay of socio-economic and geographic factors shaping population trends in individual countries.34:Ta8f,{ // Filter data based on the selected country const filteredData = data.filter(d => d["Country Name"] === countryselect); // Sort the data by the "Year" column in ascending order filteredData.sort((a, b) => a.Year - b.Year); // Set up the SVG dimensions const width = 800; const height = 500; const margin = { top: 20, right: 10, bottom: 30, left: 70 }; // Create an SVG element const svg = d3.create("svg") .attr("width", width) .attr("height", height); // Extract years and population values const years = filteredData.map(d => d.Year); const malePopulation = filteredData.map(d => +d["Population, male"]); const femalePopulation = filteredData.map(d => +d["Population, female"]); // Create scales for x and y axes const xScale = d3.scaleBand() .domain(years) .range([margin.left, width - margin.right]) .padding(0.1); const yScale = d3.scaleLinear() .domain([0, Math.max(d3.max(malePopulation), d3.max(femalePopulation))]) .nice() .range([height - margin.bottom, margin.top]); // Create line functions for male and female populations const lineMale = d3.line() .x((d, i) => xScale(years[i]) + xScale.bandwidth() / 4) .y(d => yScale(d)) .curve(d3.curveMonotoneX); const lineFemale = d3.line() .x((d, i) => xScale(years[i]) + (xScale.bandwidth() * 3) / 4) .y(d => yScale(d)) .curve(d3.curveMonotoneX); // Add lines to the SVG with different colors svg.append("path") .data([malePopulation]) .attr("class", "line-male") .attr("d", lineMale) .attr("stroke", "blue"); // Change the color for male population svg.append("path") .data([femalePopulation]) .attr("class", "line-female") .attr("d", lineFemale) .attr("stroke", "red"); // Change the color for female population // Add x-axis svg.append("g") .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(xScale)); // Add y-axis svg.append("g") .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(yScale)); // Add legend const legend = svg.append("g") .attr("transform", `translate(${width - margin.right - 100},${margin.top})`); legend.append("rect") .attr("width", 18) .attr("height", 18) .attr("fill", "blue"); legend.append("text") .attr("x", 24) .attr("y", 9) .attr("dy", "0.32em") .text("Male "); legend.append("rect") .attr("x", 0) .attr("y",25) .attr("width", 18) .attr("height", 18) .attr("fill", "red"); legend.append("text") .attr("x", 24) .attr("y", 34) .attr("dy", "0.32em") .text("Female "); // Return the SVG node return svg.node(); } 35:T589, { const selectedCountry = countryselect.toString(); const selectedYear = year7.toString(); const selectedCountryData = data.filter(entry => entry["Country Name"] === selectedCountry && entry.Year === selectedYear); const enrollmentPrimaryFemale = selectedCountryData[0]["School enrollment, primary, female (% gross)"]; const enrollmentPrimaryMale = selectedCountryData[0]["School enrollment, primary, male (% gross)"]; const enrollmentSecondaryFemale = selectedCountryData[0]["School enrollment, secondary, female (% gross)"]; const enrollmentSecondaryMale = selectedCountryData[0]["School enrollment, secondary, male (% gross)"]; const barData = [{ x: ['Primary Enrollment, Female', 'Primary Enrollment, Male', 'Secondary Enrollment, Female', 'Secondary Enrollment, Male'], y: [enrollmentPrimaryFemale, enrollmentPrimaryMale, enrollmentSecondaryFemale, enrollmentSecondaryMale], type: 'bar', marker: { color: ['#3498db', '#e74c3c', '#2ecc71', '#f39c12'], }, text: [`${enrollmentPrimaryFemale}%`, `${enrollmentPrimaryMale}%`, `${enrollmentSecondaryFemale}%`, `${enrollmentSecondaryMale}%`], }]; const layout = { title: `School Enrollment by Gender and Level - ${selectedCountry} (${selectedYear})`, xaxis: { title: 'Gender and Level', }, yaxis: { title: 'Enrollment Percentage', }, }; Plotly.newPlot('ba', barData, layout); } 36:T5ff,{ // Filter data based on the selected country const filteredData = data.filter(d => d["Country Name"] === countryselect); // Sort the data by the "Year" column in ascending order filteredData.sort((a, b) => a.Year - b.Year); // Extract years and Age dependency ratio values const years = filteredData.map(d => d.Year); const ageDependencyRatio = filteredData.map(d => d["Age dependency ratio (% of working-age population)"]); const oldAgeDependencyRatio = filteredData.map(d => d["Age dependency ratio, old (% of working-age population)"]); const youngAgeDependencyRatio = filteredData.map(d => d["Age dependency ratio, young (% of working-age population)"]); // Create traces for the three lines const trace1 = { x: years, y: ageDependencyRatio, type: 'scatter', mode: 'lines+markers', name: 'Age dependency ratio', line: { color: 'blue' }, }; const trace2 = { x: years, y: oldAgeDependencyRatio, type: 'scatter', mode: 'lines+markers', name: 'Age dependency ratio, old', line: { color: 'red' }, }; const trace3 = { x: years, y: youngAgeDependencyRatio, type: 'scatter', mode: 'lines+markers', name: 'Age dependency ratio, young', line: { color: 'green' }, }; // Define layout options const layout = { title: `Age Dependency Ratios - ${countryselect}`, xaxis: { title: 'Year' }, yaxis: { title: 'Percentage' }, }; // Create the Plotly chart Plotly.newPlot('zzz', [trace1, trace2, trace3], layout); } 37:Teb7,{{ const width = 950; const height = 500; const legendWidth = 100; const legendHeight = 210; const legendX = width - 100; const legendY = height - 40; const countriesToHide = [""]; const projection = d3.geoEqualEarth().fitExtent([[20, 20], [width - 20, height - 20]], { type: "Sphere" }); const path = d3.geoPath(projection); const color = d3.scaleSequential(d3.interpolateReds).domain([d3.min(filteredData, d => d.value), d3.max(filteredData, d => d.value)]); const countriesInData = new Set(filteredData.map(d => d.Country)); // Change the color scale domain for increased sensitivity // Find the minimum and maximum values in filteredData const minValue = d3.min(filteredData, d => d.value); const maxValue = d3.max(filteredData, d => d.value); // Adjust the color scale domain to increase sensitivity color.domain([minValue, maxValue * 0.9]); const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]) .attr("width", width) .attr("height", height) .on("click", reset); const g = svg.append("g"); const legendScale = d3.scaleLinear() .domain([d3.min(filteredData, d => d.value), d3.max(filteredData, d => d.value)]) .range([legendHeight, 0]); g.append("path").datum({ type: "Sphere" }).attr("fill", "none").attr("stroke", "White").attr("d", path); // Draw paths for African countries const africanCountriesPaths = g.append("g") .selectAll("g") .data(mergedData.features.filter(d => countriesInData.has(d.properties.name))) .enter().append("g"); // Add path elements for countries with tooltips africanCountriesPaths.append("path") .attr("d", path) .attr("fill", d => countriesInData.has(d.properties.name) ? color(parseFloat(d.properties.value)) : "none") .attr("stroke", "black") .attr("stroke-width", 0.5) .on("mouseover", handleMouseOver) .on("mouseout", handleMouseOut) .append("title") .text(d => `${d.properties.name}: GDP growth (annual %) ${parseFloat(d.properties.value).toFixed(3) || 0} %`); svg.append("text").attr("x", width / 2).attr("y", 52).attr("text-anchor", "middle").attr("font-weight", "bold").attr("font-size", "22px").text("Africa GDP growth (annual %)"); const gradient = svg.append("defs").append("linearGradient").attr("id", "gradient").attr("x1", "0%").attr("y1", "100%").attr("x2", "0%").attr("y2", "0%").attr("spreadMethod", "pad"); gradient.selectAll("stop").data(color.ticks().map((t, i, n) => ({ offset: `${100 * i / n.length}%`, color: color(t) }))).enter().append("stop").attr("offset", d => d.offset).attr("stop-color", d => d.color).attr("stop-opacity", 1); svg.append("g").attr("transform", "translate(20,0)").append(() => Legend(color, { title: d => `GDP growth (annual %) ${Year} (%)`, width: 260 })); svg.append("g").attr("transform", `translate(${legendX + legendWidth + 6},${legendY})`).call(d3.axisRight(legendScale).ticks(5)); const zoom = d3.zoom().scaleExtent([1, 4]).on("zoom", zoomed); svg.call(zoom); function reset() { svg.transition().duration(750).call(zoom.transform, d3.zoomIdentity, d3.zoomTransform(svg.node()).invert([width / 2, height / 2])); } function zoomed(event) { const { transform } = event; g.attr("transform", transform); g.attr("stroke-width", 1 / transform.k); } function handleMouseOver(event, d) { // Show tooltip on mouseover const tooltip = d3.select("body").append("div") .attr("class", "tooltip") .style("opacity", 0); tooltip.transition() .duration(200) .style("opacity", .9); tooltip.html(`${d.properties.name}: GDP growth (annual %) ${parseFloat(d.properties.value).toFixed(3) || 0} %`) .style("left", (event.pageX) + "px") .style("top", (event.pageY - 28) + "px"); } function handleMouseOut() { // Hide tooltip on mouseout d3.select(".tooltip").remove(); } return svg.node(); }} 38:T4b9, { // Filter data based on the selected country const filteredData = data.filter(d => d["Country Name"] === countryselect1); // Create traces for the stacked area graph const areaTrace1 = { x: filteredData.map(d => d["Year"]), y: filteredData.map(d => d["Agricultural land (% of land area)"]), fill: 'tozeroy', mode: 'none', name: 'Agriculture, value added (% of GDP)' }; const areaTrace2 = { x: filteredData.map(d => d["Year"]), y: filteredData.map(d => d["Agriculture, value added (% of GDP)"]), fill: 'tonexty', mode: 'none', name: 'Agricultural land (% of land area)' }; // Layout options for the stacked area graph const layout = { title: `Stacked Area Graph - ${countryselect1}`, xaxis: { title: 'Year', type: 'linear', tickmode: 'linear', tick0: filteredData[0].Year, dtick: 1 }, yaxis: { title: 'Percentage', side: 'left', rangemode: 'tozero' }, // Set rangemode to 'tozero' for positive-only y-axis showlegend: true }; // Combine traces for the stacked area graph const combinedData = [areaTrace2, areaTrace1]; // Create the Plotly chart for the stacked area graph Plotly.newPlot('Sk', combinedData, layout); } 39:T436,Overall Growth Trend: Electricity production shows a consistent upward trajectory across all African countries over the analyzed time period (1998-2010). This suggests a collective effort to enhance energy infrastructure and meet the increasing demands of growing economies. South Africa's Initial Lead: At the beginning of the time period, South Africa holds the highest position in electricity production. This aligns with the country's well-established energy infrastructure and economic prominence on the continent. Emergence of North Africa: A noteworthy transition occurs over time, with North Africa emerging as the leading region in electricity production. This shift signifies rapid progress and substantial improvements in energy generation within North African countries. Implications for Regional Development: The rise of North Africa in electricity production implies a regional focus on enhancing energy capabilities. This shift may have broader implications for economic development, industrialization, and overall quality of life in the North African region.3a:T431, { const year2Value = year4.toString(); // Filter data based on the selected year // const filteredData = data.filter(d => d.Year === year2Value); const filteredData = data.filter(d => d.Year === year2Value && !["Sub-Saharan Africa (developing only)", "Sub-Saharan Africa (all income levels)", "Sub-Saharan Africa excluding South Africa", "Sub-Saharan Africa excluding South Africa and Nigeria"] .includes(d["Country Name"])); // Prepare data for the bar graph const barData = [{ x: filteredData.map(d => d["Country Name"]), y: filteredData.map(d => d[`Electricity production (kWh)`]), type: 'bar', marker: { color: 'rgba(41, 128, 185, 0.7)', // You can customize the bar color }, text: filteredData.map(d => d["Country Name"]), }]; // Layout options const layout = { title: `Bar Graph for Electricity production (kWh) - ${year2Value}`, xaxis: { title: 'Country' }, yaxis: { title: 'Electricity production (kWh)' }, }; // Create the Plotly chart Plotly.newPlot('my', barData, layout); } 3b:T5db,The analysis of the contribution to electricity production in North Africa provides valuable insights into the energy landscape of the region. Here are the key findings: Dominant Role of Oil: Oil as a Primary Contributor: The data reveals that oil plays a pivotal role in powering North Africa. It stands out as the primary contributor to electricity production, showcasing its reliability and consistent utilization in meeting the region's energy needs. Significant Natural Gas Utilization: Natural Gas as the Second Major Contributor: Following oil, natural gas emerges as the second major contributor to electricity production. The substantial utilization of natural gas underscores its importance in diversifying the energy mix and ensuring a stable and resilient power supply. Hydroelectric Power's Growing Impact: Growing Contribution of Hydroelectric Power: The data highlights a noteworthy trend – the increasing contribution of hydroelectric power to electricity production. This indicates a positive shift toward renewable energy sources, reflecting efforts to embrace sustainable and environmentally friendly power generation. Overall Increase in Contributions: Progressive Growth Across All Sources: A key observation is the collective growth in the contributions of oil, natural gas, and hydroelectric power over the years. This signifies a comprehensive approach to energy development, where multiple sources are harnessed to meet the region's increasing electricity demands.3c:T7b9,{ // Filter data based on the selected country const filteredData = data.filter(d => d["Country Name"] === countryselect2); // Create traces for the line graph (Electric power consumption vs Electricity production) const lineTrace = { x: filteredData.map(d => d["Year"]), y: filteredData.map(d => d["Electric power consumption (kWh)"]), mode: "lines", name: "Electric power consumption", yaxis: "y1" }; const lineTrace2 = { x: filteredData.map(d => d["Year"]), y: filteredData.map(d => d["Electricity production (kWh)"]), mode: "lines", name: "Electricity production", yaxis: "y2" }; // Create traces for the grouped bar graph const barTraceHydro = { x: filteredData.map(d => d["Year"]), y: filteredData.map(d => d["Electricity production from hydroelectric sources (kWh)"]), name: "Hydroelectric", type: "bar", }; const barTraceGas = { x: filteredData.map(d => d["Year"]), y: filteredData.map(d => d["Electricity production from natural gas sources (kWh)"]), name: "Natural Gas", type: "bar", }; const barTraceOil = { x: filteredData.map(d => d["Year"]), y: filteredData.map(d => d["Electricity production from oil sources (kWh)"]), name: "Oil", type: "bar", }; // Layout options const layout = { title: `Electric Power Consumption vs Electricity Production Over Time - ${countryselect2}`, xaxis: { title: 'Year' }, yaxis: { title: 'Consumption (kWh)', side: 'left' }, yaxis2: { title: 'Production(KWh)', overlaying: 'y', side: 'right' }, barmode: 'group', // Set the bar mode to 'group' for grouped bars margin: { l: 100, r: 20, t: 40, b: 60 }, // Adjust the left margin here }; // Combine traces for the line graph and grouped bar graph const combinedData = [lineTrace, lineTrace2, barTraceHydro, barTraceGas, barTraceOil]; // Create the Plotly chart with subplots Plotly.newPlot('IMP', combinedData, layout); } 3d:T482,{// Get the selected year from the slider const selectedYear = year6.toString(); // Assuming selectedYear holds the selected year // Filter data for the selected year const filteredData = data.filter(entry => entry.Year === selectedYear); // Create a treemap trace for Health expenditure, total (% of GDP) const treemapTraceHealthExpenditure = { type: 'treemap', labels: filteredData.map(entry => entry["Country Name"]), parents: filteredData.map(() => ''), // Assuming all countries are at the same level values: filteredData.map(entry => entry["Health expenditure, total (% of GDP)"]), text: filteredData.map(entry => `${entry["Country Name"]}
Exp: ${entry["Health expenditure, total (% of GDP)"]}%`), hoverinfo: 'text+value', marker: { colors: 'Greens' // You can customize the color scale } }; // Define layout options for the treemap const treemapLayoutHealthExpenditure = { title: `Health expenditure, total (% of GDP) - ${selectedYear}`, }; // Plotly command to create the treemap for Health expenditure Plotly.newPlot('treemapDivHealthExpenditure', [treemapTraceHealthExpenditure] , treemapLayoutHealthExpenditure); }3e:T61e, Visualizing Africa’s Complex Development Story Through extensive data exploration, this project unveils a multidimensional view of Africa’s socioeconomic landscape. The visual tapestry illuminates intricacies around economic growth, demography, education, health, and sustainability across the continent. Notable variations emerge in the agricultural GDP contribution, underscoring the sector’s dominance for nations like Liberia and Sierra Leone. Urbanization narratives challenge assumptions, with North African outliers charting unique pathways. Shifts in working-age and dependency ratios signal Africa’s impending demographic dividend. Positively, primary and secondary school enrollments show promise. However, gender imbalances in youth literacy and workforce participation highlight areas needing policy attention. Diversification into renewable energy demonstrates Africa’s commitment towards sustainable, resilient models. However, health expenditure variations and HIV/AIDS trends in nations like Sierra Leone reveal systemic gaps intersecting infrastructure, affordability and awareness. Population metrics mapped against indicators expose Africa’s diverse realities - from Burkina Faso’s striking urbanization to the uneven spread of socioeconomic gains. By condensing complex statistics into intuitive visual models, this project aims to build stakeholder literacy regarding Africa’s advancement opportunities and persisting equity challenges. The insights can galvanize regional coordination efforts to uplift human capabilities.3f:[{"id":"b09022eb054d50b51412cc1740b9fc7d638178a1d4b42b769612a23660e662de9bbcf84b40418097ae4192bb810bfe9e8bc7040112c18294a7d2cb18cbb34223","href":"https://static.observableusercontent.com/files/b09022eb054d50b51412cc1740b9fc7d638178a1d4b42b769612a23660e662de9bbcf84b40418097ae4192bb810bfe9e8bc7040112c18294a7d2cb18cbb34223","name":"P2.csv","type":"text/csv","lastModified":1716253814737.481,"size":331207},{"id":"d9687523b1cba754db69a93eac9ffe9055cd9f298d8bbf757a2cf06e2690f961dbcc701a92b1da3fc8b25d7ce9dafed5502e77894460a7236215a8970af27138","href":"https://static.observableusercontent.com/files/d9687523b1cba754db69a93eac9ffe9055cd9f298d8bbf757a2cf06e2690f961dbcc701a92b1da3fc8b25d7ce9dafed5502e77894460a7236215a8970af27138","name":"Countries_json (1).json","type":"application/json","lastModified":1716253814737.481,"size":6124554}] 2f:{"id":"963a6c2720f41e14","slug":"understanding-african-socioeconomic-trends-through-data","title":"Analyzing African Socioeconomic Trends Through Data Visualization","thumbnail":"48ea0a5fd3b2561d65590f9bf882480fe2076d4411b43fa1b5badc253a6258a2","access_level":"public","create_time":"$D2024-05-21T01:10:14.737Z","update_time":"$D2025-06-10T15:50:02.364Z","view_time":null,"first_public_time":"$D2024-05-21T01:10:14.737Z","has_unpublished_edits":null,"fork":{"id":"eff6f461b7cc394f","slug":null,"title":"Understanding African Socioeconomic Trends Through Data Visualization","owner":{"id":"5e361c8eabbc0428","login":"ahteshamuddin-mohammed-ws","name":"Ahteshamuddin Mohammed's Workspace"}},"stars":0,"starred":false,"owner":{"type":"team","id":"5e361c8eabbc0428","login":"ahteshamuddin-mohammed-ws","name":"Ahteshamuddin Mohammed's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/3eedaaff478a39aa8224914a8c4739ecf26ccc7c69ed0ae4cf82d843ef161cc2","tier":"starter_2024"},"version":1328,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"cd5297dac7e9eb4b","login":"ahteshamuddin-mohammed","name":"Ahteshamuddin Mohammed","avatar_url":"https://avatars-next.observableusercontent.com/0d7be6a2e6da71dd83658868fbec97bb078f1e16dce179e2c22de6ca7c5c7e79","tier":null}],"members":[{"type":"individual","id":"cd5297dac7e9eb4b","login":"ahteshamuddin-mohammed","name":"Ahteshamuddin Mohammed","avatar_url":"https://avatars-next.observableusercontent.com/0d7be6a2e6da71dd83658868fbec97bb078f1e16dce179e2c22de6ca7c5c7e79","tier":null,"role":"editor"},{"type":"team","id":"5e361c8eabbc0428","login":"ahteshamuddin-mohammed-ws","name":"Ahteshamuddin Mohammed's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/3eedaaff478a39aa8224914a8c4739ecf26ccc7c69ed0ae4cf82d843ef161cc2","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Analyzing African Socioeconomic Trends Through Data Visualization","cells":[{"id":0,"value":"# Analyzing African Socioeconomic Trends Through Data Visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"p2 = await FileAttachment(\"P2.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"p2","database":"$undefined"},{"id":1327,"value":"data = FileAttachment(\"P2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"world_countries = FileAttachment(\"Countries_json (1).json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"Plotly = await require('https://cdn.plot.ly/plotly-latest.min.js')\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"topojson = require('topojson-client@3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"geojson = topojson.feature(world_countries, world_countries.objects.countries)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"import {Legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"mergedData = {\n const map = new Map(filteredData.map(d => [d.Country, d.value]));\n for (const feature of geojson.features) {\n feature.properties.value = map.get(feature.properties.name) || 0;\n }\n return geojson;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":578,"value":"filteredData = {\n \n return data.filter(d => d.Year === Year.toString())\n .map(d => ({Country: d['Country Name'] , value:d['GDP growth (annual %)'] }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1238,"value":"## **Introduction:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1105,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":654,"value":"## *Population distribution and Analysis*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":283,"value":"viewof year3 = Inputs.range([1998, 2010], { value: 1998, label: \"Slide to change year\", step: 1 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":293,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":669,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":665,"value":"The grouped bar graph reveals **North Africa** as the most densely populated region in Africa. A consistent trend emerges across the continent, indicating that rural populations generally outnumber urban populations. However, noteworthy outliers challenge this prevailing pattern. Notable exceptions include **Algeria, South Africa, Ghana, Morocco, and the broader North Africa region**. These outliers suggest unique demographic dynamics in these countries, warranting further investigation into the factors shaping their distinctive urban-rural population landscapes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1202,"value":"viewof year9 = Inputs.range([1998, 2010], { value: 1998, label: \"Slide to change year\", step: 1 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1097,"value":"html`

`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":699,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1095,"value":"{\n const xColumn = \"Urban population growth (annual %)\";\n const yColumn = \"Rural population growth (annual %)\";\n const selectedYear = year9.toString(); \n\n // Filter data for the selected year\n const filteredData = data.filter(entry => entry.Year === selectedYear);\n\n // Prepare data for the scatter plot or bubble plot\n const scatterData = [{\n x: filteredData.map(d => +d[xColumn]),\n y: filteredData.map(d => +d[yColumn]),\n mode: 'markers',\n text: filteredData.map(d => d[\"Country Name\"]),\n marker: {\n size: filteredData.map(d => Math.sqrt(+d[yColumn]) * 11), // Adjust the multiplier for a larger bubble size\n color: 'rgba(500, 0, 0, 0.7)', // You can customize the bubble color\n },\n type: 'scatter'\n }];\n\n // Layout options\n const layout = {\n title: `Bubble Plot for ${yColumn} vs. ${xColumn} - ${selectedYear}`,\n xaxis: { title: xColumn },\n yaxis: { title: yColumn },\n };\n\n // Create the Plotly chart\n Plotly.newPlot('myScatterPlo', scatterData, layout);\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1140,"value":"**Male vs Female population growth** \n\n**Note:** I have selected **Burkina Faso** from the dropdown as it was an outlier in earlier visualization and wrote the relevant comments below visualization. However, you can choose any country you want and investigate it accordingly.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"viewof countryselect = Inputs.select([...new Set(data.map(d => d[\"Country Name\"]))], { label: \"Select a country\" });\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":772,"value":"As Burkina Faso was one of the outliers in Urbanization. I wanted to see gender contribution in population growth. So, this plot is analysis of population distribution among male and female over the time period in the country Burkina Faso.\n\nBoth male and female population are almost equal. However, Female population is more with small extent compared to Male population. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":807,"value":"**Labor force composition among different age groups**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":467,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":802,"value":"The analysis of labor composition in two age groups, 15 to 24 and 15 to 64, provides intriguing insights into the dynamics of workforce participation. Here are the key observations:\n\n**Gender Disparity in Labor Force Participation:**\n\nThe pie charts highlight a significant gender disparity in labor force engagement, with women constituting the dominant share of the population. Despite their numerical majority, women are observed to be less involved in the labor force compared to men.\n\n**Age-Related Trends:**\n\nIncreasing Female Workforce with Age: As the age group increases from 15 to 24 to 15 to 64, there is a noticeable rise in the proportion of working females. This suggests that more women are entering the labor force as they transition to older age groups, reflecting changing societal and economic dynamics.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":465,"value":"{\n\nconst filteredData = data.filter(d => d[\"Country Name\"] === countryselect);\n\nconst malePercentage = filteredData.map(d => d[\"Labor force (15-24 years), male (% of total labor force 15-24 years)\"])[0];\nconst femalePercentage = filteredData.map(d => d[\"Labor force (15-24 years), female (% of total labor force 15-24 years)\"])[0];\n\nconst pieData = [{\n labels: ['Male', 'Female'],\n values: [malePercentage, femalePercentage],\n type: 'pie',\n marker: {\n colors: ['#3498db', '#e74c3c'],\n },\n textinfo: 'percent+label',\n textposition: 'inside',\n}];\n\nconst pieLayout = {\n title: `Labor Force Composition(15-24 years) - ${countryselect}`,\n};\n\nPlotly.newPlot('myPieChart1', pieData, pieLayout);\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":459,"value":"{\n // Replace with the desired country\nconst filteredData = data.filter(d => d[\"Country Name\"] === countryselect);\n\nconst malePercentage = filteredData.map(d => d[\"Labor force (15-64 years), male (% of total labor force 15-64 years)\"])[0];\nconst femalePercentage = filteredData.map(d => d[\"Labor force (15-64 years), female (% of total labor force 15-64 years)\"])[0];\n\nconst pieData = [{\n labels: ['Male', 'Female'],\n values: [malePercentage, femalePercentage],\n type: 'pie',\n marker: {\n colors: ['#3498db', '#e74c3c'],\n },\n textinfo: 'percent+label',\n textposition: 'inside',\n}];\n\nconst pieLayout = {\n title: `Labor Force Composition(15-64 years) - ${countryselect}`,\n};\n\nPlotly.newPlot('myPieChart', pieData, pieLayout);\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1143,"value":"**School Enrollment by Gender and Level**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1065,"value":"viewof year7 = Inputs.range([1999, 2010], { value: 1999, label: \"select Year\", step: 1 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1015,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1138,"value":"Overall Enrollment Growth:\nThe graph illustrates a positive trajectory in school enrollment for both males and females over the specified period. This growth is observed in both primary and secondary education levels, reflecting a general improvement in access to education.\n\nGender Disparity in Secondary Education:\nOne intriguing observation is the persistent gender gap in secondary school enrollment. Despite the overall growth in enrollments, male students consistently outnumber their female counterparts in secondary education. This points to a specific challenge or pattern in educational access and participation between genders, particularly in higher levels of schooling.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1009,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":620,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":813,"value":"Here are the key observations:\n\nOverall Positive Trend:\nThe graph portrays a positive trajectory in the overall age dependency ratio from 1998 to 2010. This indicates a favorable demographic shift characterized by a decrease in the burden on the working-age population to support dependents.\n\nDecrease in Young Age Dependency:\nA notable decline in the age dependency ratio for young people is observed over the specified time frame. This implies a reduction in the proportion of the population requiring support from the working-age segment, contributing positively to the country's demographic structure.\n\nSlight Increase in Old Age Dependency:\nWhile the overall age dependency ratio is on the decline, there is a marginal increase in the age dependency ratio among older individuals. This suggests a subtle shift in the demographic composition, possibly influenced by factors such as increased life expectancy.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":618,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"viewof Year = Inputs.range([1998, 2010], { value: 1996, label: \"Year\", step: 1 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":848,"value":"## *Agriculture Indicators Analysis*\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1114,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"*GDP growth across Africa*\n\nThere was a huge change in GDP growth in all the countries over the period of 1998 to 2010.\n\n1) The countries with positive growth in their GDPs were **Algeria, Ghana, Chad, Ethiopia, and Burkina Faso**.\n \n2) The countries with uniform GDP were **Tanzania, Liberia and Mali.**\n \n3) The countries with major downfalls in their GDP's were **Mozambique, Morocco, and Botswana**.\n \n4) Overall, the GDPs of almost all the countries fell in 2009; a potential reason for this may be the economic recession. \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1253,"value":"**Agriculture value added to overall GDP growth**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":832,"value":"viewof year2 = Inputs.range([1998, 2010], { value: 1998, label: \"Select Year\", step: 1 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":823,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":827,"value":"\n\n{\n const year = year2.toString();\n const column = \"Agriculture, value added (% of GDP)\";\n\n // Filter data for the selected year\n const filteredData = data.filter(entry => entry.Year === year);\n\n // Prepare data for the dot plot\n const dotData = [{\n y: filteredData.map(d => d[\"Country Name\"]),\n x: filteredData.map(d => d[column]),\n mode: 'markers',\n type: 'scatter',\n marker: {\n size: 10,\n color: 'rgba(10, 10, 250, 0.7)', // You can customize the dot color\n },\n text: filteredData.map(d => d[\"Country Name\"]),\n }];\n\n // Layout options\n const layout = {\n title: ` ${column} - ${year}`,\n yaxis: { title: 'Country' },\n xaxis: { title: column },\n };\n\n // Create the Plotly chart\n Plotly.newPlot('ABC', dotData, layout);\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":853,"value":"Here are the key observations:\n\nLiberia's Dominance:\nLiberia consistently stands out as a top performer, making a substantial and consistent contribution to its GDP through the agriculture sector. This trend underscores the economic significance of agriculture in Liberia's overall development.\n\nSierra Leone's Noteworthy Performance:\nSierra Leone also emerges as a noteworthy performer in terms of agriculture value added to GDP. The sustained positive contribution from Sierra Leone's agriculture sector suggests a stable and impactful role in the country's economic makeup.\n\nRegional Strength:\nThe presence of both Liberia and Sierra Leone as standout performers implies a positive regional trend. The proximity of these neighboring countries suggests a shared strength and significance of agriculture in shaping the economic landscape of this specific African region.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1262,"value":"Note: I have selected **Liberia** here since it is outlier in the above visualization for my further investigation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":878,"value":"viewof countryselect1 = Inputs.select([...new Set(data.map(d => d[\"Country Name\"]))], { label: \"Select a country\" });\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":233,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":228,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":897,"value":"Given that Liberia boasts the highest contribution of agriculture value added to its economy in Africa, a closer examination was conducted to discern any correlation with its agricultural land. Surprisingly, the visualization suggests an inverse relationship between the increase in agricultural land and the value added to the economy. Contrary to expectations, the data implies that a rise in agricultural land does not necessarily translate to a proportional increase in economic value added. This intriguing observation prompts further exploration into the multifaceted factors influencing Liberia's agricultural sector and economic dynamics.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1187,"value":"## *Electricity Production and Analysis*\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":912,"value":"viewof year4 = Inputs.range([1998, 2010], { value: 1998, label: \"Select Year\", step: 1 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":910,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":925,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":907,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1265,"value":"Note: I have selected **North Africa** for my further investigation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":903,"value":"viewof countryselect2 = Inputs.select([...new Set(data.map(d => d[\"Country Name\"]))], { label: \"Select a country\" });\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":194,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":939,"value":"$3b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1175,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1193,"value":"## *Health sector Analysis*\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1017,"value":"viewof year6 = Inputs.range([1998, 2010], { value: 1998, label: \"Year\", step: 1 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1022,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1002,"value":"Correlation Between HIV Prevalence and AIDS Deaths:\n\nThe visualization indicates a direct relationship between the prevalence of HIV and the number of deaths caused by AIDS. As the prevalence of HIV increases, there is a corresponding rise in AIDS-related deaths.\nSignificant Health Challenge:\n\nSierra Leone faces a considerable health challenge due to the HIV/AIDS epidemic. The increasing prevalence suggests transmission of the virus within the population, leading to a higher number of AIDS-related fatalities.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":504,"value":"\n{\n const selectedColumn= radios.toString();\n const selectedCountry = countryselect3.toString();\n // Filter data for the selected country\n const filteredData = data.filter(entry => entry[\"Country Name\"] === selectedCountry);\n\n const trace = {\n x: filteredData.map(d => d.Year),\n y: filteredData.map(d => +d[selectedColumn]),\n type: 'scatter',\n mode: 'lines+markers',\n name: selectedColumn,\n };\n\n const layout = {\n title: `${selectedColumn} Over the Years - ${selectedCountry}`,\n xaxis: { title: 'Year' },\n yaxis: { title: selectedColumn },\n };\n\n Plotly.newPlot('mal', [trace], layout);\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1271,"value":"## Conclusion","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1291,"value":"$3e","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W3f","stdlib":"1"}} 30:"$2f:body"