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Add 1 files
Browse files- index.html +57 -13
index.html
CHANGED
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@@ -181,6 +181,13 @@
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font-size: 12px;
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}
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@media (max-width: 768px) {
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.controls {
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flex-direction: column;
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@@ -206,7 +213,7 @@
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<body>
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<div class="container">
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<h1>World Development Indicators</h1>
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<p class="subtitle">Life Expectancy vs. GDP per capita</p>
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<div class="controls">
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<div class="filter-buttons" id="region-buttons">
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@@ -230,6 +237,7 @@
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</div>
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<div class="tooltip" id="tooltip"></div>
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</div>
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<script>
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@@ -255,6 +263,7 @@
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let state = {
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data: null,
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filteredData: null,
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minYear: 1950,
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maxYear: 2023,
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currentYear: 1990,
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@@ -300,18 +309,52 @@
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async function loadData() {
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try {
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// Load the CSV data
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const rawData = await d3.csv("
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//
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-
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return {
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entity: d.Entity,
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code: d.Code,
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year: +d.Year,
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lifeExpectancy: d['Life expectancy - Sex: all - Age: 0 - Variant: estimates'] ?
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};
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}).filter(d =>
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d.lifeExpectancy !== null &&
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@@ -320,7 +363,7 @@
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);
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// Filter out any invalid data points
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state.data =
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!isNaN(d.year) &&
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!isNaN(d.lifeExpectancy) &&
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!isNaN(d.gdpPerCapita) &&
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@@ -529,7 +572,7 @@
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]);
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// Update axes
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const xAxis = d3.axisBottom(state.x).ticks(5, "
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const yAxis = d3.axisLeft(state.y);
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state.svg.select(".x-axis")
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@@ -574,8 +617,9 @@
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.html(`
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<strong>${d.entity}</strong><br>
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Year: ${d.year}<br>
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Life Expectancy: ${d.lifeExpectancy.toFixed(1)} years<br>
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GDP per capita: $${d.gdpPerCapita.
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Population: ${d3.format(",.0f")(d.population)}
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`)
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.style("left", (event.pageX + 10) + "px")
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@@ -587,7 +631,7 @@
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newCircles.append("circle")
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.attr("r", 0)
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.attr("fill", d => config.regions[d.region].color)
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.attr("opacity", 0.7)
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.transition(t)
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.attr("r", d => state.size(d.population))
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@@ -603,7 +647,7 @@
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})
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.select("circle")
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.attr("r", d => state.size(d.population))
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.attr("fill", d => config.regions[d.region].color);
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// Add country labels for larger bubbles
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circles.selectAll("text").remove();
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font-size: 12px;
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}
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.disclaimer {
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text-align: center;
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font-size: 12px;
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color: #7f8c8d;
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margin-top: 15px;
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}
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@media (max-width: 768px) {
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.controls {
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flex-direction: column;
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<body>
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<div class="container">
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<h1>World Development Indicators</h1>
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<p class="subtitle">Life Expectancy vs. GDP per capita (1950-2023)</p>
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<div class="controls">
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<div class="filter-buttons" id="region-buttons">
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</div>
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<div class="tooltip" id="tooltip"></div>
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<p class="disclaimer">Note: For countries missing region data in their year, we use the region from their latest available data</p>
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</div>
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<script>
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let state = {
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data: null,
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filteredData: null,
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countryRegions: {}, // Store the most recent region for each country
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minYear: 1950,
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maxYear: 2023,
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currentYear: 1990,
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async function loadData() {
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try {
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// Load the CSV data
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const rawData = await d3.csv("world-data.csv");
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// First pass: Create a lookup table for country regions from the most recent data
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const latestYearData = {};
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// Find all unique entities
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const entities = new Set(rawData.map(d => d.Entity));
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// For each entity, find the most recent year that has region data
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entities.forEach(entity => {
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// Get all records for this entity, sorted by year (descending)
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const entityData = rawData
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.filter(d => d.Entity === entity)
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.sort((a, b) => b.Year - a.Year);
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// Find the first record with region data
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const latestDataWithRegion = entityData.find(d =>
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d['World regions according to OWID'] && d['World regions according to OWID'] !== ""
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);
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if (latestDataWithRegion) {
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latestYearData[entity] = latestDataWithRegion['World regions according to OWID'];
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} else {
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// Fallback if no region data exists at all
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latestYearData[entity] = 'Unknown';
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}
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});
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// Process the data with region fallback
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const processedData = rawData.map(d => {
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// Use the region from our lookup table as fallback
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const region = d['World regions according to OWID'] ||
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latestYearData[d.Entity] ||
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'Unknown';
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return {
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entity: d.Entity,
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code: d.Code,
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year: +d.Year,
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lifeExpectancy: d['Life expectancy - Sex: all - Age: 0 - Variant: estimates'] ?
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+d['Life expectancy - Sex: all - Age: 0 - Variant: estimates'] : null,
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gdpPerCapita: d['GDP per capita, PPP (constant 2021 international $)'] ?
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+d['GDP per capita, PPP (constant 2021 international $)'] : null,
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population: d['Population (historical)'] ?
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+d['Population (historical)'] : null,
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region: region
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};
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}).filter(d =>
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d.lifeExpectancy !== null &&
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);
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// Filter out any invalid data points
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state.data = processedData.filter(d =>
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!isNaN(d.year) &&
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!isNaN(d.lifeExpectancy) &&
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!isNaN(d.gdpPerCapita) &&
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]);
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// Update axes
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const xAxis = d3.axisBottom(state.x).ticks(5, "$,.0f");
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const yAxis = d3.axisLeft(state.y);
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state.svg.select(".x-axis")
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.html(`
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<strong>${d.entity}</strong><br>
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Year: ${d.year}<br>
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Region: ${d.region}<br>
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Life Expectancy: ${d.lifeExpectancy.toFixed(1)} years<br>
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GDP per capita: $${d.gdpPerCapita.toLocaleString('en-US', {maximumFractionDigits: 0})}<br>
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Population: ${d3.format(",.0f")(d.population)}
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`)
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.style("left", (event.pageX + 10) + "px")
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newCircles.append("circle")
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.attr("r", 0)
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.attr("fill", d => config.regions[d.region] ? config.regions[d.region].color : "#95a5a6")
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.attr("opacity", 0.7)
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.transition(t)
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.attr("r", d => state.size(d.population))
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})
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.select("circle")
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.attr("r", d => state.size(d.population))
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.attr("fill", d => config.regions[d.region] ? config.regions[d.region].color : "#95a5a6");
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// Add country labels for larger bubbles
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circles.selectAll("text").remove();
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