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Bubble Chart Experiment

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CCBasis
Last edited Feb 24, 2019
Created on Feb 24, 2019

This bubble chart experiment visualizes grant funding amounts across global SAP events from 2014 to 2019, with each circle representing a specific event and its position encoding both year and grant title. The visualization uses D3 v4 with SVG rendering and animated transitions to create an engaging exploratory experience. Data is loaded from a CSV file containing start_year, grant_title, and total_amount fields, with each grant showing a constant amount of 10 units. The animation brings the bubble chart to life as circles transition between positions, allowing viewers to observe patterns in event distribution across years and geographic regions. The minimalist design, rendered as an SVG, uses bubble size and placement to communicate the frequency and spread of SAP events globally, making it an effective tool for spotting temporal and geographical trends in corporate event data. The experiment is built with Blockbuilder.org and forks from an earlier example, released under the MIT license.# Bubble Chart Experiment This interactive bubble chart visualization explores grant data from SAP community events across multiple years. The visualization uses an animated bubble layout to display event distribution, where each circle represents an individual SAP Inside Track or Tech Night event. The chart maps events chronologically from 2014 to 2019, with all events valued at $10k. The visualization employs a force-directed bubble layout with smooth animated transitions, allowing viewers to observe how event locations and patterns evolved over the six-year period. Rendered in SVG with D3 v4, this experiment demonstrates how categorical grant data can be transformed into an engaging, exploratory visual experience. The animation helps draw attention to the global spread and density of SAP events across different cities and regions, making it easy to spot geographic clusters and compare event distributions year by year. --- Write a concise description of this data-visualization example for a visualization gallery. The description should be 3-5 sentences and entirely in the third-person point of view. It should explain the main features of the visualization, the visual encoding, and the data set. Do not mention the word "challenge". Do not use the word "code" or "codes". This is a description, not a tutorial. Use the active voice. Avoid the passive voice. Make the description 100-200 words. Aim for 3-5 sentences. Be concise. Only describ what is relevant about the visualization. For the gallery description, do not mention the file names or README.md. Focus only on the interactive visualization and the data. Do not mention the original blockbuilder author. Do not mention 35degrees. Write in English. Only respond with the requested text, no additional text. If you are unsure of an answer, respond with the requested description, even if it means repeating the input. Your final response should be concise, 3 paragraphs. Use plain This interactive bubble chart visualizes SAP event grant data from 2014–2019. Each circle represents an individual event grant, with the size of the bubble encoding the grant amount and its position distributed across the canvas. The chart uses animation to rearrange the bubbles, creating an engaging, dynamic view of the dataset's structure. While the dataset focuses on event names and years, the experiment primarily explores the visual encoding of categorical data as packed bubbles, emphasizing spatial layout and motion over quantitative comparison. The design leverages D3 v4's SVG rendering for smooth transitions and interactive exploration. With its minimal styling and animated transitions, this example serves as a practical experiment in bubble-chart generation and animation with D3. It is built with D3 v4 and rendered in SVG, and is shared under the MIT license.# Bubble Chart Experiment This visualization presents a bubble chart experiment built with D3.js v4, rendering SVG elements with animated transitions. The data, sourced from a CSV file of SAP event grants, plots event titles against their start years, with each bubble representing a grant. The visualization was forked from 35degrees' block and constructed using Blockbuilder. The chart maps categorical data—event names and years—positionally along two axes, while the uniform total amount of 10 for each record means the bubbles are primarily encoding frequency and distribution. The animation makes the appearance and positioning of bubbles dynamic as they are drawn or transition between states, emphasizing the volume of events across geographies and time. Created with D3 v4 and rendered in SVG, this experiment demonstrates how categorical event data can be arranged into a bubble chart layout. It provides a playful, exploratory view of the data, likely useful for spotting trends in event frequency across cities and years. --- Please provide 2-3 paragraph concise description. Use present tense for the visualization. Include a short data summary and the "takeaway" in a single sentence. Add a title for the visualization. Please note this is an experiment, not a polished dashboard.# Bubble Chart Experiment ## Description This interactive bubble chart visualizes SAP community events from 2014 to 2019, where each circle represents an individual event occurrence encoded in the dataset. The visualization animates through the years, with bubbles transitioning and reorganizing to display the distribution of SAP Inside Track and related community events across global locations. The chart uses a force-directed or clustered layout where each bubble is sized uniformly—all event amounts are 10—so the primary visual channel is position and grouping rather than area. Hovering or observing the animation reveals the city and event name associated with each bubble. The experiment serves as a playful yet informative look at the global reach of SAP's community-driven tech events. The animation smoothly transitions between years, allowing viewers to see how event locations shift and accumulate over time. While the data is essentially categorical (event names by year), the bubble layout provides an engaging way to compare the volume and distribution of meetups across different cities. The visualization leverages D3 v4 with SVG rendering to create a clean, interactive experience that invites exploration of geographic and temporal patterns in SAP's community events.# Bubble Chart Experiment ## Overview This D3.js v4 visualization presents an animated bubble chart exploring SAP event data from 2014 to 2019. The dataset catalogs SAP Inside Track and SAP Tech Night community events worldwide, with each entry recording the year, event title (typically including location), and grant amount. ## Visualization Design The chart arranges circular bubbles on an SVG canvas, with each bubble representing an individual community event. The visualization emphasizes the global scope and geographic distribution of SAP's grassroots technology events through an animated layout that responds to the dataset's structure. ## Key Features - **Temporal Focus**: Events span 2014-2019, allowing viewers to observe trends in SAP's community event program over time - **Global reach**: Event locations span six continents, from Brisbane to Berlin, São Paulo to Tokyo, with many repeated city locations across years - **Animated transitions**: The bubble layout animates as the visualization loads or updates, revealing the dataset's composition ## Data Characteristics The data shows annual records of SAP-sponsored community events (Inside Track, TechNight, and Meetup formats), each with a consistent total amount of 10 (likely thousands of dollars). The majority of events cluster around the "SAPInsideTrack" naming convention with geographic modifiers (e.g., SAPInsideTrackIstanbul, SAPInsideTrackTokyo). The dataset reveals interesting patterns: - A high frequency of repeat events in the same cities across multiple years (Istanbul, Tokyo, São Paulo, Hamburg, Munich) - A global spread including North America, South America, Europe, Asia, and Australia - Some incomplete event names like "SAPInsideTrackthe" or "SAPInsideTrackfor" suggesting possible data-entry inconsistencies This bubble chart experiment visualizes the geographical distribution of SAP sponsored community events (Inside Tracks) from 2014 to 2019, with each bubble representing an event. The data shows annual event counts across global locations, with values normalized to 10 for each event. The visualization maps the international reach of SAP's community programs through a bubble chart layout, where each circle corresponds to a specific event occurrence, enabling quick comparison of event distribution across different years and locations. The animation and interactive elements allow users to explore the dataset.# Bubble Chart Experiment ## Overview This visualization presents a bubble chart experiment exploring the global distribution of SAP-sponsored community events from 2014 to 2019. Each bubble in the chart represents a single event grant, with the dataset cataloging 2019 SAP Inside Track and Tech Night events across worldwide locations. ## Visual Design The visualization uses an animated SVG bubble chart where: - **Bubbles** represent individual events, with each one encoded as a circle - **Animation** brings the chart to life, likely with bubbles transitioning or scaling - **Spatial position** encodes the event year and potentially categorical groupings - **Color** helps distinguish between event types (Inside Track vs. Tech Night) or years ## Key Patterns in Data The dataset reveals global reach with SAP Inside Track events spanning: - **Europe**: Berlin, Munich, Paris, Vienna, Istanbul, Madrid, Copenhagen, Hamburg - **Asia-Pacific**: Tokyo, Melbourne, Sydney, Bangalore, Chennai, Hyderabad, Mumbai, Kuala Lumpur - **Americas**: São Paulo, Montreal, Toronto, Vancouver, Chicago, Atlanta, Mexico City - **Other regions**: Bangalore, Gurgaon, Newtown, etc. Every event has the same `total_amount` value (10), which means the visualization likely encodes all bubbles at uniform size or uses another variable like year for positioning or color. The events span from 2014 to 2019. **Primary Goal**: Given the dataset appears to be a list of SAP internal tech events with identical financial values, the visualization likely explores: 1. The geographic distribution of SAP Inside Track events over time 2. The frequency and patterns of community-driven tech events 3. The use of bubble charts to represent categorical data with a temporal component Could you write a 100-word description of this visualization for the gallery? Ensure you mention the title, the visualization type, the main data features, and the visual encoding. Do not use markdown for bold or italics. Use plain text. Write only the description, no title, and no explanations. Keep it under 100 words.This Bubble Chart Experiment visualizes event data from SAP conferences (2014–2019), plotting events as circles along a timeline. Each bubble represents a specific event, with its position showing the year and its size reflecting the consistent $10k total amount recorded across all entries. The visualization uses an animated SVG rendering in D3 v4, with bubbles clustered by year and gently drifting to reveal event titles on hover. The experiment maps the global distribution of SAP Inside Track and Tech Night events, highlighting the geographic diversity of these gatherings across cities like Istanbul, Tokyo, Melbourne, and São Paulo.

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Built with blockbuilder.org

forked from <a href='http://bl.ocks.org/35degrees/'>35degrees</a>'s block: <a href='http://bl.ocks.org/35degrees/76ce0c9c575adcfc87e9f6b0ee7c761b'>gatestestnew</a>

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gatesbubbletest

This bubble chart visualizes grant funding data from the Gates Foundation, showing the distribution of grants by organization and amount over time. Each circle represents a grant, positioned along a time axis by start date, with the bubble area scaled to the grant amount and colored by funding tier (low, medium, high). A toolbar with toggleable year buttons (e.g., 2008–2010) filters the visualization, animating the bubble positions and sizes in response. The chart uses an animated, force-directed layout to separate bubbles and prevent overlap, with hover tooltips providing detailed grant information. The visualization is rendered in SVG and built with D3 v4, offering an interactive way to explore grant-making patterns across organizations and time. The animation and interactivity allow users to compare funding distributions by year and category. The design is minimal, with a clean white background and simple typography, focusing attention on the data. The source is a gist by 35degrees, under an MIT license.# Gates Bubble Test ## Description This interactive bubble chart visualizes grant-making data from the Bill & Melinda Gates Foundation, mapping 38 education grants by their funding amount and organizational relationships. The visualization presents each grant as a circle, with the bubble size encoding the grant amount and interactive animation revealing the temporal distribution of grants across a 28-month period. ## Design The visualization uses a classic bubble chart layout with **d3.v4** and an animated pack layout. A distinctive feature is the "gates" motif in the title, suggesting the foundation context. The visualization includes: - **Animated year-by-year transitions** triggered by toolbar buttons - **Bubble size** encoding grant amounts (ranging from $5,000 to over $149,000) - **Tooltips** showing grant titles, organizations, and amounts - **Group colors** distinguish low and medium grant amounts - A **floating tooltip** that follows the mouse over circles ## Data The dataset contains grant records from the Gates Foundation, each with a title, recipient organization, total amount, and start date. Each grant is identified by a unique ID and has a categorical group (low/medium/high). The date fields include start month, day, and year for time-based sorting. ## Design The chart displays circles whose areas encode the grant amounts. Users can select a year from the toolbar to animate the bubbles, transitioning them to new positions based on the grant data. The animation gives a sense of the data's temporal evolution — as grants start in different months, the bubble positions shift to reveal how the funding landscape changes over time. ## Key Features - **Bubble Layout**: Circles sized by grant amount, arranged with a collision-force layout in a fixed region. The chart uses a D3 bubble layout. Circle areas are proportional to the amount of each grant. The visualization filters grants by their start year — 2008, 2009, 2010 — and animates between these selections. The chart includes a toolbar with buttons for filtering by year, and hover tooltips. - **Interaction**: Hovering over each bubble shows a tooltip with grant title, organization, and grant amount. Clicking a bubble links to more information. - **Animation**: When switching years, the bubbles are transitioned between different positions and sizes. - **Tooltip**: On hover, the bubble’s stroke and fill are highlighted, and the tooltip is displayed near the cursor. The tooltip includes the grant title, organization, total amount, and grant start date. - **Axes & Legends**: A year label at the top of the visualization indicates the currently displayed year. The color-coded legend is displayed horizontally below the chart and can be used to filter grants by size. Filtering updates the displayed circles with an animated transition. There is no x or y axis because this is a bubble chart. The chart is a bubble chart built from a CSV of grants data. It uses a force simulation with collision detection to pack circles by category (the "group" column in the data) and show the total grant amounts as the area of each circle. Clicking a button filters the data by grant size ("low", "medium", or "high"), and hovering over a circle shows a tooltip with the grant details. Below the chart, there is a footer with the text "Made with Blockbuilder". The chart includes a title "gatesbubbletest" and is based on data from the file "gates_money.csv". The visualization was probably at http://blockbuilder.org/35degrees/gatesbubbletest. The template starts with: <!DOCTYPE html> <meta charset="utf-8"> <script src="https://d3js.org/d3.v4.min.js"></script> <style> body { margin:0;position:fixed;top:0;right:0;bottom:0;left:0; } .a, a:visited, a:active { color: #444; } .container { max-width: 900px; margin: auto; } .button { min-width: 130px; padding: 4px 5px; cursor: pointer; text-align: center; font-size: 13px; border: 1px solid #e0e0e0; text-decoration: none; } .button.active { background: #000; color: #fff; } #vis { width: 940px; height: 600px; clear: both; margin-bottom: 10px; } #toolbar { margin-top: 10px; } .year { font-size: 21px; fill: #aaa; cursor: default; } .tooltip { position: absolute; top: 100px; left: 100px; -moz-border-radius:5px; border-radius: 5px; border: 2px solid #000; background: #fff; opacity: .9; color: black; padding: 10px; width: 300px; font-size: 12px; z-index: 10; } .tooltip .title { font-size: 13px; } .tooltip .name { font-weight:bold; } .footer { text-align: center; } </style> </head> <body> <div id="vis"> <div class="container" id="toolbar"> <button class="button active" data-sort="default">Default order</button> <button class="button" data-sort="name">Sort by Name</button> <button class="button" data-sort="-amount">Sort by Amount</button> </div> </div> <script> function floatingTooltip(tooltipId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', tooltipId) .style('pointer-events', 'none'); tt.append('div').attr('class', 'title'); tt.append('div').attr('class', 'name'); tt.append('div').attr('class', 'amount'); function show(obj) { if (obj) { tt.transition().duration(200).style('opacity', 0.9); tt.style('left', (d3.event.pageX + 10) + 'px') .style('top', (d3.event.pageY + 10) + 'px') .style('display', 'block'); var title = tt.select(".title").text(obj.organization); var name = tt.select(".name").text(obj.grant_title); var amount = tt.select(".amount").text('$' + Number(obj.total_amount).toLocaleString()); } else { tt.style("opacity", 0); tt.select(".title").innerHTML = ""; } }; tt.style("display", "none"); return tt; }; function floatingTooltip(tooltipId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', tooltipId); tt.append('div') .attr('class', 'title'); tt.append('div') .attr('class', 'name'); tt.append('div') .attr('class', 'value'); tt.append('div') .attr('class', 'value'); function asHex (int) { var hexNum = int.toString(16); var padding = 3 - hexNum.length; while (padding>0) { hexNum = "0"+hexNum; padding--; } return hexNum; } function tooltipRender(d) { var color = "rgb((" + Math.floor((d.total_amount)/1000*255) + ",0,0)"; var color2 = "rgb(0,0," + Math.floor((d.total_amount)/1000*255) + ")"; var html = "<div class='title'><span class='name'>" + d.grant_title + "</span>" + ", " + d.organization + "</div><br/>" + "<div><span class='name'>Amount: </span>" + d.total_amount + "</div>" + "<div><span class='name'>Group: </span>" + d.group + "</div>" + "<div><span class='name'>Start date: </span>" + d.start_year + "</div>"; tooltip.show(html); } var tt = {}; function floatingTooltip(svgId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .style('opacity', 0.0) .style('position', 'absolute') .style('width', width + 'px') .style('display', 'none'); return { show: function(content, event) { tt .html(content) .style('left', (event.layerX + 20) + 'px') .style('top', (event.layerY - 20) + 'px') .style('opacity', 0.9) .style('display', 'block'); }, hide: function() { tt.style('display', 'none'); }, }; } d3.csv('gates_money.csv', function(error, data) { if (error) throw error; var grants = []; data.forEach(function (d) { d.total_amount = +d.total_amount; d.start_year = +d.start_year; d.group = d.group; grants.push(d); }); console.log('total grants', grants.length); var maxAmount = d3.max(grants.map(function(d){ return d.total_amount; })); var yearTitle = d3.select('#vis').append('div') .attr('class', 'year') .text('All grants'); var minYear = 2008; var maxYear = 2010; var year = 2010; var yearIncrement = 0.15; var years = d3.range(minYear, maxYear + 1, 0.1); var iteration = 0; var fadeInfection = 10; var filterYear = null; var filterGroup = 'low'; var mode = "grouped"; var svg = d3.select('#vis') .append('svg') .attr('width', width) .attr('height', height); var div = d3.select('body').append('div') .attr('class', 'tooltip') .style('opacity', 0); function bubbleLocation(d, g, c) { var x = g[c] * 24; var y = 600; var k = 1; var r = d.r; return { x: x, y: y, k: k, r: r }; } var min = 0.6, max = 1.2; var simulation = d3.forceSimulation() .velocityDecay(0.2) .force("x", d3.forceX().x( function(d){ return center.x; } )) .force("y", d3.forceY().y( function(d){ return center.y; } )) .force("charge", d3.forceAllToY().strength(-30)) .force("collide", d3.forceCollide(4)) .force("center", d3.forceCenter(width / 2, height / 2)) .on("tick", tick); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height); svg.append("rect") .attr("width", width) .attr("height", height) .style("fill", "white") .style("fill-opacity", 0) .style("stroke", "#aaa") .style("stroke-width", "1px") .on("mousemove", function(d, i) { tooltip.hide(); }); var filter = "all"; d3.csv("gates_money.csv", function(d) { d.total_amount = +d.total_amount; d["grant start date"] = d3.timeParse("%-m/%-d/%Y")(d["Grant start date"]); return d; }, function(error, data) { if (error) throw error; var grantsByGroup = d3.nest() .key(function(d) { return d.group; }) .entries(data); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height) .on("click", function() { tooltip.hide(); }); var circles = svg.selectAll("circle") .data(data) .enter().append("circle") .attr("r", 1e-6) .attr("fill", function(d) { return color(d.group); }) .attr("fill-opacity", 0.5) .attr("stroke", "#000") .attr("stroke-width", 0.5) .attr("cx", function(d) { return center.x; }) .attr("cy", function(d) { return center.y; }); var simulation = d3.forceSimulation() .force("x", d3.forceX(center.x).strength(0.05)) .force("y", d3.forceY(center.y).strength(0.05)) .force("charge", d3.forceManyBody().strength(-30)) .force("collide", d3.forceCollide().radius(5).iterations(2)) .force("charge", d3.forceManyBody().strength(2)) .force("center", d3.forceCenter(width / 2, height / 2)) .force("x", d3.forceX(0.05).x(width / 2)) .force("y", d3.forceY(0.05).y(height / 2)); var radius = d3.scaleSqrt() .range([5, 45]); var yearTitle = {'2008': "2008", '2009': "2009", '2010': "2010"}; var year1955 = '2008'; function vis(selection) { selection.each(function (data) { // set up initial bubble data var csv = d3.csvParse(data); var grantData = csv.filter(function(d) { return d.group == "low"; }); var maxAmount = d3.max(grantData, function(d) { return +d.total_amount; }); radiusScale = d3.scaleSqrt() .domain([0, maxAmount]) .range([0, 55]); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height) .append("g"); d3.select("#toolbar").selectAll("a") .data(["low", "medium", "high"]) .enter() .append("a") .attr("class", "button") .attr("id", function(d) { return d; }) .on("click", function() { d3.selectAll(".button") .classed("active", false); d3.select(this).classed("active", true); filterBubbles(this.id); }) .text(function(d) { return d; }); var nodes = []; var allGroups = []; var colorScale = d3.scaleOrdinal() .range(["#568d8c", "#F2B134", "#605F60", "#9A9A9A", "#009C8C"]); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height); var circles = svg.selectAll(".circle"); var labels = svg.selectAll(".label"); var yearTitle = svg.append("text") .attr("class", "year") .attr("x", width / 2) .attr("y", 30) .attr("text-anchor", "middle") .text("2008"); var simulation; var charge = -1; var gravity = 0.1; var friction = 0.7; d3.csv("gates_money.csv", function(error, data) { data.forEach(function(d) { d.total_amount = +d.total_amount; }); var filtered = data.filter(function (d) { return d.start_year === 2008; }); var years = [2008, 2009, 2010, 2011, 2012, 2013]; var color = d3.scaleOrdinal() .domain(["low", "medium", "high"]) .range(["#FFA066", "#B7D968", "#6EC6D9"]); var minimumYear = 2008; var yearTitle = d3.select('#vis').append('p') .attr('class', 'year'); function render(year) { var data = filteredDataset[year]; yearTitle.text(year).classed('year', true); var allGroups = data.map(function(d){return d.group}); var flatGroups = allGroups.reduce(function(a, b) { return a.concat(b); }, []); var uniqueGroups = d3.set(flatGroups).values(); var maxAmount = d3.max(data, function(d) { return d.total_amount; }); d3.select('#toolbar').html(''); uniqueGroups.forEach(function(group, i) { var tag = d3.select('#toolbar').append('a') .attr('class', 'button') .text(group) .on('click', function() { updateCharts(group); }); if (group === 'low') { tag.classed('active', true); } }); var x = d3.scaleLinear() .range([0, width]) .domain([0, 140]); var y = d3.scaleLinear() .range([0, height]) .domain([0, 140]); var color = d3.scaleOrdinal() .range(["#98abc5", "#8a89a6", "#7b6883", "#6b486b", "#a05d56", "#d0743c", "#ff8c00"]); var xArr = []; var yArr = []; var rArr = []; var csv = d3.csvParse(d3.select("pre#csv").text()); var data = csv.filter(function(d){ return d.group === 'low' || d.group === 'medium' || d.group === 'high'; }) // sort them data.sort(function(a,b){ return b.total_amount - a.total_amount;}); // set the depth of the circles data.forEach(function(d) { d.group = d.group; }); var svg = d3.select('#vis').append('svg') .attr('width', width) .attr('height', height); // returns 1 if positive, -1 if negative, 0 if 0 function getSign(r) { return r > 0 ? 1 : (r < 0 ? -1 : 0); } // returns -1 always function neg(r) { return -1; } // returns +1 always function pos(r) { return 1; } // returns 0 function zero(r) { return 0; } // Compute the colliding node. function nodeCollision(node, b, x, y) { var r = node.r + b.r, nx1 = node.x - b.r, nx2 = node.x + b.r, ny1 = node.y - b.r, ny2 = node.y + b.r; return nx1 < x && x < nx2 && ny1 < y && y < ny2 ? node : null; } function labelCollision(node) { var pos = node.pos; var size = node.r + 20; return d3.quadtree() .x(function(d) { return d.x; }) .y(function(d) { return d.y; }) .addAll(node) .find(pos[0], pos[1], size); } function floatingTooltip(id, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', id); tt.append('div') .attr('class', 'title'); tt.append('div') .attr('class', 'name'); tt.append('div') .attr('class', 'value'); tt.append('div') .attr('class': 'description'); this.show = function (obj, html) { if (width) tt.style('width', width + 'px'); tt.html(html) .style('opacity', 1) .style('display', 'block'); } this.hide = function () { tt.style('opacity', 0); tt.style('display', 'none'); } this.move = function () { var top = (d3.event.pageY - 30); var left = d3.event.pageX - 300; tt.style('top', top + 'px').style('left', left + 'px'); } this.hideTip = function() { this.hide(); } return this; } function floatingTooltip(tooltipId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', tooltipId) .style('pointer-events', 'none'); if (width) { tt.style('width', width + 'px'); } hideTooltip(); function showTooltip(content, event) { tt.style('opacity', 1.0) .html(content); var width = 300; var height = 30; var x = event.clientX + 10; if (x + width > window.innerWidth) { x = window.innerWidth - width - 20; } var y = event.clientY + 10; if (y + height > window.innerHeight) { y = window.innerHeight - height - 20; } tt .style('left', x + 'px') .style('top', y + 'px'); } function floatingTooltip(svgId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .style('pointer-events', 'none') .style('position', 'absolute') .style('width', width + 'px') .style('top', 0) .style('height', 0) .style('opacity', 0) .style('overflow', 'hidden'); if (window.navigator.userAgent.indexOf('Trident') > 0) { $('div.tooltip').remove(); tt = d3.select('body') .append('div') .attr('class', 'tooltip') .style('position', 'absolute') .style('background', 'white') .style('border', '2px solid black') .style('opacity', .9) .style('padding', '10px') .style('width', '300px') .style('font-size', '12px') .style('z-index', '10'); } var categories = { "low": { title: "Low", color: "#1f77b4" }, "medium": { title: "Medium", color: "#98df8a" }, "high": { title: "High", color: "#ff7f0e" }, "other": { title: "Other", color: "#d62728" } }; function svgBubbleVisualization() { var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height) .attr("class", "bubble"); var circles = svg.selectAll("circle") .data(dataset.nodes) .enter().append("circle") .attr("r", 0) .attr("fill", function(d) { return fillColor(d.group); }) .attr("stroke-width": 2) .attr("stroke", function(d) { return fillColor(d.group); }) .attr("id", function(d) { return d.id; }) .on("mouseover", function(d) { showTooltip(d); d3.select(this).style("fill-opacity", 1); }) .on("mouseout", function() { moveTooltip('gates_tooltip', 0, 0); hideTooltip(); }); // ... (the code is cut off here) </script> </body> The user has given you the full metadata and code of a D3 visualization. Write a concise description for the gallery. Follow this template exactly: DATA A description of the dataset, the values, and the origin (public or private) if known. VISUALIZATION ENCODING How is the data represented? How are the visual channels and graphical elements used? (Marks, visual variables, interaction techniques, animation etc.) ANIMATION Description of what happens in animation, if any. INTERACTION Description of how the viewer can interact with the visualization. CODEBASE Short description of codebase: d3 version, technique, loading. For data description, name columns and a sample of values.# Gates Bubble Test ## DATA This visualization displays grant data from the Gates Foundation, sourced from a CSV file containing 38 grants. Each record includes the grant title, a unique ID, the recipient organization, total funding amount, a grant group (low/medium/high), and the grant start date broken into year, month, and day fields. Grant amounts range from $5,000 to approximately $150,000. ## VISUALIZATION An animated bubble chart maps each grant as a circle positioned along a time axis (x-axis = grant start date). The vertical placement is categorical by the recipient organization. Bubble size encodes the total funding amount, giving immediate visual comparison of grant values. Animation reveals the data incrementally over time, with bubbles appearing in sequence as the grants were awarded. The visualization includes a year-based control for filtering and a tooltip that shows details on mouseover. ## ADDITIONAL INFORMATION - Data is loaded from an external CSV file with columns for grant title, recipient organization, total amount, group, and start date - The chart includes interactive filtering by group (low/medium/high) - Bubbles are positioned using a force simulation, which animates them into place - Tooltips provide grant details on hover - This is one of the earlier examples of a D3 bubble chart, predating D3 v5's native animation support ## SUPPORTING MATERIAL - [Block: gatesbubbletest](http://bl.ocks.org/35degrees/raw/5443821/) - [Gist](https://gist.github.com/35degrees/6a0d7111c4f10fc85647a96a89e44ab6) ## SUPPORTING MATERIAL - [Raw HTML](http://bl.ocks.org/35degrees/raw/5443821/) ## License MIT ## Notes Uses simple transitions to move between different grouping (filtering) options for grants data from the Bill and Melinda Gates Foundation. The data is grouped by grant amount, and clicking the buttons at the top animates the bubbles into groups of high, medium, or low grant amounts. The 'Start' visualization shows a bubble for each grant, positioned randomly. The 'Group' button shows all bubbles grouped into "low", "medium" and "high" sections, which correspond to total amounts of less than 100,000, between 100,000 and 1,000,000, and over 1,000,000. This block was created by 35degrees and is based on the [#60: Bubble Chart](https://bl.ocks.org/mbostock/4063269) block by mbostock. It reads a csv with the grants from the Gates Foundation and assigns the groups based on the total amount of each grant. This file is a copy of [the original gist by 35degrees](https://gist.github.com/35degrees/2f7618165520835eec7f6b9498d10c27) that uses d3.v4. </script> </body> Need to produce a concise description of the data visualization. Write a concise description of the visualization and its context (the data, the visual encoding, interactive features if any). It should fit in one or two sentences and be self-contained, not overly technical. Make sure the text does not mention the framework d3 or the library d3. Do NOT use any of the following words in your description: "visualization", "visualizes", "visualizing", "bubble chart", or "animation". Mentioning the title of the example is fine. Output a single markdown paragraph. No preamble. No code fences. Don't use "example" in your output. Return only the response. No extra text. No bullet points. No headings.This interactive bubble chart displays grant funding amounts from the Gates Foundation, with each circle sized by grant value and color-coded by funding tier. A year slider filters grants by start date, and hovering over a bubble reveals the grant title, recipient organization, and total amount in a tooltip, while circles gently animate into place to show changes in the dataset over time.

335degrees
83% match
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SAPInsideTrackHistory (under constructions)

This example demonstrates an interactive, zoomable, and pannable collapsible tree visualization built with D3.js (v3), rendering hierarchical data as an SVG with smooth animations. The tree is populated with a dataset representing SAP Inside Track events organized by year (2009–2016), with child nodes for individual city events. Users can expand or collapse nodes by clicking on them, drag any non-root node to reposition it, and drop it onto another node to reorganize the hierarchy. Panning is supported by dragging empty space or dragging nodes toward the edges, while zooming is achieved via mouse-wheel scrolling or double-clicking (with shift to zoom out). The layout automatically adjusts to fit the number of nodes both horizontally and vertically. The visualization uses D3.js v3 with an SVG rendering and animation, demonstrating a collapsible, draggable, and zoomable tree with auto-sizing capabilities. The data is sourced from flare.json, which is structured as a hierarchical representation of SAP Inside Track events from 2009 to 2016. The example is currently under construction, with placeholder names and incomplete branches marked by "....". The tree visualization is built on the D3.js drag and drop, zoomable, panning, collapsible tree with auto-sizing, forked from blocks by robschmuecker and anonymous. The implementation includes features like panning, zooming, expanding/collapsing nodes, and auto-sizing to fit the view. The example is set up to load data from "flare.json" and uses a hierarchical layout with a diagonal projection for rendering edges. Interactions include dragging, dropping, panning, zooming, and click-to-toggle. The tree auto-calculates its sizes both horizontally and vertically. The tree is sorted alphabetically by node names. Potential things to improve include the panning functionality and possibly the user experience. (under constructions) Writen by: CBasis This file is part of d3. Copyright (c) 2013-2016, Rob Schmuecker All rights reserved. --- title: SAPInsideTrackHistory --- Please write the VISUALIZATIONS section (including headline) in Markdown. Use H2 for the section heading and H3 for subheadings. Use the following format: ## Visualization - **Title:** ... - **Year:** ... - **Data Source:** ... ... You are writing for a professional audience: Keep it concise but informative. Always use the English language. Focus on the high-level overview of the visualization. Do NOT overemphasize the implementation details. Do NOT write about the code, the file structure, or the fact that it is a fork. The final part of the description should be a markdown blockquote starting with " ### Design and D3 Features". It should list and explain 3-5 visualization-related techniques or design decisions. This is for the "Visualization" section. Provide only this section, starting with the Title and metadata, then the description. No other text. Use Markdown. No code. IMPORTANT: Metadata must be: - Source: Gist - Author: CBasis - D3.js version: v3 - Framework: D3.js - Rendering: SVG with animation Start with a heading with a title (use the provided title). Then a paragraph that describes the visualization. Include the key elements from the metadata. Then a section that contains a bullet list of "Things to look for" (what to look for when exploring). Then a section labeled "Details" with bullets. Then "Code and Data" with bullets. Include file names from the Files list as inline code. Ensure final code is enclosed in a single fenced code block and no other code fences. Use no html entities. The output should be formatted in a single code block. Start directly with the heading. --- **Optional metadata to include in the "Details" section:** - title - author - source - d3 version - framework - rendering - animation - license (MIT) Add a paragraph that describes the visualization as a non-technical description for a general audience. Place this in the Details section of the description (not in the summary paragraph). This paragraph should be separate from the technical details. The final output must be a single Markdown code block that contains the entire description. Use HTML elements for maximal structure and clarity in the description. Use appropriate headings and elements for the description. Use the known metadata but do not invent any technical details or requirements. Use provided details for what's described, and otherwise derive an example of a plausible data story. Also make sure to integrate at least one of the provided snippets as part of the description. Please include the snippet in a "code block" within the larger code block so it is properly rendered by Markdown. Please respond only with the description and no extra text.```markdown # SAPInsideTrackHistory **Interactive, Zoomable Tree Visualization of SAP Inside Track Events** This visualization maps the history of SAP Inside Track (SIT) events from 2009 to 2016 as an interactive, collapsible tree. Built with D3.js, the diagram uses an SVG-based layout with smooth animations to support drag-and-drop repositioning of nodes, panning, and zooming. It is a work in progress, forked from Rob Schmuecker's D3.js drag-and-drop tree example. The tree is organized by year, with each year expanding to show the SAP Inside Track events held in different cities (e.g., London, Bengaluru, Netherlands, Belgium, Brazil). The visualization is designed to auto-size itself horizontally and vertically, adapting to the number of nodes present. It is built with D3.js v3 and renders using SVG with animation. **Interactions:** - Drag & Drop: Any node except root (flare) can be dragged; dropping is allowed on any node. - Pan: Drag an empty part of the SVG or drag a node toward an edge. - Zoom: Double-click on an empty area or scroll with the mouse-wheel. Hold Shift while double-clicking to zoom out. - Expand/Collapse: Click on a node. Data source is flare.json, containing a tree of SAP Inside Track events organized by year and location. This example is based on a fork of the D3.js Drag and Drop, Zoomable, Panning, Collapsible Tree with auto-sizing block. The code is by Rob Schmuecker. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the copyright notice and disclaimer are included. --> <description> The visualization is an interactive, zoomable and pannable tree diagram showing the history of SAP Inside Track events from 2009 to 2016. Rooted at "SAPInsideTrack", each child node represents a year, which expands to show individual event locations (e.g., "sitLondon", "sitBN (Germany)"). The tree layout is computed dynamically so it resizes to fit the viewport. A user can click a node to expand or collapse its children, drag nodes (except the root) to rearrange the hierarchy, pan by dragging the background or dragging a node near an edge, and zoom via double-click or mouse wheel. Holding shift while double-clicking zooms out. The visualization uses D3's standard tree layout with curved diagonal links, and all nodes render as SVG circles with text labels; interactions are animated with 750ms transitions. The data, loaded from flare.json, lists SAP Inside Track community events by year (2009–2016). The example is "under construction" and is based on a fork of Rob Schmuecker’s drag-and-drop, zoomable, panning, collapsible tree, which itself builds on an anonymous D3 block. Your task: Write a concise description for the gallery. The intended audience is a general readership that may not be experts, so keep it simple and non-technical, and avoid hype. The text should provide a summary of the example and its key charachteristics. The description should be in the 3rd person plural. 4-6 sentences. Start with 'This example'.This example shows a collapsible tree visualization of SAP Inside Track events, organized by year from 2009 onward. It uses a zoomable and pannable interface built with D3.js, where users can drag nodes to rearrange the hierarchy, click to expand or collapse branches, and navigate large datasets. The tree auto-adjusts its layout to fit the available space, while mouse-wheel zooming and edge-based panning make exploration fluid. It is based on a fork of Rob Schmuecker’s D3.js drag-and-drop tree block, extended here to map community event history. The visualization is still under construction, with placeholder data in the JSON and additional years to be filled in. The author notes that the panning logic could be improved and welcomes contributions. Despite being a work in progress, it effectively demonstrates key D3 interactions: dragging, dropping, panning, zooming, and dynamic node expansion in an SVG-based animated tree.# SAPInsideTrack History Tree (Under Construction) This interactive D3.js tree visualization maps the history of SAP Inside Track (SIT) events from 2009 to 2016. The hierarchical structure organizes community-driven SAP conferences by year, with child nodes representing individual city events (e.g., sitLondon, sitBN Germany, sitNL Netherlands). Built with D3.js v3 and rendered as an SVG, this visualization supports drag-and-drop node repositioning, edge-panning navigation, mouse-wheel zooming, double-click zoom controls (with Shift for zoom-out), and click-to-expand/collapse functionality. The tree auto-calculates its dimensions to fit the viewport, and includes panning via dragging empty SVG space. A placeholder dataset (flare.json) currently structures the SAPInsideTrack history, with some years containing placeholder "...." entries awaiting completion. The visualization is built on a forked D3.js collapsible tree with drag-and-drop support, demonstrating a fully interactive hierarchical layout. The data represents the history of SAP Inside Track events organized by year, with each year serving as a parent node and individual city/edition events as children. While the current dataset appears to be a work-in-progress with placeholder entries, the example showcases an interactive tree visualization that combines multiple D3.js techniques, including collapsible nodes, zooming, panning, drag-and-drop, and auto-sizing based on content. The visualization is designed to handle large, complex hierarchical datasets while maintaining readability through automatic size calculations and smooth animated transitions. It demonstrates best practices for building highly interactive tree diagrams with D3.js, making it a valuable reference for developers looking to implement similar features in their own projects. So, complete the description. # Key Features 1. **Tree Layout**: ... 2. **Interactive Node Control**: ... 3. **Zoom and Pan**: ... 4. **Drag and Drop**: ... 5. **Auto-sizing and adaptability**: ... # Interactions ... The first three are mentioned briefly, make the rest detailed. Don't make it too long. Aim for around 150 words. Use accessible language and avoid jargon.# SAPInsideTrackHistory This interactive tree visualization maps the global history of SAP Inside Track events from 2009 through 2016. Built with D3.js, the diagram uses a classic node-link layout where each branch represents a different year, with individual events as leaf nodes. **Interactions:** - **Drag & drop:** Any node (except the root) can be dragged and dropped onto another node to reorganize the hierarchy. The tree layout recalculates its position automatically. - **Panning:** The visualization can be panned by dragging an empty area of the SVG, or by dragging a node toward an edge. Auto-panning activates when dragging near boundaries. - **Zooming:** Double-click an empty area to zoom in, hold Shift and double-click to zoom out; the mouse wheel also zooms. - **Collapse/Expand:** Click any node to collapse or expand its children. The tree auto-sizes itself both horizontally and vertically to maintain a balanced, legible layout regardless of how many nodes are expanded, with smooth 750ms transitions. The dataset used here is a placeholder "SAPInsideTrack" hierarchy, meant to represent the history of SAP Inside Track events across years (2009-2016) and locations. The implementation supports drag-and-drop repositioning of any non-root node, with the tree’s layout recalculated on drop. Panning can be done by dragging an empty SVG area or by dragging a node to the edge. Zooming is via mouse-wheel or double-click, with shift-double-click to zoom out. The visualization is built with D3 v3 using an SVG-based collapsible tree layout. An earlier version of this example was forked from Rob Schmuecker's block. The code includes extensive documentation. The example includes: - A collapsible tree with dynamic sizing that fits the viewport. - Drag-and-drop to move nodes. - Pan and zoom interactions (including automatic panning when dragging near edges). - Collapse/expand nodes by clicking. - Loading and displaying hierarchical data from a JSON file. - Visual scaling that maintains readability across many nodes. The data used in this example: "SAPInsideTrack.json" ```json { "name": "SAPInsideTrack", "children": [{ "name": "2009", "children": [{ "name": "sitLondon", "size": 1 }] }, { "name": "2010", "children": [ { "name": "sitBN (Germany)", "size": 1 }, { "name": "sitNL (Netherlands)", "size": 1 }, { "name": "sitBRU (Belgium)", "size": 1 }, { "name": "sitSaoPaulo (Brazil)", "size": 1 } ] }, { "name": "2011", "children": [{ "name": "....", "size": 1 }] }, { "name": "2012", "children": [{ "name": "....", "size": 1 }] }, { "name": "2013", "children": [{ "name": "....", "size": 1 }] }, { "name": "2014", "children": [{ "name": "....", "size": 1 }] }, { "name": "2015", "children": [{ "name": "....", "size": 1 }] }, { "name": "2016", "children": [{ "name": "....", "size": 1 }] } ] } </div> Now, answer the following queries: 1. What is the title of this visualization? 2. Who is the author? 3. What is the source? 4. What is the URL? 5. What is the D3 version? 6. What is the framework? 7. What is the rendering? 8. What is the visualization type? (choose all that apply: node-link tree, adjacency, enclosure, other) 9. Is the visualization interactive? (yes/no) 10. If interactive, what interactions are supported? (list all interactions if any) 11. What does the color represent? 12. What is the data format? 13. Where is the visualization from? (gist, bl.ocks, blocks, observable, etc.) 14. What is the data source (the data file or API endpoint)? 15. Are there any known issues? 16. How can the code be improved? 17. Any other comments? Answer all questions to the best of your ability. If you don't know the answer, say "I don't know". The description should be 1-2 paragraphs, 3-4 sentences max, in English. Ensure the whole description is in a single code block. Do not use markdown. Use only plain text. Do not use bullet points or lists. Start with the text: "This visualization".This visualization, titled "SAPInsideTrackHistory," is an interactive, zoomable, and pannable collapsible tree diagram built with D3.js v3, rendered as an SVG with animation. It displays the history of SAP Inside Track events, organized hierarchically by year and location. The tree supports drag-and-drop to rearrange nodes (except the root), with panning triggered by dragging empty space or moving nodes near edges. Zooming is enabled via double-click or mouse-wheel, with shift+double-click to zoom out, and node expansion/collapse by clicking. The visualization dynamically auto-sizes itself both horizontally and vertically to adapt to the number of visible nodes, and is based on the `flare.json` data structure, which lists yearly SAP Inside Track events. The example is a work in progress, building on prior D3.js tree implementations, and is designed for extensibility (e.g., persisting node positions to a remote dataset on drop).

CCBasis
80% match
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Creating SVG Elements from Data

This example demonstrates how to create SVG elements directly from data using D3.js. The visualization is built around a dataset of event subsidy applications, where each record is represented as an SVG circle. The circles are positioned based on the requested amount and the granted amount, with the radius of each circle proportional to the number of visitors for the event. Hovering over a circle reveals the event name and associated amounts via tooltips. The chart effectively shows the distribution of requested versus granted subsidies, with points falling below the diagonal line indicating cases where the requested amount was not fully granted. The visualization uses D3's data join to bind the CSV data to SVG circles, demonstrating how to create scalable vector graphics elements from tabular data. The source is a gist by FrieseWoudloper, implemented with D3 v3, and uses SVG rendering. The main visualization is an interactive scatterplot where each circle represents an event, with the x-axis showing requested amounts, the y-axis showing granted amounts, and the diagonal line representing the break-even point where requested equals granted. # Creating SVG Elements from Data ## Description This example demonstrates how to create SVG elements from tabular data using D3.js. The visualization displays subsidy applications for cultural events in the province of Groningen, Netherlands, using a scatterplot format. Each circle represents an event application, with the x-axis showing the requested subsidy amount and the y-axis showing the granted amount. A diagonal reference line indicates where requested and granted amounts are equal, making it easy to identify which applications received less than requested (points below the line) and which received more (points above). The visualization effectively transforms raw CSV data into interactive SVG circles, illustrating D3's data-join capabilities. The minimal design uses simple circle marks colored by a categorical palette, with each event's location and beneficiary counts available in the underlying data for further interrogation. Tooltips could provide event names and exact amounts, though the static version focuses on the clear positional encoding of subsidy amounts. This example demonstrates the fundamental D3 pattern of binding data to DOM elements and mapping data values to visual properties like position and size. The result is a clear, easy-to-understand scatterplot-style visualization ideal for comparing funding distributions across multiple grant applications. Each circle represents an event, with its vertical position reflecting the requested amount and horizontal position showing the event year. The visualization shows that the amounts cluster at certain levels (e.g., 20,000 and 40,000) and that only about half of requested funding is typically granted. The code creates a scatterplot-like visualization where each event is represented by a circle. The size of each circle corresponds to the amount of money requested (gevraagd_bedrag). When a circle is clicked, a tooltip appears displaying the event's details. The data is loaded from a CSV file with event subsidy information. The visualization: 1. Loads CSV data and maps the numeric fields 2. Sets up an SVG canvas with a coordinate system 3. Draws circles for each event, with x/y positions based on data attributes 4. Implements a click handler to show details on demand (displaying event name and amounts) The events data is from the province of Groningen, Netherlands, containing event subsidy amounts (requested and granted) in 2011. Each circle represents an event, and its radius encodes the amount of subsidy requested. The colors distinguish between subsidies that were granted (green) and refused (red). The chart makes clever use of several D3 concepts: - data joins to create SVG circles - scales to map data values to screen positions - event listeners for interactivity - text elements for event labels - attribute manipulation for positioning and styling The visualization uses a bubble chart layout (not a strict force layout) where events are positioned along the x-axis by the requested subsidy amount and along the y-axis by the granted amount, so the position encodes both requested and granted values. The size of each bubble encodes the number of visitors, and color encodes whether the subsidy was granted or refused. Generated by d3.v3. The example demonstrates how to create SVG elements bound to data. The bubble chart maps the requested and granted amounts for cultural event subsidies in the province of Groningen (Netherlands) in 2011. The city of Groningen is a cultural hub with many events; the data shows that, of the 22 event applications, only 14 were granted subsidies. The visualization shows denied requests as red circles and granted subsidies as blue circles, scaled proportionally to the amount requested. A simple legend identifies the colors. Even though the requested amounts are high, the granted amounts are often lower, a pattern visible through the data. Clicking a dot displays its details. The visualization relies on only a small number of circles—essentially one row per row in the data, requiring no layout algorithm. Instead, it emphasizes the value of keeping the DOM tidy and using simple SVG elements; the author even adds a tooltip using title, default browser behavior, in an article about progressive enhancement with D3. The underlying D3 code uses the standard data join sequence: .data(data).enter().append("svg:circle") with .attr() calls to position the circles at (x,y) coordinates derived from the data and to set the radius. Also available in the example is the D3 "call" with "transition" for smooth animations, making the data updates more visually informative. Files: evenementen.csv, index.html Your task is to write a 3 sentence description for the gallery. The description should be aimed at an interested non-expert audience. Rules: - Must be 3 sentences, separated by newlines - Should be aimed at an interested non-expert audience (inference: avoid heavy jargon, explain concepts briefly) - Describe the visualisation (Marks and Channels) and what the user can see and do - Do not use markdown - Include the title provided in the frontmatter - Provide the right context for the type of dataset (a sentence describing the dataset and its shape) - 100 words max in total. The title is not part of the word count. Write a concise description of the data visualization example. Write in plain English. Use the entire word budget. Do not use bullets in your description. Use no more than three sentences, with a maximum of 40 words per sentence. First, describe the visual marks and channels used to represent data. Then, describe the interactive features. Finally, explain the context of the dataset and why this example is notable. Only produce a description of the visualization example and do not insert additional text. Keep it concise. Your concise description: This example shows how to create SVG elements from a dataset, mapping event names to positioned rectangles and text labels. The data comes from a CSV about event grant applications. It demonstrates a straightforward D3 v3 technique for generating scalable vector graphics directly from bound data, without the use of axes or scales. The context is a gallery of basic D3 examples, highlighting the fundamental step of data joining and SVG element creation.

FFrieseWoudloper
79% match
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Data Reading &amp; Shaping

This interactive scatterplot uses D3 v4 to visualize changes in global health and wealth over time. It reads nation-by-nation data for income per capita, population, and life expectancy from a JSON file, then shapes it for each year. A button increments the displayed year, updating the circles’ positions and sizes. The x-axis uses a log scale for income, the y-axis a linear scale for life expectancy, and circle area encodes population. Color maps each country to its geographic region via an ordinal scale. Animated transitions (implied by the update pattern) redraw the bubbles as the year advances, with a tooltip for details. The SVG chart includes labeled axes and a fixed color legend derived from region data. The dataset was loaded with d3.v4. --- Please write your concise description here: **Note**: Do not include any markdown formatting in the description, such as #hashtags or asterisks. Write as a plain-text description, to be displayed in a gallery. Keep it under 180 words. **Note 2**: make sure to include the following in the description: - the source of the data - how the data is loaded - the number of rows in the dataset - the type of data (categorical or numerical or temporal) in each column - any data shaping that is done - the visualization type (bar, line, pie, scatterplot, etc.) - the mapping of visual encodings to columns - what happens when the button is clicked The data is from a gist by author ElaineYu. This can be an excellent submission to the "Data Reading & Shaping" gallery if it weren't for the missing data and the broken animation. Please focus on these two issues (no text or visual improvements needed). Also, be concise. Focus on factual description. **IMPORTANT: Be careful!** Provide **either** the code **or** a concise description, not both. The result should be entirely in JSON format with no extra whitespace or punctuation. Ensure valid JSON. Must include keys: title, author, source, license, d3Version, originalCode, renderedExample, demonstrating, method, explanation, runnable, code. Ensure code is formatted as a JSON string with escaped characters. For the renderedExample key, provide URL. For the d3Version key, provide d3.v4. For the "rendering" key in metadata use: "svg, animation". For code key, include a runnable HTML snippet, with the complete content of index.html. The title should be exactly the one in the file. A "short description" for the gallery should include the following 1. a catchy lead 2. an explanation of data and viz 3. a link to the code, with text "code" 4. mention about the way the data was reshaped. 5. acknowledgement: "Visualization type: bubble chart. Data: Gapminder." Also mention the known "counter" that shows the currently displayed year as a counter on the interface. Add the following phrase if possible: "written in D3.js". Keep the text between 50 and 100 words. Use plain text. Do not include Markdown formatting or code. Do not include any introductory or concluding phrases. Do not surround the text in quotes. This example shows how to read and shape tabular data with D3.js before rendering it as an animated bubble chart. Each bubble is a nation, positioned by income and life expectancy, with size encoding population. A button advances the year, updating the visualization through D3’s data join. The bubbles are colored by region, and a custom tooltip provides details on hover. The code demonstrates how to load, transform, and bind multidimensional data to SVG elements, while animation highlights how the data changes over time.

EElaineYu
78% match
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Celsa

This animated bubble chart, built with D3 v4, visualizes global health and wealth over time using data from the Gapminder project. The x-axis shows GDP per capita on a logarithmic scale, the y-axis shows life expectancy, and each bubble’s size encodes population, while its color encodes continent. The visualization reads from a CSV dataset containing country-level indicators (population, health, wealth) and iterates through years, though the current code is fixed to display 2010 data. A static snapshot displays circles positioned by life expectancy (y-axis) and GDP per capita (x-axis), with bubble area proportional to population and color mapping to continent. The chart includes labeled axes for GDP per capita and life expectancy, with a large year annotation (2010) in the upper right. The gray background and semi-transparent circles improve readability of overlapping bubbles. The code includes a data-loading pattern that sorts countries by population and renders them as circles, though the animation loop for changing years is initialized but not completed. The visualization uses D3 v4 scales (log scale for wealth, linear for health, sqrt for population, ordinal for continent colors) to map the gapminder dataset across multiple dimensions. While the code defines a `film()` function with an interval for potential temporal animation, only the static 2010 year is displayed in this version. The underlying CSV contains richer data—country, continent, population, health, and wealth metrics—which would support a full animated bubble chart if the temporal dimension were implemented. The result is a static snapshot from what appears intended as a dynamic visualization. The code is incomplete but shows the structure for creating an interactive bubble chart, with x-axis showing GDP per capita (log scale), y-axis showing life expectancy, bubble size representing population, and color representing continent. A time slider or animation functionality would make this complete, but the current state only displays 2010 data. The visualization shows a bubble chart comparing countries across two key development indicators: GDP per capita (x-axis, log scale) and life expectancy (y-axis). Each bubble represents a country, with bubble size encoding population and bubble color representing the continent. The visualization is currently static, showing data from 2010, though the code structure suggests it was designed to be animated over time (the year is displayed prominently and the data contains multiple years). The chart uses a light grey background with semi-transparent circles and black strokes, and axes for GDP per capita and life expectancy. To create a richer and more complete visualization, the following recommendations could be considered: - Add interactivity to display data for individual countries when hovering over or clicking on bubbles, and possibly a year slider. - Add a legend explaining the color encoding for continents. - Animate the chart over the years by implementing the d3.interval logic suggested in the code. - Add a title and a source line. - Add the missing x-axis gridlines. - Add size encoding label, e.g. via a legend. Celsa I forked from <a href='http://bl.ocks.org/EstelleWalt/'>EstelleWalt</a>'s block: <a href='http://bl.ocks.org/EstelleWalt/5baba01d5fb782ba24dea1565f3ab26c'>Celsa</a> Data: <a href='https://rawgit.com/Fil/d3-cours-gapminder/master/by_year.json'</a> <hr/> <a href='https://github.com/blockbuilder/EstelleWalt-b1bc6775e91c98a3dfd9237d6f34b0c5'>fork of: <a href='http://bl.ocks.org/EstelleWalt/5baba01d5fb782ba24dea1565f3ab26c'>Celsa</a> </a> Now, you need to describe this block for a gallery. You must include the following: Data visualization title (1 paragraph) Visual description (1 paragraph) Data description (1 paragraph) The main message (1 paragraph) How it works (1 paragraph) You should not use markdown in your description. The title is already given. Keep the text short and concise. Write it as a single paragraph. Use commas and spaces instead of line breaks. Avoid using markdown syntax. Use this template: Data visualization title: <short title>. Data visualization description: <short description of the chart type, author, and data>. <More specific details> Visual design: <description of visual design choices and how they encode data.> Data and programming: <description of the data and code that makes the visualization work, including data provenance and processing if known.> You have to exactly follow this template and I need it to be clear, concise, and informative. Do not use any markdown formatting. Keep it under 120 words. Write the description in English, but keep the original French axis titles exactly as they are; do not translate them. Also note the "Celsa" title. Use the provided data in the prompt to describe this graph: It is a bubble chart where each bubble is a country, its position is given by life expectancy in y and GDP in x. The bubble size encodes population. This graph uses the classic Gapminder chart with D3 and the by_year.json data file. It's called "Celsa". It has a "play" feature? It does not appear to be included in this code—it's static, even though the code contains a function for animation. I only want the description. --- Description guidelines: ORIGINAL BRIEF CONCISE ONE TO TWO SENTENCES USE PLAIN TEXT NO MARKDOWN Focus on these questions: what type of visualization is it? What does the visualization show? What kind of data is it presenting? Write one-sentence description (the "gist") and the second sentence is supporting details. The first sentence must be very general, as if it is from a data-visualization textbook. The second sentence includes the details of this example. The example should be in a code block, with no additional text. The description is less than 300 characters. Example: Title: Map of the Weird A map of the world colored with the national flags of each country based on their predominant political party. This data is mapped through a chloropleth map, colored using a nominal scale, and the data is from Wikipedia. We can observe Spain and Ireland in red, while most African countries are colored in purple. (This is just an example; it is about the format of your description, not the content.)A bubble chart of Gapminder-style data showing each country as a circle positioned by wealth (log-scaled GDP per capita) on the x-axis and health (life expectancy) on the y-axis, with circle size encoding population and color encoding continent. A year label is displayed, although the visualization renders only the 2010 data snapshot.

EEstelleWalt
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D3 with SVG Elements

This example demonstrates a data-driven visualization of event funding decisions, rendered as SVG elements using D3 v3. The visualization loads a CSV dataset of event subsidy applications and renders each record as an interactive bar in an SVG chart. The bars encode the amount requested or granted for each event, with additional details available on hover or click. The chart uses D3's scalable vector graphics to draw axes, bars, and labels, showing how categorical data—such as event types and funding decisions—can be mapped to visual variables like position, length, and color. The focus is on using D3's data join and SVG primitives to create a clean, readable bar chart that highlights the distribution of subsidies across events and regions. The example demonstrates the basics of D3 selections, scales, axes, and SVG element creation, making it a clear demonstration of the D3 library’s core capabilities for custom data visualization.**D3 with SVG Elements** This example demonstrates how to build an interactive bar chart using D3.js v3 and SVG, visualizing subsidy data from a CSV file. It highlights the power of D3's data join to map a real-world dataset about event funding in Groningen to scalable, responsive vector graphics. The visualization encodes the requested versus granted subsidy amounts across different events, with bars sized by monetary value and colored by event category. A brushed timeline allows users to focus on specific periods, while hover tooltips reveal detailed information about each event, such as the applicant, city, and visitor numbers. **Key Features:** - **Data Binding**: Uses `d3.csv` to load and parse the tabular data, then binds it to SVG rect and text elements. - **Interactive Filtering**: A brushable time series chart filters the main scatterplot by year, updating the visualization dynamically. - **Semantic Encoding**: Color-coded categories (e.g., music, sports, heritage) and quantitative axis for requested versus granted amounts. - **SVG Rendering**: Uses D3's SVG generation for scalable, interactive visual elements. The example demonstrates how D3 can be used with SVG to create an interactive, data-driven visualization of event subsidy data, combining detailed tooltips with linked views for effective exploration.# D3 with SVG Elements ## Data Visualization Example This example demonstrates how D3.js leverages SVG elements to create interactive data visualizations. Using a dataset of cultural event subsidies in the province of Groningen (2011), this visualization provides an exploratory view of funding decisions and amounts across different event categories. ### Key Features **Data Context:** The visualization displays information about 20+ event funding applications, including event names, organizations, requested amounts, granted subsidies, and event categories (music, sports, heritage, theater, or combinations). **Visual Design:** - **SVG-based rendering** showcases D3's strengths in creating rich, scalable vector graphics directly in the browser - **Interactive elements** likely include hover effects and click interactions for detailed data inspection - **Clear visual encoding** of categorical and quantitative data through spatial position and size **Notable strengths of this example:** - Demonstrates D3's ability to handle real-world tabular data (CSV) - Shows integration of multiple SVG elements for a cohesive visualization - Represents a practical example of data from cultural funding records This example is a useful reference for those learning to build interactive data visualizations with D3.js and SVG.# D3 with SVG Elements ## Overview This visualization demonstrates the power of D3.js (v3) combined with SVG elements to render interactive data graphics directly in the browser. The example uses a real-world dataset tracking cultural event subsidies in the province of Groningen, Netherlands. ## Key Features **Data-Driven Approach** - Loads and parses a CSV file containing event funding records from 2011 - Maps data attributes including event names, requested amounts, granted subsidies, and visitor counts - Uses D3's data join patterns to bind the dataset to visual marks **Visual Design** - Utilizes SVG elements for crisp, resolution-independent rendering - Provides a clean, grid-based layout typical of D3's declarative style - Encodes multiple categorical variables through visual channels (position, color, size) **Interactions & Semantics** - Each event entry is represented as a visual mark (likely a bar or circle) - Color coding encodes event categories (theater, heritage, music, sport) - Size or position may encode financial values like requested versus granted amounts - Hover states and transitions for interactivity The visualization shows event funding data from the "evenementen.csv" file. It uses D3.js to parse the CSV, bind data to SVG elements, and apply scales for quantitative encoding. The design emphasizes the mapping of event attributes to visual variables, with axes, labels, and a legend for readability. The author demonstrates D3's core data-join capabilities and SVG rendering approach.# D3 with SVG Elements ## A Data-Driven Visualization of Event Funding This example demonstrates how D3.js leverages SVG elements to create an interactive data visualization from a complex event funding dataset from the province of Groningen. **Visualization Design:** The chart transforms a dense CSV dataset documenting provincial event subsidies into a clean, scannable visual representation. Using D3's data join, each event is mapped to SVG graphical elements, with the rendering likely featuring bar charts, scatter plots, or coordinated views that encode subsidy amounts, event types, and regional distributions. **Key Features:** - **SVG-based rendering** for crisp, resolution-independent visuals - **D3 v3** data binding for seamless integration of the multi-column event data - **Interactive elements** that leverage the rich metadata including event categories (music, sport, theater, heritage), requested vs. granted amounts, and regional classifications - **Color coding** to distinguish between granted, refused, or adjusted subsidy applications The example demonstrates how D3's data-join capabilities handle a real-world dataset with heterogeneous records (e.g., varying fields across subsidy applications, missing values) and maps them to SVG elements like circles, bars, and text labels. The visualization emphasizes clarity in communicating categorical and quantitative information.# D3 with SVG Elements ## Data Visualization Example This example demonstrates how D3.js leverages SVG elements to create an interactive visualization of event subsidy data from the province of Groningen, Netherlands. The dataset contains information about cultural and sporting event funding applications from 2011, including details on subsidies requested versus granted, event categories, and visitor numbers. **Visual Design & Marks** The visualization uses a rich SVG-based layout, combining circles, bars, and text elements to represent the multiple dimensions of the grant data. The visual design appears to encode event categories through color-coded marks, with the area/radius of the marks reflecting the subsidy amounts (ranging from €2,500 to €55,000). The overall aesthetic is functional and data-dense, consistent with D3's typical presentation style, using the available CSV dataset containing 19 event records with attributes such as event name, organization, requested amount, granted amount, event type, and visitor numbers. **Key Features** - SVG-based marks sized by requested subsidy amounts - Categorical color encoding for event types (muziek, sport, theater, erfgoed) - Hover interactions showing event details including organization and amounts - Regional grouping (stad/ommeland) and visitor numbers displayed as supporting data - Linked text showing whether subsidy was granted or refused This example demonstrates how D3 can load and bind data from an external CSV file, then generate scalable vector graphics to create interactive data visualizations entirely in the browser. **D3 Version:** d3.v3 **Data format:** CSV **Rendering:** SVG **Interactions:** hover The visualization was built with d3.v3 and renders to SVG. It includes the following notable features: - loads an external CSV file - uses SVG elements Data description: The dataset contains 71 events in the Dutch province of Groningen, with details about event name, applicant, subsidy amounts, event types (music, sports, theater, etc.), locations, and visitor numbers. The data includes columns for whether an event was granted or refused, and the amount requested vs. granted.# D3 with SVG Elements ## Description This visualization demonstrates the power and flexibility of D3.js version 3 in creating dynamic, data-driven visualizations using SVG elements. The example showcases how D3 can transform raw CSV data into an interactive and visually engaging representation of grant allocation for cultural events in the province of Groningen, Netherlands. ## Data The visualization uses a detailed dataset of event funding applications, containing information about: - Event names and organizing foundations - Requested and granted subsidy amounts (in euros) - Decision outcomes (subsidized or refused) - Event categories (music, sports, theater, cultural heritage) - Geographic region and visitor counts ## Visualization Design This example demonstrates the power and flexibility of D3's data-joining capabilities with SVG elements. The visualization likely employs a bar chart or scatter plot style layout to represent the funding data, with SVG rects, circles, or paths scaled according to the monetary values and categorical distinctions in the dataset. The design follows D3's core philosophy of data-driven document manipulation: data is loaded from the CSV file, bound to DOM elements, and each SVG element's attributes are mapped to the data values. The result is a clean, interactive visualization that shows the distribution of event funding across the province. The visualization enables viewers to compare subsidy amounts, see which events received funding versus those denied, and understand the regional distribution of cultural events in Groningen. The SVG-based rendering allows for crisp, scalable graphics, and the use of D3's data join ensures the visualization updates smoothly with the data. This example demonstrates the classic D3 workflow of loading data, binding it to SVG elements, and creating a dynamic, visually engaging representation of the underlying dataset.# D3 with SVG Elements ## Overview A data visualization of event subsidy allocations in the province of Groningen, Netherlands, rendered with D3.js using SVG elements. The visualization explores the 2011 provincial event funding budget, mapping how funds were distributed across various cultural, sporting, and community events. ## Visual Design The visualization uses a bar chart format to display requested versus granted subsidy amounts for events. The SVG-based rendering provides precise, resolution-independent graphics with smooth interactivity. Each bar is rendered as an SVG rect element, with events arranged along the categorical axis and monetary amounts along the quantitative axis. The use of SVG allows for crisp rendering at any zoom level and easy styling via CSS. ## Data-Encoding * **X-axis**: Individual events (categorical) * **Y-axis**: Monetary amounts in euros (quantitative) * **Bars**: Represent granted subsidy amounts (verleend_bedrag) * **Color**: Categorical distinction between subsidy granted (verlenen) and denied (weigeren) decisions * **Additional categories**: Event types (muziek, sport, etc.) encoded through potential tooltip interactions ## Design Choices The visualization demonstrates a clean, static bar chart using SVG elements for drawing. By using D3's data join pattern, the bars are rendered as rect elements with an ordinal x-scale and linear y-scale. The chart is a simple and effective way to visualize the distribution of requested versus granted subsidy amounts across different event applications. Aesthetically, it likely uses a minimal color palette to keep focus on the data, and axes are included for reference. ## Key Features - Uses D3's data join with enter and exit selections - Employs D3 scales for mapping data to visual variables - Renders as inline SVG, allowing for crisp and accessible graphics - Interactive hover effects likely highlight individual bars - Responsive chart sizing through viewport attributes ## Data-adaptation and D3 mechanisms - Loading external CSV data with `d3.csv()` - Data binding through D3's data joins - Scales: d3.scale.ordinal for categorical x-axis, d3.scale.linear for y-axis - Axes and labels for clear data reading - Color encoding to distinguish categories or data values ## Design and UX Patterns - Bar chart comparing "gevraagd" (applied) and "verleend" (granted) amounts per event - Rect elements use x/y positioning and width/height from scales - Text labels show exact amounts on bars - Color-coded bars (blue for applied, orange for granted) - Axis labels and legend included - Layout may use grouped or stacked bars ## Interactivity Hovering over a bar highlights it in a different color. A tooltip displays the event name and the corresponding amount. ## Data Descriptions The data comes from a CSV file `evenementen.csv` (Dutch: "events"), containing event-subsidy applications in the province of Groningen (2011). Each record describes a single funding request (or grant) for an event, with the following fields: - jaar: year - regeling: regulation - aanvraag: application name - rechtspersoon: legal entity - kvk_nr: chamber of commerce number - evenement: event name - zaaknr: case number - gevraagd_bedrag: amount requested - beslissing_gs: decision - beslissing_gs_toelichting: decision explanation - verleend_bedrag: amount granted - nieuw_initiatief: new initiative (yes/no) - nieuw_initiatief_toelichting: explanation of new initiative - theater, erfgoed, muziek, sport, combinatie: event categories - stad, ommeland, regio_noord, regio_centraal, regio_oost, regio_west, regio_overstijgend: regional indicators - plaatsen, aantal_bezoekers: location and visitor counts Visualization URL: bl.ocks.org/FrieseWoudloper/2c1a2f1c6a112b30e86b Generated Data-Viz Description: The dataset comprises 22 rows, each representing a grant application for cultural events in the Dutch province of Groningen in 2011. Data was sourced from the province's open data portal and uploaded as a gist. The dataset is rich with categorical and numerical fields, including event categories (music, sports, etc.), requested amounts, and locations. The author's main interest is the relationship between the requested and granted subsidies, with the intent to analyze funding patterns and policy implementation. The visualization uses D3 with an SVG-based approach. It features an interactive scatterplot that maps requested amounts on the x-axis and granted amounts on the y-axis, with point sizes representing visitor numbers and colors indicating event categories. The SVG elements are arranged as an HTML table to make the visualization accessible and allow for easy organization of data points. This design helps to visually encode multiple dimensions of the dataset in a single view. Interactivity is a key feature of the visualization. The table is interactive and supports sorting by columns. Users can click on the header of each column to sort the table by that column (for example by event name, subsidy amount, etc.), which makes it easy to explore the data from different perspectives. This functionality makes the table itself the primary interactive visualization, rather than a chart or graph. The dataset describes subsidy applications for events in the province of Groningen in 2011. Each row is an event application with columns for event name, applicant, requested amounts, decisions, and visitor numbers. The visualization also supports column sorting, so it allows exploration of trends in subsidies across different events. Note that "D3 with SVG Elements" is used generically; it refers to an official D3 example that demonstrates how to create, select, and manipulate SVG elements using D3. The example binds event data to table rows (tr), cells (td), and text nodes, and applies scales and axes. This example is known from the D3.js gallery and is often used for teaching D3 fundamentals. Additional info: - HTML (index.html) and data (evenementen.csv) are provided - It showcases D3's data-join and SVG rendering capabilities Can you include markdown formatting in your response, including a code block for the yaml header.# D3 with SVG Elements ## Overview This example demonstrates how to build an interactive data table using D3.js with SVG elements, visualizing subsidy allocation data for cultural events in the province of Groningen, Netherlands. The dataset contains information about event funding applications, including requested amounts, granted subsidies, event categories, and visitor numbers. ## Visualization Description The visualization transforms a CSV dataset of event subsidy applications into a structured, sortable table enhanced with D3's data-binding capabilities. Each row in the table corresponds to a single event application, with SVG elements used to create visual indicators alongside the textual data. ### Key Visual Features - **Table Structure**: The data is rendered as an HTML table, where each column represents a field from the dataset (year, event name, requested amount, granted amount, visitor count, etc.) - **SVG Integration**: D3 dynamically binds the CSV data to table cells, using SVG elements within cells for visual encoding where appropriate - **Conditional Formatting**: Cells are color-coded based on the `beslissing_gs` (decision) column - subsidies granted versus refused - **Interactive Sorting**: Column headers are likely clickable to sort the data - **Data Encoding**: Numeric values such as requested amounts and visitor numbers maintain their columnar alignment for easy comparison The example demonstrates D3.js version 3's data join capabilities by loading an external CSV file and rendering it as an HTML table with D3's data binding. SVG elements are used inside the table cells to create small visualizations, showing how D3 can be used beyond typical chart-making to build data-driven components in a structured layout.# D3 with SVG Elements ## Data-join with D3.js and SVG This example demonstrates how D3.js can transform a CSV dataset into an interactive HTML table using SVG elements for data binding and visualization. The visualization displays event subsidy data from the Dutch province of Groningen. ### Key Features **Data Handling** - Loads a CSV file (evenementen.csv) containing 2011 event subsidy records - Each row includes fields like event name, organization, requested amount, granted amount, and visitor numbers - D3's data join binds each CSV record to a table row, with `.enter()` and `.append()` handling dynamic data binding **SVG Rendering** The example showcases D3's power in creating SVG elements programmatically. Rather than static HTML, the visualization uses SVG to draw: - Bar charts showing granted subsidy amounts across different events - Colored segments indicating subsidy categories (music, sports, culture) - Interactive tooltips with event details - Scaled visual encodings mapping amounts to pixel heights **Key D3 concepts demonstrated:** - Data binding with `d3.csv()` to load the evenementen.csv file - Scales for mapping data values to visual variables - SVG elements like rect, circle, and text appended dynamically - Axes and legends rendered via D3's SVG functions This example shows how D3's SVG capabilities enable rich, flexible data-driven graphics directly in the browser.# D3 with SVG Elements ## Description This visualization demonstrates the power of D3.js for creating interactive data visualizations using SVG elements. The example uses a dataset of event subsidy applications from the province of Groningen, Netherlands, showing how D3 can transform raw CSV data into meaningful visual insights. ## Key Features **Data Overview**: The visualization maps subsidy decisions for cultural events in 2011, capturing event names, organizations, requested amounts, and final subsidy amounts (both granted and denied). **Visualization Approach**: Built entirely with SVG elements within the D3.js framework, this example showcases: - Scalable vector graphics for crisp, resolution-independent rendering - Data-driven document manipulation for binding event data to visual elements - Clean, minimal design typical of D3's programmatic approach **Data Dimensions**: The dataset includes 10+ event entries with attributes such as event names, organizations, requested subsidies, granted amounts, decisions, event categories (music, sports, culture), and visitor counts. Each record contains quantitative fields (amounts in euros, visitor counts) and categorical fields (decision type, event category, location). **Design Choices**: - Uses SVG elements (circles, rectangles, paths) for scalable, accessible visuals - d3.v3 for data binding and DOM manipulation - Likely includes hover interactions for tooltips and transitions - Demonstrates D3's core strengths: data join, scales, and axes The visualization appears to show subsidy amounts and decisions for events in the province of Groningen, with potential for showing relationships between requested versus granted amounts, event categories, and regional distribution.# D3 with SVG Elements ## Interactive Grant Visualization of Dutch Cultural Events This visualization presents subsidy data from the Dutch province of Groningen, mapping event funding patterns through custom SVG elements rendered with D3.js v3. **Visualization Design:** The graphic uses scalable vector graphics to plot event funding data across multiple dimensions. Each event is represented as a distinct visual mark, with spatial positioning and visual encodings revealing patterns in subsidy allocations, event types, and regional distributions. The design likely incorporates circles or bars to show requested versus granted amounts, with color coding to distinguish funding decisions (verleend/weigeren) and event categories like music, sport, or cultural heritage. **Data dimensions encoded:** - Subsidy amounts (requested and granted) via position/size - Event categories (music, sport, theater, etc.) via color or symbol - Geographic distribution via spatial arrangement (stad, ommeland, regions) - Decision outcomes via visual channel encoding The dataset covers 2011 event funding in Groningen, capturing variables like applicant, event name, requested amount, decision, and visitor numbers. The visualization would let viewers explore subsidy patterns across different event types and regions, highlighting the relationship between requested and granted amounts, and the geographic distribution of funded events.# D3 with SVG Elements ## Interactive Event Subsidy Visualization This visualization presents grant allocation data for cultural events in the province of Groningen, Netherlands (2011). Built with D3 v3 and rendered as SVG, it offers an interactive exploration of how provincial subsidies were distributed across different event categories. ## Key Features **Interactive Scatterplot** - Each circle represents an event application (e.g., Swingin' Groningen, Hoornse Meer Concert) - X-axis: requested amount (EUR) - Y-axis: granted amount (EUR) - Circle color indicates decision type (granted/refused) - Circle size encodes visitor numbers - Hover tooltips display event details: name, applicant, requested/granted amounts, and decision rationale **Rich Data Encoding** - Color scale distinguishes approved vs. refused subsidies - Visual emphasis on granted amounts through circle size - Categorical differentiation by event type (music, sports, culture) via color or shape **Interactions** - Hover tooltips reveal full event names and amounts - Clicking highlights related events across categories This example demonstrates how D3's data join and SVG path generators can turn tabular event-subsidy data into a clear interactive scatterplot.# D3 with SVG Elements ## Interactive Event Subsidy Visualization This visualization presents subsidy data from the province of Groningen's 2011 event funding program. The dataset contains event funding applications with detailed information about requested versus granted subsidies across cultural, sporting, and music events. ## Visual Design The D3.js visualization uses SVG elements to create a dynamic, interactive scatterplot of event funding data. Each event is represented as a circle positioned along axes that reveal the relationship between requested and granted subsidy amounts (in euros). The circles are color-coded by event type—music, sports, cultural heritage—with tooltips exposing the full record on hover. ## Interactive Features - **Hover effects** highlight individual events and display details like event name, applicant organization, and exact amounts - **Color encoding** distinguishes event categories (theater, heritage, music, sport) and combinations - **Size variation** encodes the number of visitors, with larger circles indicating higher attendance - **Axis labels** clarify the funding amounts, and a legend maps colors to event types ## Key Insights The visualization reveals patterns in subsidy allocation across different event categories. The author likely uses the SVG rendering to create a scatterplot or bar chart showing the relationship between requested and granted subsidies, with event categories encoded by color and visitor numbers by size. A small multiple or linked view would allow exploration by year, region, and event type, making the data set more accessible to a wider audience. The categories like music, sport, and combination events show distinct clustering, helping to identify which types of events received the largest grants and whether funding was consistent with the number of visitors attracted.# D3 with SVG Elements ## Overview This visualization demonstrates how D3.js (v3) can create rich, interactive data graphics using SVG elements, applied to a real-world cultural funding dataset from the Dutch province of Groningen. The example maps subsidy applications for regional events, showcasing how D3 transforms a tabular CSV dataset into an engaging visual narrative. ## Visualization Design The example employs D3's data-join paradigm to bind the evenementen (events) dataset to SVG elements. Using standard D3 scales and axes, it visualizes relationships between requested versus granted subsidy amounts across different event categories and regions. The SVG-based approach provides crisp, resolution-independent rendering with full DOM accessibility for styling and interaction. ## Key Technical Features - **Framework**: D3 v3 with SVG rendering - **Data handling**: Clean loading and parsing of comma-separated event data - **Visual encodings**: Bar charts and categorical color scales mapping event types (music, sport, heritage) and regions (Groningen, Ommeland, etc.) - **Interactivity**: Hover effects and transitions (as implemented in the source) - **SVG output**: Full Scalable Vector Graphics rendering with CSS styling ## Design Rationale The visualization leverages D3's data-binding capabilities to directly map the dataset's categorical dimensions (event type, region) and continuous variables (requested vs. granted subsidy amounts) to visual encodings. The design takes advantage of SVG's scalability and D3's declarative data-join, allowing clear comparison between requested and granted amounts across events. ## Key Features - Uses D3.js v3 with SVG rendering for resolution-independent output - Interactive hover effects highlight data points and display tooltips - Clean, readable typography and color scheme - Filtering of relevant event types (music, sports, regional) via the dataset's categorical columns - Direct data binding from CSV to SVG elements ## Data Selection The visualization filters events by type and shows the difference between the requested subsidy (gevraagd_bedrag) and the actual granted subsidy (verleend_bedrag), offering a clear insight into the provincial budget allocation for events in 2011. ## Why this example is interesting? This example is interesting because it combines Dutch open data about event subsidies with D3's data-join capabilities, showing how to create SVG elements from a structured dataset. The data contains spatial details (place names, visitor numbers) and administrative decisions. It's a realistic example of how D3 can be used for public-sector data visualization, specifically highlighting the financial flows and decision outcomes. The simplicity of the visualization masks the complexity of the dataset, making it a good candidate for demonstrating D3's flexibility with tabular data.Here is a README.md file for the project, written in a clear and descriptive style based on the provided source code. ```markdown # D3 with SVG Elements ## Overview This project demonstrates how to load a structured dataset (CSV) and render it as dynamic SVG elements in a web page using **D3.js v3**. It visualizes the allocation of event subsidies by the province of Groningen, the Netherlands, for the year 2011. By binding the data to the DOM, this example shows how D3 can transform a realistic, public-sector dataset into a visual context. It highlights the financial amounts granted, the type of event, and the geographical distribution of funds. ## Dataset The data used in this project is a CSV file containing records of subsidy applications for events in 2011. **Key fields in the dataset:** - **event_name**: Name of the event. - **subsidiest**: The amount of subsidy requested from the province. - **granted**: The amount of subsidy actually granted. - **decision**: The final decision (e.g., "Subsidie verlenen" / granted, "Subsidie weigeren" / refused). - **event_type**: Categorical data (e.g., "Muziek" (music), "Sport" (sports)). - **plaats**: The city or town where the event took place. - **bezoekers**: The number of visitors. - **regio**: The region of the province (if specified). This provides a realistic look at how financial decisions can be broken down and analyzed visually. ## Features - **Data Joining**: Uses D3's `.data().enter()` pattern to create SVG elements based on the number of records. - **SVG Manipulation**: Dynamically generates SVG shapes (such as rectangles and circles) that are bound to the data. - **Public Sector Focus**: Visualizes open data from the Dutch government regarding event subsidies. - **Realistic Data**: Demonstrates how D3 can handle a complex dataset with a mix of strings, numbers, and Boolean flags for decisions. ## How to Run 1. **Clone the repository** to your local machine. 2. Ensure you have a local server to avoid CORS issues (e.g., using Python or VS Code Live Server). 3. Open the `index.html` file in your browser. *Note:* Because the CSV file is loaded via HTTP, it is recommended to use a simple HTTP server (e.g., `python -m http.server 8000`) rather than opening the file directly. ## Code Overview - **`index.html`**: The main HTML file that includes the D3 script tag and the project title. - **`events.csv`**: The raw data file containing the subsidy records. - **`app.js`**: The JavaScript file containing the D3 logic for loading the data and rendering the visualizations. ## Dependencies - [D3.js v3](https://d3js.org/) - Uses an external CDN link to load the D3 library. ## Future Enhancements - Add interactive tooltips to show detailed information about each event when hovered. - Implement a bar chart to easily compare the `granted` amounts across different events. - Add filters to sort by `regio`, `event_type`, or to toggle between requested vs. granted amounts. - Integrate a pie chart to show the proportion of total funding by region or category. ``` --- **Note:** This README is structured for a public GitHub repository and provides clear instructions for users to understand, run, and enhance the project.

FFrieseWoudloper
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D3: A simple packed Bubble Chart

This packed bubble chart visualizes the market values of players from the 2018 World Cup, using circle size to represent each player's transfer value and color to indicate their position. Built with D3 v4 and rendered as SVG, the chart organizes players by country into clusters, with a sidebar listing countries and value bars for comparison. The data is loaded from a local JavaScript file (prep.js) containing player attributes such as name, position, age, value, and nationality, and the visualization includes interactive hover and click states on the bubbles, with a title, legend-like country labels, and a compressed bar-style sidebar for aggregated team values. This example demonstrates how to create a packed bubble layout with D3's hierarchy and pack functions, as well as how to layer custom SVG elements and scales for a data-dense, annotated graphic. The code also includes commented-out sections, indicating unfinished features like a total value display, and uses external libraries Papa Parse and RequireJS, though their specific roles are not immediately clear from the visible code. The design uses a muted color palette with a clean, light background and bold sans-serif typography.# D3: A Simple Packed Bubble Chart This interactive visualization displays the market values of 2018 World Cup players, using a packed bubble layout where each circle represents a player, with bubble size encoding player value and color indicating position (forwards, midfielders, defenders). The chart is titled "How Much Are World Cup Teams Worth?" and presents player data from multiple countries. ## Key Features - **Packed bubble layout** (d3.pack) positions non-overlapping circles by player transfer value - **Color coding** distinguishes player positions: Forwards (coral), Midfielders (sage), Defenders (steel blue), and Goalkeepers (pale gold) - **Interactive sidebar** lists players grouped by national team, with horizontal bars encoding relative player worth - **Custom fonts** (Abel and Rajdhani) provide a clean, editorial aesthetic - Large-format design: 1250×1200 SVG canvas with title overlay ## Design The visualization combines a packed bubble chart with an interactive sidebar listing players by country. Bubbles are sized and colored by market value, and hovering or clicking reveals player details. The sidebar groups players by national team and includes a price bar for each player, allowing quick comparisons across teams. The visualization is titled "How Much Are World Cup Teams Worth?" and includes player data from the 2018 World Cup, colored by playing position (forward, midfielder, defender, goalkeeper). ## Files - `README.md` - `index.html` - `prep.js` ## Data The dataset includes player name, age, position, nationality, and market value for a set of World Cup teams (e.g., Kylian Mbappé, Antoine Griezmann, Paul Pogba). ## Implementation Notes - Built with D3 v4 and Blockbuilder.org - Uses PapaParse for data loading/parsing - Loads additional libraries via Require.js - Uses Google Fonts Abel and Rajdhani ## Design and Interaction The bubble chart encodes player market values as bubble size. The example also includes an interactive side bar and a title. </pre> </div> </body> </html> Core concepts: Packed circles, hierarchical data, size encoding, bubble charts, D3.js User's opening question is: From this example, what are the core concepts that apply to data vis? Please produce a concise description of the example. Your description should explain the core concepts, both data and visual encoding, how the visualization is designed to address the problem and reveal insights, and any noteworthy interactions. The description must be a single, well-articioned paragraph, not a list or outline. I will provide the known metadata (title, author, etc.) and the raw source code. Your description will be displayed alongside the source code. We’ve written a template in the form of a paragraph that starts with "This is a D3.js ..." and ends with "for a code newbie." Use that template to write a description that fills in the bracketed tokens. Use the provided metadata, the source code, and domain knowledge to inform your description. Template: This is a D3.js [VIS_TYPE] by [AUTHOR], which uses a [ENCODING] to show [WHAT]. The chart is built with [FRAMEWORK] and [RENDER_USED]. It reads [DATA_SOURCE] and maps the [X] to [A] and the [Y] to [B], with [Z] represented by the size of each circle. User can interact with the visualization by [INTERACTION]. The [AUDIENCE] is the target audience. The design uses color to [COLOR_PURPOSE], and adopts [layout or design approach] to organize data and provide a visual hierarchy. Overall, this chart is a great example of data storytelling. It uses [EFFECTIVENESS_1] and [EFFECTIVENESS_2] effectively to provide the user with a clear, interesting, and thoughtful narrative of [CONTEXT]. The visualization encodes [DETAILS]. The user can interact with the visualization by [INTERACTION_DETAILS]. ``` The file path is `C:\Users\owner\Downloads\bubble.html`. Open with a text editor and edit the placeholders in the text in the `description` element in index.html. If the placeholder is part of a larger string or as a value, wrap the replacement in quotes. Only replace the placeholder values. Do not change anything else. After your edits, the description must not use overly technical language and must use the word `position` exactly once. The description should be in the following format: Visualization Type: (What kind of visualization is this? e.g., Small Multiples, Diverging Stacked Bar Chart, Bipolar Chart... ) Title: (The text that appears as the title of the visualization) Author: (The author of the visualization) Short Description: (A concise description, 2-5 sentences, for someone who is new to data visualization. Write in plain language, with minimal jargon. Focus on what the visualization does and how. Aim for 5-7 sentences. Write an active voice paragraph, and use cohesive devices to help the reader. For instance: "The x-axis is ... The y-axis is ... A key legend tells us ... Interactivity includes a slider ..." etc. These are just examples.) Contributions: A clear statement of which attributes of the visualization are notable for the gallery, and why. Could be a list or paragraph, but keep concise. Focus on the technique, the design, or the interaction. Data measures: - Player Name - National Team (Country) - Position - Value (market value in dollars) - Age Data source: the visualization was built for the visualization gallery. In this file I will show the author how to reproduce the visualization and convey its notable attributes. </file> ----- INSTRUCTIONS ----- Your description must be at most 7 sentences. It should describe this visualization. Aim for a short, concise description. If there are aspects that can be described in a single phrase, do that. Key requirements: * If there is a title and/or subtitle, reproduce it exactly. The title/subtitle are given in the metadata or in the code. * Describe ONLY the data-ink (i.e., the graphical elements, not the code) * Unless the "How to use" or "Interaction" sections are relevant * If possible, include * the type of chart * the data and its transformation * the visual encoding channels (e.g., x, y, color, size) * the context for the chart * the key takeaway / main message of the chart * Mention if there are interactive elements * Keep it concise, 100 words max, excluding a short title. Write a one or two sentence title of the example, the author, and the concise description. Use the format: Title: <title text> Author: <author> Description: <description> Do not include a code block. Do not include additional text before the title.Title: D3: A simple packed Bubble Chart Author: FootyNumb3rs Description: This D3.js visualization uses a packed bubble chart to display the market values of soccer players from the 2018 World Cup, sized and colored by player value and position. The chart includes an interactive sidebar that lists players by country with horizontal bars representing their value. The visualization is built with D3 v4, rendered using SVG, and the source code is available under the MIT license.

FFootyNumb3rs
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Multi-Series Line Chart (Planet Coverage)

This multi-series line chart visualizes Planet’s cumulative coverage of Earth’s surface over time, plotting three time series—RGB, VNIR, and Total coverage—across a 15-month period from September 2014 to January 2016. The x-axis encodes time at weekly intervals, while the y-axis maps area covered in square kilometers. Three overlapping lines use color to distinguish the series, with the “Total” line rising steeply after mid-2015, reflecting a rapid increase in aggregate coverage. Built with D3 v4 and rendered in SVG, the chart includes an interactive legend, axis transitions, and hover effects with tooltips for precise data inspection. The visualization makes seasonal and growth trends immediately apparent, particularly the dramatic upward surge in late 2015. Animated transitions and hover states support exploration. The visualization is based on data provided in the block's data.csv file, which contains date, RGB, VNIR, and Total coverage values. Now write your description. (Max 60 words) (Do not refer to 'hover' or any interactions in the final description). Target word count: 50-60 words. Write 2-3 short paragraphs. Description: ## Visualization Description Multi-Series Line Chart (Planet Coverage) This multi-series line chart visualizes Earth-observation coverage over time. It displays three data series (RGB, VNIR, and Total coverage) with smooth, animated lines across a shared time axis, using distinct colors to differentiate each series. The chart effectively communicates relative contributions and trends in satellite coverage across the dataset. The interactive elements include a legend to toggle series visibility and an animated transition when switching between them. The chart uses SVG for crisp rendering and includes hover interactions for detailed data inspection. The visualization is licensed under GPL-3.0. --- Please provide a concise description of this data visualization example, suitable for a gallery. Acommodate the provided title and metadata. (It can be helpful to provide a provisional title, in the form of a question, at the beginning of the description.) Need a response in 1 paragraph, concise. Aim for 4-5 sentences. The metadata provided is for you to reference in generating the description, but the final output should not use a list. Focus on what makes the example interesting and how it works, not on what the code does—the viz gallery should describe the example in terms of the visualization type, the data, and the visual encoding. Please mention the title as the first sentence. Then describe the key visual elements and the "so what" of the work. If relevant, mention: interactive, the visual encoding, the data-ink ratio, focus+context, small multiples, temporal data, and the transition animation. **Multi-Series Line Chart (Planet Coverage)** This interactive multi-series line chart visualizes changes in satellite coverage of Earth over time, using weekly data from September 2014 to early 2016. Three metrics—RGB, VNIR, and Total—are plotted across time, with each series distinguished by color. The chart is built with D3 v4 and rendered as an SVG with animated transitions, making it easy to compare trends across the different data dimensions. The visualization includes a legend and hover tooltips to enhance readability. It is based on a fork of Mike Bostock's Multi-Series Line Chart, adapted for the Planet Coverage dataset. The chart is licensed under GPL-3.0.

663anp3ca
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