Melting Data for Multiple Lines

100%

Melting Data for Multiple Lines

✓ Published0🌍 Public

Last edited Jun 1, 2020

Created on Jun 1, 2020

Forked from Melting Data for Multiple Lines

This visualization animates the growth of COVID-19 confirmed cases across multiple countries, using stacked line paths that extend day-by-day. The y-axis applies a random multiplier to China’s data after day 52, creating a visual distortion. Built with D3 v5, it uses `scaleLinear`, `nest`, and `line` with `curveBasis` to generate smooth trajectories, while `transition` animates the lines. Country labels and color-coded legends are rendered as SVG text and circles, with data sourced from CSV files.

AI-generated description

MIT Licensed

Similar vizzes

Loading thumbnail…

Melting Data for Multiple Lines

This example visualizes COVID-19 confirmed cases across several countries using multiple animated line charts. The visualization uses D3.js v5 with SVG rendering to display data from CSV files, showing how cases grow over days since the outbreak began. The implementation employs D3 scales for linear and time axes, the `nest()` function to group data by country, and `line()` with `curveBasis` to generate smooth paths. A color legend and animated transitions update the lines dynamically, while a custom `yValue` function adds random variance to China’s data for demonstration purposes.

Vvans01

95% match

Loading thumbnail…

Melting Data for Multiple Lines

This visualization uses D3.js (v5) to animate the growth of confirmed COVID-19 cases across multiple countries, displaying them as a series of colored lines on a scatter plot. The lines are drawn using `d3.line` with `curveBasis` smoothing, and each country is distinguished by color from `d3.schemeCategory10`. The animation progresses through time by updating the data on a timer, redrawing the line paths with `transition().duration(1000)`, and the labels and circles animate to track the latest values. Data is processed from CSV files, where the code melts the data from a wide format into a long format for plotting.

Cczy68

94% match

Loading thumbnail…

Melting Data for Multiple Lines

This visualization animates COVID-19 confirmed case counts for several countries, showing each country’s trajectory as a distinct colored line over days since the outbreak began. The chart uses D3 v5, with scales from `scaleLinear` and `scaleOrdinal`, and draws paths via `d3.line` with a basis curve. The data comes from a CSV file that was melted from a wider format. As the animation progresses, it redraws the lines and updates circles and labels at each line’s endpoint.

Rrigt

93% match

Loading thumbnail…

Melting Data for Multiple Lines

This example visualizes the number of confirmed COVID-19 cases over time for several countries, using animated multiple lines that appear to "melt" as they extend. The chart initially loads all data, then after a brief delay, it progressively renders lines day by day up to day 92, with each line colored and labeled for its country. The code uses D3 v5 APIs such as `nest()`, `line()`, `scaleLinear()`, and `transition()` to update the paths, while the `yValue` function adds random noise to China’s data after day 52, creating a melting effect. Data is loaded from CSV files and rendered as SVG.

Ssafdarawan

93% match

Loading thumbnail…

Melting Data for Multiple Lines

This example animates cumulative confirmed COVID-19 cases across multiple countries, plotting each country as a colored line over days since its first confirmed case. The animation progressively reveals data up to day 92, with circles and labels marking the latest value of each line. Built with D3 v5, it uses `nest()` to group data by country, `scaleLinear` for axes, `line` with `curveBasis` for smooth paths, and transitions to update paths over time. The data comes from two CSV files processed via `loadAndProcessData`.

Iinroadings

92% match

Loading thumbnail…

Melting Data for Multiple Lines

This example shows animated multi-line charts tracking COVID-19 confirmed cases over time for several countries, with colored lines that progressively extend as the day counter advances. A legend and axis labels accompany the visualization. The code uses d3.nest() to group data by country, d3.line() with curveBasis for smooth paths, and d3.scaleLinear for x/y axes. Circle markers and text labels display the latest value of each line. Data comes from CSV files processed through a custom loadAndProcessData function.

VVikas Pathak

91% match

Loading thumbnail…

Melting Data for Multiple Lines

This example shows COVID-19 death counts for multiple countries over time, with each country represented as a colored line and labeled at its final value. The visualization uses a line generator with a basis curve, along with nested data grouping to separate countries. It displays the data on linear scales for both axes and includes a color legend in the corner. The code imports and processes data from two CSV files, filtering and merging them to build the dataset, and renders the chart as an SVG using D3 v5 APIs like `d3.nest`, `d3.line`, and `d3.axisLeft`.

Iinroadings

88% match

Loading thumbnail…

Melting Data for Multiple Lines

This visualization tracks cumulative COVID-19 death counts across multiple countries over time, using a multi-line chart that plots deaths against days since the first reported case. The chart includes a draggable vertical line that, when positioned at a specific day, reveals the death toll for each country at that point in time. The code uses D3 v5 APIs including `nest()`, `scaleLinear`, and `line` with `curveBasis` to generate the paths, while a custom `colorLegend` module displays the categorical color scale using `scaleOrdinal` and `schemeCategory10`. Data comes from the included CSV files `data01.csv` and `data02.csv`.

Iinroadings

87% match