Skip to main content
SimilarExport ZIP
100%

Melting Data for Multiple Lines

✓ Published1🌍 Public
Bbprokopowicz
Last edited Apr 6, 2020
Created on Apr 6, 2020

This example visualizes population projections for global regions from 1950 to 2100 as multiple smoothed lines, each colored by region via an ordinal color scale and accompanied by a legend. The chart uses d3.scaleTime, d3.scaleLinear, d3.axisLeft, and d3.axisBottom to render axes, while d3.line with d3.curveBasis draws the paths. Data is loaded from CSV files and melted into a tidy format before rendering as SVG.

AI-generated description

D3 course follow along.

MIT Licensed

Similar vizzes

Loading thumbnail…

Melting Data for Multiple Lines

This example visualizes population estimates and projections over time by region, with multiple colored lines tracing each region's trajectory from 1950 to 2100. The chart emphasizes final population values by sorting lines in descending order and using a legend that displays region names with corresponding colors. Built with D3 v5, the code employs `scaleTime`, `scaleLinear`, `scaleOrdinal`, `line`, `curveBasis`, `nest`, and `axisBottom`/`axisLeft` to render the SVG. Data is loaded and melted from two CSV files containing UN population estimates and medium variant projections.

PPatricio Albornoz
95% match
Loading thumbnail…

Melting Data for multiple lines

This example visualizes population estimates and projections by region from 1950 to 2100, using multiple overlaid lines to show trends over time. The view encodes time on a linear scale and population on a log-free axis, with a categorical color legend identifying each region. The underlying dataset is merged from two UN CSV files using a custom data-melting process that reshapes wide-format rows into long-format records. The chart uses d3.scaleTime, d3.scaleLinear, d3.nest, and d3.line with curveBasis to render smooth paths, while a reusable colorLegend component draws the SVG legend.

Pprincit
95% match
Loading thumbnail…

Melting Data for Multiple Lines

This example shows population over time by region for the world, displaying multiple smooth lines on a chart with a color legend to distinguish regions. The data is melted from a wide CSV format into tidy rows, combining historical estimates from 1950 to 2014 with medium-variant projections from 2015 to 2100 from the UN. The chart uses D3 v5’s `scaleTime`, `scaleLinear`, `scaleOrdinal`, `line` with `curveBasis`, `nest` for grouping by region, and custom axis and legend components. Lines are rendered as SVG paths with a multiply blend mode, and the axis labels use custom formatting for population values.

Ssafdarawan
94% match
Loading thumbnail…

Melting Data for Multiple Lines

This example shows population over time by region for the world, displaying multiple smooth lines on a chart with a color legend to distinguish regions. The data is melted from a wide CSV format into tidy rows, combining historical estimates from 1950 to 2014 with medium-variant projections from 2015 to 2100 from the UN. The chart uses D3 v5’s `scaleTime`, `scaleLinear`, `scaleOrdinal`, `line` with `curveBasis`, `nest` for grouping by region, and custom axis and legend components. Lines are rendered as SVG paths with a multiply blend mode, and the axis labels use custom formatting for population values.

Ssafdarawan
94% match
Loading thumbnail…

Melting Data for Multiple Lines

This example shows world population projections from 1950 to 2100 as multiple smoothed lines, with a vertical line marking the currently selected year. The chart uses a color legend to distinguish regions, and hovering over the chart updates the selected year. It uses `d3.nest()` to group data, `d3.line()` with `curveBasis` for path generation, and `d3.scaleTime`/`d3.scaleLinear` for axes. The data is melted from CSV files, and the view is rendered as an SVG.

KKurt J Nilsen
94% match
Loading thumbnail…

Melting Data for Multiple Lines

This example visualizes population over time by region, showing six major world regions as distinct colored lines from 1950 to 2100. The chart uses a line generator with curveBasis for smooth paths, while a color legend identifies each region. The code melts wide-format UN population data into tidy rows, combining estimates and medium-variant projections from CSV files. It employs d3.scaleTime, d3.scaleLinear, d3.scaleOrdinal, d3.nest, and d3.axisBottom/axisLeft, rendering as SVG with custom tick formatting for abbreviated population values.

Ppplomith
94% match
Loading thumbnail…

Melting Data for Multiple Lines

This chart displays population estimates and projections from 1950 to 2100 for regions worldwide, using multiple lines colored by country and sorted by their final population values. The visualization relies on d3.csv to load UN data, d3.nest to group rows by region, and d3.line with curveBasis to generate smooth paths. A custom colorLegend module renders the categorical color scale, while axes are built with d3.axisLeft and d3.axisBottom, and the data is reshaped through a custom “melting” process that converts wide-format year columns into long-format rows.

Ssafdarawan
94% match
Loading thumbnail…

Melting Data for Multiple Lines

This example visualizes global population trends from 1950 to 2100, showing how different regions change over time as multiple lines. The chart uses `d3.line` with `curveBasis` to draw smooth paths, with color encoding via `d3.scaleOrdinal` and a custom `colorLegend`. Data is melted from wide to long format using a custom `melt` function that processes UN population estimates from two CSV files, concatenating historical estimates with medium-variant projections. The visualization includes axis labels, gridlines, and a legend, with regions sorted by their final population value.

Mmeihaoming
93% match