Simple Radar Chart

Smoothed Radar Chart

This visualization displays a smoothed radar chart comparing qualitative and accessibility metrics for Paris Nord railway station, including cleanliness, security, comfort, and average distance. The dataset comes from a CSV file containing station attributes. The code uses D3.js v4 with SVG rendering and animation capabilities, utilizing the `RadarChart` function to generate the chart. It employs `d3.scaleLinear` for radial scaling, `d3.scaleOrdinal` for color assignment, and includes a glow filter defined with SVG `feGaussianBlur` and `feMerge` elements to enhance visual appeal. The chart presents multiple axes arranged circularly, with data points connected to form a polygon shape.

79% match

Kkratikashetty1102@gmail.com

Radar Chart

A radar chart displays monthly temperature anomalies for a selected year, using a radial time scale for months and a linear scale for anomaly values. A color scale maps anomaly magnitude, from blue for cooler to red for warmer conditions. A slider menu lets viewers choose the year. The visualization uses D3 v7’s `scaleTime`, `scaleLinear`, `lineRadial`, and `scaleSequential` with the `interpolateRdBu` color scheme, rendering points and a connected path via SVG. Data comes from Massachusetts temperature records spanning 1895 to 2022.

78% match

Smoothed Radar Chart

This example visualizes passenger station amenities and accessibility data for French train stations as a smoothed radar chart. It compares two stations—Paris Nord and one other—across seven axes including cleanliness, security, comfort, and PMR assistance scores, using an animated SVG rendering. Built with D3 v4, the visualization employs an ordinal color scale and a custom radar chart module that maps values to radial coordinates. Data comes from a CSV dataset containing station quality metrics and passenger counts, with the chart updating dynamically when the data loads.

77% match

Simple Radar Chart

A simple radar chart displays two randomly generated 24-hour data series as radial lines around a central point, with colored polygons for each series. The visualization uses d3.js version 2.5.0 to render SVG elements, including concentric circular axes with tick labels and radial spokes for each hour. The code defines a linear radius scale from the merged data range, then draws paths using `d3.svg.line.radial()` to connect the data points, with green and blue strokes distinguishing the series.

RRemiFaure

77% match

Smoothed Radar Chart

This visualization compares passenger services at the Gare de Lyon and Paris Nord railway stations, displaying seven quantitative axes covering attributes like cleanliness, security, and accessibility. It uses D3.js v4 to render a smoothed radar chart with an SVG and CSS animation, drawing data from a CSV dataset of French train stations. RadarChart.js constructs the polygon using smooth curves, with each station labeled and color-coded.

Smoothed Radar Chart

This example visualizes a smoothed radar chart comparing passenger ratings across multiple French train stations, showing metrics like cleanliness, security, and communication. The D3.js v4 code loads station data from a CSV file using `d3.csv`, then renders the chart as an SVG with two station profiles overlaid. It uses a custom `RadarChart` function that draws circular grid lines, labels, and area polygons with optional glow filters, animated to display the comparative data.

Fork of Radar Chart

This radial chart displays monthly temperature anomalies for Massachusetts from 1895 onward, with a slider to select a specific year. Each point represents a month’s deviation from the historical average, colored along a blue-to-red diverging scale. The chart uses a D3 v7 radial line generator (`d3.lineRadial`) with a time scale for angles and a linear scale for radii, rendered as SVG. Data comes from a local CSV parsed with `d3.csv` and `d3.timeParse`.

Wwillapolman@gmail.com

CChaturyaAkhilaPallem@my.unt.edu

RADAR CHART

This radar chart visualizes the climate data of various cities, plotting multiple meteorological attributes for comparison. The chart displays axes for each variable in the dataset, which includes temperature, humidity, pressure, CO2 emissions, wind speed, and wind direction. Built with D3.js, the visualization uses SVG rendering and CSV data parsing to create a multi-dimensional overview. The code implements custom scales and line interpolation to map the dataset's values onto radial axes, with each city represented as a distinct path.