Vega-Lite MFRED Hourly Kw vs kVAR

Vega-Lite MFRED Hourly Kw vs Time

This visualization plots hourly kilowatt readings from the MFRED dataset against time, with each of the 26 generator units (AG01 through AG26) colored distinctly by category. It uses the Vega-Lite API, registered with vega and vega-lite, to render point marks on an SVG canvas. The data is loaded via d3.csv, aggregated from 15-minute intervals into hourly totals using d3.group, and the chart auto-fits to the window size. Tooltips display the generator name on hover.

91% match

Vega-Lite MFRED Visualization KVAR vs KWh

This visualization plots kW against kVAR for each of 26 aggregate generators in the MFRED dataset, with points colored by generator ID to compare real versus reactive power output. It uses the Vega-Lite API with d3.v5 for data loading, transposing the CSV into per-generator records via `getData`. The chart applies custom axis and label styling from `config.js`, renders as an SVG through vega-tooltip, and displays 15-minute interval data across all generators.

90% match

Vega-Lite MFRED Hourly Kw vs Time Histogram

This visualization displays hourly electricity consumption in kilowatts over time, with individual aggregator groups colored distinctly and tooltips revealing their names. Built with the Vega-Lite API and D3, it transforms 15-minute CSV data from MFRED by grouping timestamps into hourly intervals and summing values across 26 aggregator channels. The bar chart uses `vl.markBar` with configurable axis styling and a dark color scheme, rendered as an SVG via Vega-Lite’s view engine.

87% match

MFRED Kw Histogram

This visualization displays a histogram of kW and kVAR measurements from the MFRED dataset, with individual aggregates (AG01 through AG26) distinguished by color. The bars are rendered using the vega-lite-api’s markBar with a black background and configured axes. The data is loaded via d3.csv from a GitHub-hosted CSV file, then grouped by hour using d3.group to compute totals. The chart uses D3 v5, Vega-Lite v4, and the vega-lite-api wrapper, with a custom tooltip handler for interactivity.

83% match

Fork of Vega-Lite API Template

This scatter plot visualizes the relationship between microwave and office lighting power consumption, using a semi-transparent line mark to reveal density patterns in the data. The visualization is built with the Vega-Lite API, which translates specifications into Vega and renders as SVG. Data is loaded asynchronously from a CSV file using `d3.csv`, with the plot dimensions automatically fitting the browser window. Visual styling for axes and labels is customized through a separate configuration object, and interactive tooltips are enabled via the vega-tooltip handler.

KKeyvan Rezaei

Fork of Vega-Lite API Template

This visualization displays daily gas prices across multiple stations, plotted over time as a scatterplot of firebrick-colored points. Tooltips reveal station names on hover. The chart is built using the Vega-Lite API (vl) with Vega and Vega-Lite libraries, rendering to SVG via d3.v5, and pulls data asynchronously from a CSV hosted on GitHub Gist, sourced from the UCI Auto MPG dataset.

Ttmcgirr

Vega-Lite API Template

This visualization displays a scatter plot of vehicle fuel efficiency data from the UCI Auto MPG dataset, with horsepower on the y-axis, miles per gallon on the x-axis, and points colored by origin while sized by weight. It is built using the Vega-Lite API, which programmatically constructs Vega-Lite specifications, and renders through the Vega and Vega-Lite libraries. The code loads CSV data with d3.csv, applies custom axis and label styling from a config object, and renders an interactive SVG view with tooltips via vega-tooltip.

GGenpinZ

Vega-Lite API Fork

This example presents a scatter plot of IBM intraday trading volume, with each circle sized and colored to emphasize fluctuations over time. The visualization is built using the Vega-Lite API, which registers with Vega, Vega-Lite, and D3 v5 to render SVG output and tooltips. Data is loaded asynchronously from a CSV file via d3.csv, sourced from the UCI Machine Learning Repository’s Auto MPG dataset, though the code actually fetches IBM intraday data from a Gist. The chart auto-fits to the window size and applies custom axis and font styling from a configuration object.

GGarrett Siegel