A6 Sequential Color Scale

A6 Diverging Bar Chart

This diverging bar chart visualizes police shootings by race and gender, with male counts extending left from a central vertical axis and female counts extending right. The chart uses a red-blue diverging color scale, with color intensity corresponding to magnitude. Built with D3 v7, the visualization aggregates CSV data, filters to only male and female records, and uses separate linear scales for each gender to position the bars from the center. Race categories are placed on a band scale along the y-axis, while the diverging color interpolation and axes are constructed using D3’s scale utilities.

88% match

A4 Bar Chart in D3

This visualization displays the number of police shootings recorded by racial category, using a bar chart where bar heights correspond to aggregated counts from a CSV dataset. The chart is rendered in SVG via D3.js v7, using `d3.csv` for data loading, `d3.rollup` for aggregation, and `d3.scaleBand` and `d3.scaleLinear` for positioning. CSS hover effects change bars from steelblue to orange, while axis labels are added via `d3.axisBottom` and `d3.axisLeft`.

84% match

A7 Multi-Line Chart D3

This example visualizes police shooting incidents over time by race, plotting yearly counts as multi-line charts for White, African American, Hispanic, and Other groups. It uses D3 v7 with time and linear scales, a line generator, and d3-legend to map data from a CSV file into an SVG. Missing years are filled with zero values, and axes display formatted year labels.

81% match

A7 Small Multiples Chart D3

This example presents a 2x2 grid of small multiple line charts, visualizing police shooting counts per year across four racial categories. It uses a D3 v7 ordinal color scale and time scale, with data loaded from a CSV file. Labels and legends are added with d3-legend. The code uses `d3.csv`, `d3.group`, `d3.rollup`, `d3.scaleTime`, and `d3.line` to construct shared axes, create the line generator, and arrange chart groups via manual translation transforms.

80% match

A6 Scatter Plot

This scatter plot visualizes the U.S. police shootings dataset, showing the count of shootings aggregated by victim age and race. The code uses D3 v5’s `d3.nest()` and `rollup` to group data, then plots aggregated circles with `d3.scaleLinear` for axes and `d3.scaleOrdinal` with the category10 scheme for race colors. Axes are labeled with `d3.axisBottom` and `d3.axisLeft`, and a color legend is rendered via the d3-legend library. A linked YouTube tutorial explains the implementation.

A5 Bubble Chart D3

This bubble chart visualizes police shootings by age and race, with each circle representing the count of incidents for a specific age and racial category. The x-axis maps age, the y-axis maps the count of shootings, and circle size encodes frequency using a square-root scale so area remains proportional. The chart is built with D3 v7, loading CSV data, aggregating with `d3.rollup`, and rendering SVG circles using scales like `scaleLinear`, `scaleSqrt`, and `scaleOrdinal` with `schemeCategory10` colors. Axes and labels are added via `axisBottom` and `axisLeft`.

A7 Time Series Heat Map D3

This heat map visualizes police shooting incidents over time, using a grid of colored cells where each cell represents a specific year and month, with color intensity (from white to red) indicating the number of incidents. The visualization employs D3.js v7 with SVG rendering and animation, featuring a continuous color legend and interactive tooltip. Data is loaded from a CSV file, parsed using `d3.timeParse`, and aggregated into a yearly-monthly count matrix. The implementation utilizes D3's `scaleBand` for axes, `scaleSequential` with `interpolateReds` for the color scale, and the d3-legend library for the color legend.

A2 Bar Chart

This A2 Bar Chart visualizes counts of police shooting victims by race, using a logarithmic scale on the y-axis to accommodate wide disparities in the data. The chart loads a CSV from a GitHub gist using p5.js’s `loadTable()` function, aggregates race counts in `preload()`, and renders the bars with `rect()` on a 3000x3000 `createCanvas()`. Axis labels, tick marks, and bar values are drawn with custom mapping logic inside the `draw()` loop.