Chess board Visualization

test

This example displays a chessboard with algebraic square labels and a list of chess moves. Hovering over a move highlights the corresponding starting and destination squares in red. The visualization uses D3 v7 to generate SVG rectangles for the board and text elements for the labels, with mouseover and mouseout events dynamically adjusting fill colors and element stacking order. The data is defined programmatically as an array of square names.

Bbubbabjh

86% match

Chessboard in D3

This interactive chessboard visualizes classic opening moves in chess. A drop-down menu allows selection among several openings, including the Slav Defense, Italian Game, and French Defense. The board is rendered as an SVG grid of beige and tan squares, with chess pieces drawn using Unicode characters. Selecting an opening triggers step-by-step animations using chained `transition()` calls to move pieces across the board. The code uses D3.js v7 for SVG manipulation and transitions, with data loaded from a CSV of chess games.

79% match

Fork of Sunburst Chess

This visualization shows the first moves of chess games as an interactive sunburst diagram. Players explore opening sequences by hovering over arc segments, which highlight the path from the center and display the percentage of games following that line. The code uses D3’s `partition` layout with a hierarchy built from CSV data, rendering SVG paths with `d3.arc`. Each move’s color is mapped via `scaleOrdinal`, and the chessboard preview uses the chessboardjs library.

GGan Gao

75% match

Chess Game Dataset Barchart Viz

This interactive bar chart explores a chess game dataset, allowing users to view distributions of wins by color, game termination type (such as checkmate or resignation), and opening move sequences via a dropdown menu. The visualization uses D3 v7’s `scaleBand` and `scaleLinear` for axes, `flatRollup` to aggregate counts, and `schemeDark2` for bar colors. Data is fetched as a CSV and parsed with `csvParse`, with axes and menu components built using custom D3 modules.

72% match

Exploratory Data Analysis w/ Chess Dataset

This visualization explores a chess game dataset to determine which color piece wins more often. It presents the results as a simple bar chart, showing the count of games won by white versus black pieces. The code loads the data from a GitHub-hosted CSV file, processes it using D3's `flatRollup` function to aggregate win counts by winner, and then renders the summary as JSON text in a `<pre>` element. The visualization uses D3 v7 and demonstrates a basic exploratory data analysis workflow with a minimal, functional interface.

Chess Scatterplot With Menus And Brushing

A scatterplot of chess player statistics uses two dropdown menus to select the x- and y-axis fields, including ELO ratings, birth year, federation, and title. A d3.brush overlay allows users to select a rectangular region of circles, highlighting those points in green and dimming the rest. The selected points retain their highlight while changing menus, enabling tracking of individual players across different statistical dimensions. The visualization updates axes and circle positions with a d3.transition and uses d3.scaleLinear and d3.scalePoint for quantitative and categorical fields.

PPatrick-Houlihan

Heatmap

This heatmap visualizes the frequency of chess opening moves across a 10x10 grid of board positions, with color intensity representing move popularity. The visualization uses d3.scaleBand for axes, d3.scaleLinear for the color gradient from white to green, and d3.axisBottom/axisLeft for labels. Data loads from a CSV file containing sleep efficiency records, though the grid dimensions and labels are hardcoded. The code includes separate modules for axes and heatmap rendering, with d3.csv fetching and d3.selectAll joining rectangles to render cells.

With chessboard

This example visualizes the first K steps of chess games from a CSV dataset, showing the frequency of different move sequences. It features an interactive chessboard where users can drag and drop pieces, with legal moves highlighted in grey and invalid moves snapping back. The board is implemented using the chessboard.js library combined with the chess.js engine for move validation, while the underlying React framework handles the data processing and visualization of move frequencies.

PPeiyang Li