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TimStapert

@TimStapert·9 public vizzes

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Arc diagram

This arc diagram visualizes email communication patterns within the Enron corporation, using data from a CSV file. It displays employee email addresses along a horizontal axis, with arcs connecting senders to recipients. The visualization employs D3.js (with SVG rendering) to create the arcs, scale point positioning for node layout, and ordinal color scaling to distinguish job titles. Node size and label rotation enhance readability, while the zoom functionality allows for detailed exploration of the network.

May 28, 2021
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New Matrix V2

This matrix visualization displays email communication patterns within the Enron dataset, using color intensity to represent average sentiment between sender-recipient pairs. The adjacency matrix shows all employees on both axes, with cells colored from yellow through white to blue based on sentiment scores. Built with D3 v4 and React, the code uses `d3.nest()` to aggregate emails by sender and recipient, `d3.scaleLinear` for sentiment coloring, and `d3.zoom` for interactive panning. Employee job titles are encoded through axis label colors using a categorical scale, with the SVG rendered client-side.

May 25, 2021
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Adjacency matrix V1

This adjacency matrix visualizes email communication patterns from the Enron corpus, mapping each of the 149 unique sender and recipient IDs along the x- and y-axes. Cell color encodes average sentiment per sender-recipient pair, ranging from blue (negative) through white (neutral) to red (positive). The visualization uses D3 v4’s `scaleBand` for axes, `d3.nest` for hierarchical data aggregation, and `d3.scaleLinear` for the sentiment color scale, rendered as SVG with zoom support via `d3.zoom`.

May 19, 2021
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Untitled

This example shows an interactive adjacency matrix of U.S. state connections, where cell opacity encodes link strength and color indicates whether states share a region. Users can reorder the matrix by name, link count, or region using a dropdown, with smooth animated transitions via `d3.transition`. The visualization uses `d3.scale.ordinal` and `d3.scale.linear`, and renders SVG rectangles. A custom legend built with `d3.svg.legend` maps regional colors. The data comes from a local `miserables.json` file containing states and their links.

May 18, 2021
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New matrix

This matrix visualization displays email communication patterns within the Enron dataset, showing the number of messages sent between employees as colored cells in an adjacency matrix. The code uses d3.nest() to group emails by sender and recipient, calculating total message counts and average sentiment scores for each pair. It renders the visualization using d3.v4 and d3-scale-chromatic for color mapping. The example demonstrates a data preprocessing workflow where nested data is flattened into link objects containing sender and recipient information, though the actual matrix rendering and node extraction remain incomplete.

May 17, 2021
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Sorting data

This visualization displays the Enron email network as an adjacency matrix, where each cell represents the correspondence between a sender and recipient. The data is pre-processed by grouping emails by sender and receiver, calculating the total number of emails exchanged and the average sentiment score. The implementation uses d3.v4’s `d3.csv` and `d3.nest` APIs, along with custom JavaScript to structure the data before rendering the matrix. The example is built with the React framework, reading from a CSV file containing email metadata such as date, job titles, and sentiment values.

May 17, 2021
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Data manipulation trial

This visualization demonstrates the initial stages of transforming raw Enron email data into an interactive adjacency matrix. It shows how email records are grouped and aggregated to reveal communication patterns and sentiment scores between individuals. The code uses D3.js v3’s `d3.nest()` and `d3.csv()` to parse the dataset, then restructures grouped data into link objects containing sender, recipient, mail count, and average sentiment. Key D3 API methods include `d3.sum()` and `d3.mean()` for rollup calculations, alongside standard JavaScript array methods to prepare nodes for future rendering.

May 17, 2021
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Untitled

A circular arc diagram traces email communication among Enron employees, with nodes positioned along a horizontal axis and curved paths linking senders to recipients. Job titles determine node colors via a categorical scale, while node size reflects sender ID. The visualization filters the CSV dataset to 1,000 records, sorts by job title, and renders paths using SVG `path` elements with elliptical arcs. Built with D3 v4 and React, it supports zooming through `d3.zoom()` and relies on `d3.scalePoint` for positioning and `d3.scaleOrdinal` for color assignment from `d3.schemeSet3`.

May 11, 2021
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DBL 45BD

This visualization displays email communication patterns within the DBL Group 45BD workgroup, using a custom arc diagram to show relationships between employees. It plots each unique email address as a node along a horizontal axis, with curved paths connecting senders to recipients, colored by job title using the d3-scale-chromatic color palette. The code filters data to only show top-level roles like CEO, President, and Manager, and limits the dataset to 1000 records. Built with React and SVG, it implements zoom functionality through d3.zoom and reads CSV data using d3.csv.

May 11, 2021