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Hexbin Text for Athlete Height and Weight
Interactive adaptation of Observable Plot’s hexbin-text example, plotting seeded athlete body metrics on weight (x) and height (y) axes. The chart renders text labels using Plot.text with Plot.hexbin({text: “count”}) so each hexagonal bin displays how many athletes fall into that region. Users can filter the dataset by sex, adjust count-label text size, control background point opacity, and toggle visibility of the underlying raw points. Hover interaction includes Plot.crosshair and Plot.tip with Plot.pointer to inspect individual athlete details (name, sex, sport, weight, and height).
US State Labels Explorer
Interactive adaptation of Observable Plot’s plot-state-labels example, showing U.S. state labels positioned by deterministic seeded longitude/latitude centroids in an Albers USA projection. The chart overlays optional state boundaries and renders text labels for each state, with users able to choose abbreviation or full-name labeling for readability tradeoffs. Users can filter the view by region, adjust text size with a slider, and toggle state border visibility to focus on labels or geography. Hover exploration includes Plot.crosshair and Plot.tip with Plot.pointer to inspect each state’s name, abbreviation, region, and centroid coordinates.
Caltrain Stem-and-Leaf Schedule
Interactive adaptation of Observable Plot’s caltrain-schedule example, plotting deterministic Caltrain-like departures as text leaves around an hourly stem. The chart uses Plot.text with Plot.stackX2 so minute labels stack outward from the center line, splitting southbound and northbound service while coloring by service type (Local, Limited, Bullet). Users can filter by direction, filter by service type, set the latest departure hour with a slider, and switch leaf notation between two-digit minute labels and single-digit leaves. Hover exploration includes Plot.crosshair and Plot.tip with Plot.pointer to inspect each train’s number, direction, service type, and departure time.
IPO Text Dodge Timeline
Interactive adaptation of Observable Plot’s text-dodge example, plotting seeded IPO events by date and market capitalization. The chart uses Plot.text with Plot.dodgeY to stack overlapping labels vertically so same-day entries remain readable without collision. Users can filter by sector, set a minimum market-cap threshold with a slider, and switch label mode between ticker symbols and full company names. Hover exploration includes Plot.crosshairX and Plot.tip with Plot.pointer to inspect each company’s name, ticker, sector, date, and market cap.
Text Spiral Explorer
Interactive adaptation of Observable Plot’s plot-text-spiral example, plotting seeded spiral coordinates generated from deterministic index-based math. The chart renders labels with Plot.text on (x, y) positions computed from x=\sqrt{i}\sin(i/s) and y=\sqrt{i}\cos(i/s), where users adjust the turn scale parameter s. Users can control point count, spiral turn scale, label mode (letters, indices, or combined), text size, and optional anchor-point visibility. Hover exploration includes Plot.crosshair and Plot.tip with Plot.pointer to inspect point index, label, theta, radius, and precise coordinates.
This Is Just To Say — Interactive Text Layout
Interactive adaptation of Observable Plot’s this-is-just-to-say example, rendering William Carlos Williams’s poem lines as Plot.text marks inside a framed chart. The app stores deterministic seeded poem lines in a bf table and maps each line to a vertical position so users can inspect line number, stanza, and content through hover interactions. Users can switch focus mode between full poem, opening, plum, and apology stanzas, adjust text size with a slider, and change frame anchoring (top, middle, bottom) to explore layout behavior. Hover exploration includes Plot.crosshair and Plot.tip with Plot.pointer so each line’s metadata is discoverable directly on the chart.
Voronoi Labels for Airport Points
Interactive adaptation of Observable Plot’s voronoi-labels example, plotting seeded U.S. airport coordinates (longitude and latitude) with airport code labels. The chart supports Voronoi-based label placement by computing Delaunay/Voronoi cells and positioning labels at cell centroids, with optional leader lines from each point to its label. Users can change sampling density (every airport, every 2nd, every 3rd), adjust point radius, toggle Voronoi centroid labels, and toggle Voronoi mesh visibility. Hover exploration includes Plot.crosshair and Plot.tip with Plot.pointer to inspect each airport’s code, city, region, coordinates, and passenger volume.