Broadway Shows Average Theater Capacity

Average Broadway Theater Attendance by Month

This visualization uses a color-coded heatmap to show average monthly attendance for seven of Broadway’s most-attended theaters from 1990 to 2016. The data is loaded from a CSV file and processed in p5.js. Each theater has a row of twelve squares, colored from blue (lower attendance) to green (higher attendance), with a numeric legend and month labels. The sketch calculates per-theater monthly averages from the dataset, then scales colors between the global minimum and maximum attendance values.

Ssyu36

79% match

Broadway Shows Average Gross by Type

This visualization displays the average gross revenue for different types of Broadway shows from 1990 to 2016, using horizontal bars. The code calculates averages from a CSV dataset and renders them with the p5.js library, drawing axes and labels with custom functions. Green rectangles represent each show type’s average gross, scaled to thousands of dollars, with the x-axis showing tick marks and the y-axis listing category names. The data is loaded via `loadTable` from a remote CSV URL.

Ssyu36

75% match

Broadway Interactive

This interactive visualization compares the cumulative box-office revenue of the Broadway musical Hamilton with the average of other Broadway shows from July 2015 to April 2016. Two line charts, rendered as SVG with animation, allow users to toggle between datasets and adjust a time-range slider using the custom d3.slider.js library. The visualization relies on D3.js for scaling, axes, and path generation, drawing data from the included CSV files to highlight Hamilton’s rapid revenue ascent against the industry baseline.

63% match

fresh block

This visualization, built with Vue and D3 v4, tracks Broadway show attendance and financial metrics across multiple seasons as a small multiples line chart rendered on canvas. Each line represents a show’s weekly gross revenue over time, with the x-axis showing weeks and the y-axis showing gross in dollars. The data is sourced from a gist by BenHeubl and includes a CSV with fields like average ticket price, attendance, and capacity percentage. The chart is part of a blockbuilder.org project and uses canvas rendering for performance with large datasets. Interaction: Users can hover over individual lines to highlight a show and reveal its name, while the rest dim. The visualization also filters by show type (play/musical) and revival status through a dropdown menu.

final_bw_projection_chart

This interactive chart compares the cumulative gross revenue of the Broadway musical *Hamilton* against the average of other shows over time. Users can adjust multiple show attributes through dropdown menus—including actor Ulmer scores, theater seats, cast size, and review ratings—to see how a hypothetical show’s projected weekly gross compares to Hamilton’s trajectory. The visualization uses D3.js to render an animated SVG line chart with a draggable slider for date selection, pulling data from CSV files. The interface is styled with Economist-branded fonts and a red accent color.

test_data_function_broadway

A series of 51 dots arranged along a diagonal animates when a button is clicked, shifting their positions to a new diagonal layout. The visualization also computes a theater ticket sellout probability using a long logistic regression formula, logging the raw input data to the console. Built with D3.js, the SVG output uses `d3.select`, `.transition()`, and `.duration()` for the animated movement, though the animation is the only interactive element; the prediction function is not visually mapped. The code comes from a Blockbuilder.org gist and imports D3 version 3.5.5.

Dot Plot with p5.js

This dot plot visualizes ticket sales for the “Movie of the Year” from 1995 to 2021, with each green circle positioned along a horizontal axis scaled in millions and labeled by year. The example uses the p5.js library within a React framework, loading CSV data via the `loadTable()` API and rendering circles with `circle()`. Grid lines, axis labels, and movie titles are drawn using canvas drawing methods, while the data source is fetched from a GitHub Gist URL.

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