Prior selections by the Zendesk Film Salon

100%

Prior selections by the Zendesk Film Salon

✓ Published0🌍 Public

Last edited Mar 30, 2019

Created on Mar 9, 2019

This visualization maps the chronological screening history of the Zendesk Film Salon, plotting each film selection by date and highlighting the person who chose it. The data, sourced from a plain-text file, is rendered as a timeline of dots, with each point positioned along a horizontal axis for time and grouped by the selector’s name. The visualization uses d3.forceSimulation to arrange the film titles as labels near their corresponding dots, preventing overlap, while a color scale distinguishes the different contributors. The layout reveals the rotating leadership and periodic resets between rounds of picks.

AI-generated description

Similar vizzes

Loading thumbnail…

Data Mining

This interactive visualization maps the interconnectedness of films, showing how a single movie like *2001: A Space Odyssey* links to a vast network of over one hundred related titles. The primary display uses a force-directed graph layout via D3.js v4’s `forceSimulation` to position the nodes of the recommendation network. A separate chart reveals the frequency of genre pairs, drawing on the `genres.csv` dataset. Data is rendered as SVG, with the dataset loaded from external CSV files in a custom script.

MMelanieSalignat

64% match

Loading thumbnail…

Data Mining

A network visualization maps movie recommendations from a CSV dataset, showing connections between films based on association-rule mining. Users can click or hover over nodes to highlight related movies, with a tooltip displaying the film title and a zoom feature for closer inspection. The code uses d3.forceSimulation for layout, d3.csv for data loading, and SVG circles and lines for rendering, with d3.zoom enabling pan and zoom interactions.

MMelanieSalignat

62% match

Loading thumbnail…

Document

This visualization presents a multi-panel exploration of the TMDB 5000 movie dataset using D3 v5, rendered as SVG with animated transitions. It combines three coordinated chart types: a scatter plot comparing budget and popularity, a bar chart for average vote versus vote count, and a time series showing release date versus runtime. The time plot includes an overlaid line chart showing the count of movies per year, along with animated circles that draw the data points sequentially. The visualizations are linked so that interacting with one plot updates the others, enabling cross-filtering of the dataset. The layout divides the available width and height into quadrants, with the time plot spanning the bottom half and the scatter and bar charts sharing the top half. The design uses a minimal black-and-white aesthetic with white axes, labels, and strokes on a transparent background.

AAlexDaGr8

61% match

Loading thumbnail…

Data Mining

This visualization shows a network of movie recommendations, where each node represents a film and links connect movies that are frequently recommended together based on association rules. Users can drag nodes to explore relationships, and hovering over a movie highlights its directly connected neighbors. The code uses D3 v4’s `d3.forceSimulation` with `d3.forceLink` and `d3.forceManyBody` to position the graph, and renders nodes as SVG circles with drag interactions. Data comes from a CSV file containing antecedent-consequence pairs from a recommendation dataset.

MMelanieSalignat

60% match

Loading thumbnail…

Actor Graph

This interactive visualization displays a force-directed graph of actors and the movies they appear in, where each actor is represented as a node and connections between actors who share a film are shown as links. The graph is built using D3.js v7, with the `d3.forceSimulation` API handling node positioning and link physics. Data comes from a CSV file listing movies and their cast, parsed using `d3.csvParse`, and rendered as SVG elements (circles, text labels, and lines). The visualization explores collaborative networks among actors across the included film dataset.

KKyle?

60% match

Loading thumbnail…

Film nerd data with Vega-Lite API

This visualization displays one person’s film-watching records from a CSV file, plotting watch dates on the x-axis and countries on the y-axis. Each circle represents a film, with color encoding the user’s grade and tooltips revealing the title. The example uses the Vega-Lite API’s `markCircle` and `encode` methods, with D3’s `csv` loader to fetch data. Styling is customized via a config object, while the chart is rendered as SVG inside a full-window layout.

KKirsten Lund

59% match

Loading thumbnail…

Movie Stats Scatter Plot Final Iteration

This interactive visualization lets users explore IMDB movie data through a customizable scatter plot. Users select which genres or actors to display and independently choose the X and Y axes from yearly release, runtime, rating, votes, and revenue. The plot encodes genre with color and includes hover effects that reveal movie titles. Built with D3 v7, the visualization uses the `scaleLinear` and `extent` functions for scales, `axisLeft` and `axisBottom` for labeled axes, and SVG circles for data points. The project also features a separate actor network graph, though it remains incomplete. Movie data is loaded from a local CSV file.

KKyle?

59% match

Loading thumbnail…

record-movies

A scroll-driven visualization tracks the top-grossing movies of all time, showing how their box office earnings grow over their first 26 weeks of release. As the user scrolls through a timeline from 1993 to 2019, the chart updates to highlight the current top three films with pink lines, using D3’s nested data and scale functions to bind the scroll position to the year domain. The data comes from Box Office Mojo via Axios, rendered as SVG paths with d3.line and a Canvas-free DOM-based tooltip.

11wheel

59% match