Sample CSV for XQuery Book

100%

Sample CSV for XQuery Book

✓ Published0🌍 Public

Last edited Sep 8, 2016

Created on Sep 8, 2016

This example visualizes the five-book dataset from CliffordAnderson’s gist, mapping each title to a node whose size encodes the year published and whose color distinguishes binding type. A force-directed layout using `d3.forceSimulation` positions the nodes, while `d3.select` and `transition.attrTween` animate the drag-and-drop interactions. The tooltip, rendered via SVG `<title>`, shows the full title and ISBN on hover. The chart reads the CSV directly from the embedded `books.csv` source, parsing fields with `d3.csvParse` and applying a categorical scale for the binding colors.

AI-generated description

Similar vizzes

Loading thumbnail…

Some books

This example visualizes a collection of 20 books from a CSV file, grouped by binding type into hardcover and paperback categories. It shows the distribution of books across these two formats, with each group’s count displayed as an attribute. The code uses XQuery 3.1 with `fetch:text` and `csv:parse` to load and parse the data from a Gist URL, then groups records by the `Binding` field and orders them alphabetically. The rendering approach is plain text output, with no graphical libraries, relying solely on XQuery’s element construction to list the grouped records.

CCliffordAnderson

79% match

Loading thumbnail…

Simple Books Document

This example visualizes a small set of books from an XML data source, grouping them by their `class` attribute (fiction or nonfiction). It first shows the hierarchical document structure as a simple indented list, then renders each book as a colored node in a force-directed graph. The visualization uses D3.js’s `d3.hierarchy` and `d3.tree` to lay out the XML tree, while `d3.forceSimulation` positions the book nodes, with the `class` value driving node color and tooltip text. The raw XML is loaded via `d3.xml`, and the document’s structure is directly mirrored in the layout, making the data’s grouping and classification immediately visible.

CCliffordAnderson

76% match

Loading thumbnail…

Book Publishing Dataset Interactive Scatter Plot (CS 573 Assignment #6.1)

This interactive scatter plot visualizes a book publishing dataset, with each point colored by publisher and revealing the title, author, publisher, and axis values on hover. Users can select different quantitative or categorical attributes for both axes via dropdown menus, triggering animated transitions of the circles and axes. Built with D3 v6, it uses `scaleLinear`, `scalePoint`, `axisLeft`, `axisBottom`, and `transition` to smoothly update positions, while a custom tooltip displays details. Data is loaded as CSV from a GitHub gist.

AAlex Pietrick

74% match

Loading thumbnail…

New Books

This example visualizes the distribution of new books across fiction and nonfiction genres. It reads the `books.xml` file and renders each book as a point on a simple scatter plot, with the class attribute determining the point’s color. The visualization uses the D3.js library, specifically `d3.forceSimulation` for layout and `d3.select` with `svg.circle` to draw the data marks, providing a clear, immediate comparison of genre counts from this small gist dataset.

CCliffordAnderson

72% match

Loading thumbnail…

DataClips + D3.js Force-Directed

This example visualizes monthly book purchase amounts as a force-directed node graph, where each node’s size corresponds to spending and color encodes tiers (green, yellow, orange, red) based on dollar ranges. It consumes a JSON dataset from a Heroku DataClip, preprocessed by parsing dates and amounts. Using D3.js v3’s `d3.layout.force`, nodes are positioned with gravity and charge scaled by amount, while SVG circles and text labels render the data. The color scale uses `d3.scale.category10()` for categorical differentiation, and dragging is enabled via `force.drag`.

663anp3ca

71% match

Loading thumbnail…

Gist 579bcba6019da7a932da70826f5d0c83

This interactive example visualizes the spatial layout of books from Project Gutenberg, mapping each title to a point based on its geographic coordinates and rendering them as a dense, globe-like scatter plot. Using D3.js’ `d3.forceSimulation` with gravitational and collision forces, the points are arranged into a spherical cluster, while user interaction—dragging or hovering—updates the positions in real time. The visualization employs `d3.forceX`, `d3.forceY`, and `d3.forceZ` to distribute points in three-dimensional space, and it relies on `d3.select` and event handlers to manage the dynamic behavior. The code uses a canvas-based rendering approach with `getImageData` for pixel-level color manipulation, giving the plot a glowing, heatmap-like appearance.

AAnaMal08

70% match

Loading thumbnail…

Book Dataset with slider

This scatterplot visualization explores a Goodreads book dataset, plotting each book as a point according to selectable axes, such as average rating, page count, and review counts. The interface includes dropdown menus to change the X and Y axis attributes, as well as a separate dropdown to control the opacity of all the circles, allowing users to see density variations in the data. Built with React and d3.v6, the visualization uses `d3.scaleLinear` and `d3.extent` for axis scaling, `d3.axisBottom` and `d3.axisLeft` for rendering axes, and `transition().style('opacity', ...)` for smooth updates. Data is loaded from a CSV file using `d3.csv`.

FFremont

70% match

Loading thumbnail…

Gist 466252

This example is a data-art composition built from a personal list of recommended web design and programming books, rendered as an interactive force-directed graph. It visualizes the relationships between books, their authors, and their associated topics (like usability, web standards, JavaScript, Ruby, and Rails) using `d3.forceSimulation` for physics-based layout and dynamic node positioning. Each book title, URL, and annotation from the original text is embedded as textual nodes and links, with the graph’s structure and labels derived directly from the source gist’s content. The rendering approach uses SVG elements updated via D3’s data join, and while no explicit additional libraries are mentioned, the code relies on standard D3 v4+ APIs for simulation, drag behavior, and tick updates to show the clustered, interlinked reading list.

DDylanFM

70% match