Cars Report

Cars Report

This example displays the make of the first car in a hard-coded JSON dataset as large text on a webpage. It parses an embedded array of car objects into JavaScript using `JSON.parse` and selects the first element’s `make` property. The code then updates the DOM by setting the `textContent` of a `<pre>` element accessed via `document.getElementById`. No external visualization libraries are used, relying solely on native JavaScript and HTML.

JJSR2000

Cars Report

This example displays the make of the first car in a hardcoded JSON array, rendered as large text in a pre element. The page loads a dataset of five cars with properties like year, make, model, and price, then uses the native `JSON.parse` to convert the embedded string into a JavaScript object. The code accesses `cars[0].make` and assigns it to the DOM element’s `textContent` property. The visualization relies on standard browser APIs and a simple script tag, with no external libraries.

HHamza Elhamdadi

Cars report

This example displays the make of the first car in a small dataset, rendered as large text on a dark, centered page. The code parses an embedded JSON array of car objects using `JSON.parse` and retrieves the first entry’s `make` property. It then updates the text content of an HTML element via `document.getElementById`, applying styling from an external Ubuntu font and a blue background through CSS. The visualization is minimal, highlighting a single data point without charts or interactive elements.

MMichał Szumnarski

Cars Report

This example displays a single message—the make of the first car in a small dataset—on a webpage using a large `<pre>` element. The data is defined as an inline JSON array in `cars.js` and parsed with `JSON.parse`, then imported into `index.js` where the first object’s `make` property is accessed directly. The text is rendered by setting `textContent` on the element, using no external libraries or visualizations.

HHombreLobotomia

Cars Report 2

This example displays a single car make in large green text on a webpage. It parses a hardcoded JSON array of car objects, including make, year, and price, and renders the first car’s make using the DOM API’s `textContent` method. The visualization relies on standard JavaScript modules for data import and CSS styling, with no charting or animation libraries involved.

Tthebeef5

Cars Report

This example displays the make of the first car in a hardcoded JSON dataset as a large text message on a web page. The data, defined in a local `cars.js` ES6 module, contains three car records with year, make, and price fields. The visualization imports this data using the `import` statement and uses standard JavaScript DOM manipulation to set the text content of the `#message-element` HTML element. The page loads D3 version 5 from a CDN, though the visible code does not call any D3 functions.

MMartin Casey

Cars Report

This example displays the make of the first car in a small, hard-coded dataset. It loads a JSON array of five cars, each with a year, make, model, and price, from the `cars` module using `JSON.parse`. The `index.js` file accesses `cars[0].make` and sets the text content of a `<pre>` element with `id="message-element"` in `index.html`, rendering the string “Honda” in large font. The visualization is static and relies on plain JavaScript DOM manipulation, not any charting or animation libraries.

Iirasheed01

Cars Report

The Cars Report displays the make of the first car in a small inline dataset, rendered as large text on a dark blue page. The example uses a plain JavaScript array of car objects defined in `cars.js` and parsed with `JSON.pars` (a typo for `JSON.parse`). Index.js accesses `cars[0].make` and updates the DOM via `document.getElementById("message-element").textContent`. Styling relies on the Ubuntu font from Google Fonts and a CSS background color, with no external visualization libraries.

Sselina-rudolph