Skip to main content
SimilarExport ZIP
100%

Videogame sales

✓ Published0🌍 Public
VVamsi Bhargav Sukamanchi
Last edited Sep 24, 2020
Created on Sep 10, 2020

This example presents a minimal summary of the Videogame Sales dataset, displaying its size in kilobytes, row count, and column count as large text. It uses D3 v5 to load the remote CSV file via `d3.csv`, converts the data with `d3.csvFormat`, and reports the byte length before updating the page content. The visualization focuses on data loading and parsing rather than graphical representation, showcasing D3’s CSV utilities.

AI-generated description

The Videogames Sales dataset contains a list of video games with sales greater than 100,000 copies.

MIT Licensed

Similar vizzes

Loading thumbnail…

Video Game sales

This example displays basic metadata about a video game sales dataset, showing its size in kilobytes, row count, and column count in large text. It loads a CSV file from a GitHub gist using the D3.js v5 `d3.csv` method, then formats the content with `d3.csvFormat` to calculate the data’s byte size. The code also includes commented alternatives using the Fetch API and async/await with `d3.csvParse`. The dataset originates from Kaggle’s video game sales collection, with the visualization serving primarily to demonstrate CSV loading and summary statistics.

RRochelle
89% match
Loading thumbnail…

Video Game Sales

This example reads a CSV file of video game sales data from a public GitHub gist, originally sourced from Kaggle, and displays a plain-text summary of its size and structure. It shows the file’s approximate size in kilobytes, the row count, and the number of columns. The code uses D3.js v5’s `d3.csv` to parse the remote data and `d3.csvFormat` to measure its length, then writes the summary into a large `<pre>` element. Alternate commented approaches demonstrate `fetch` with `async/await` and `d3.csvParse`.

Ggeorgeeeee
85% match
Loading thumbnail…

Video Game Data Summary

This example displays a summary of a video game dataset, showing the total row count, column count, and data size in kilobytes, followed by a JSON-formatted preview of the first ten rows. The visualization reads a CSV file from a remote URL using d3.csv and presents the information as plain text elements on the page, with the summary rendered in a large preformatted block. The code relies on D3 v5 functions like select, csv, csvFormat, and scaleLinear, though no chart is drawn; it purely prints textual data summaries.

EEmma Schaale
83% match
Loading thumbnail…

Loading and Parsing VGSales.csv

This example loads and parses the VGSales.csv dataset, displaying the raw file size, row count, and column count in large text. It shows how D3 fetches and parses CSV data to summarize its structure. The code uses `d3.csv` to load the remote file, then formats the data with `d3.csvFormat` to calculate its size. An alternative approach using `fetch`, `async/await`, and `d3.csvParse` is also included. The dataset, sourced from Kaggle, contains video game sales metrics across global regions.

TT-Reiser
81% match
Loading thumbnail…

Loading and Parsing vgsales.csv

This example loads a CSV file of video game sales data and displays basic file statistics: the dataset’s size in kilobytes, row count, and column count. The page uses D3 v5’s `d3.csv` method to fetch and parse the data from a GitHub Gist, then renders the summary in a large `<pre>` element. The code also includes commented alternatives using `fetch` with `async`/`await` and `d3.csvParse` to demonstrate different parsing approaches.

DDaniel Ribaudo
81% match
Loading thumbnail…

Loading data - vgsales.csv

This example loads the Video Games Sales dataset from a GitHub-hosted CSV and displays its size, row count, and column count in large text. It demonstrates two approaches to fetching and parsing data: the primary method uses `d3.csv` to load and parse the file, while an alternative commented implementation uses `fetch` with `async`/`await` and `d3.csvParse`. The page also includes a commented “pyramid of doom” example showing nested promise callbacks. All results are rendered into a `<pre>` element via `document.getElementById`.

VVarun Eranki
79% match
Loading thumbnail…

video game sales

This example displays the raw contents of the video game sales dataset as a JSON-formatted text dump, showing sales figures across regions and user scores for each title. The visualization initially loads data from a remote CSV file hosted on GitHub using `fetch()` and parses it with D3 v7’s `csvParse`. It uses a simple `pre` element bound to the parsed data array through a data join, with the `select`, `join`, and `text` APIs to render the dataset exactly as structured. The state management via `setState` handles the asynchronous loading sequence from `undefined` to `'LOADING'` to the final parsed array.

QQingyang Wang Profolio
79% match
Loading thumbnail…

Video Game Sales

This example visualizes video game sales data from Kaggle, showing all games with over 100,000 sales. It uses d3.csvParse to load and parse the dataset, converting sales figures and release years to numbers for analysis. The visualization renders the current state as a JSON string in a pre element using d3’s data join, allowing users to inspect the raw data structure. The code demonstrates a state management pattern with setState, triggering re-renders through d3.select and text updates.

Hhmkyriacou
78% match