Skip to main content
SimilarExport ZIP
100%

Spotify Dataset Basic Scatter Plot with Axis Using React & D3

✓ Published0🌍 Public
JJose Morales
Last edited Sep 19, 2025
Created on Sep 19, 2025

This scatter plot visualizes 500 tracks from a Spotify Music Dataset, plotting song duration in milliseconds against a popularity score from 0–100. The chart uses a linear scale for both axes, with the x-axis labels formatted as minutes:seconds. Built with React and D3 v7, the visualization uses D3’s `scaleLinear`, `axisBottom`, `axisLeft`, and `extent` functions to create and scale the plot. Data is loaded from a CSV file and rendered as black circles with a radius of 6.

AI-generated description

Spotify Music Dataset

This dataset is a subset of the “Spotify Music Dataset” dataset available from Kaggle.

To create this subset, I simply took the first the 500 entries from the data full dataset, and removed some of the columns included. The following are included in this subset:

  • track_artist – The artist(s) performing the track (categorical attribute)
  • track_popularity – A score from 0–100 based on the track’s relative streaming frequency (quantitative sequential attribute)
  • track_album_name – The album in which the track is featured (categorical attribute)
  • playlist_name – The name of the playlist where the track is included (categorical attribute)
  • track_name – The name of the track (categorical attribute)
  • duration_ms – The length of the track in milliseconds (quantitative sequential attribute)
  • id – A unique identifier for the song (categorical attribute)
MIT Licensed

Similar vizzes

Loading thumbnail…

Spotify Dataset Basic Scatter Plot with React & D3

This example shows a basic scatter plot of Spotify track data, mapping song duration in milliseconds along the x-axis against track popularity on the y-axis. Each circle represents one of 500 songs from a Kaggle dataset subset. Using React hooks to load the CSV file, the rendering delegates to a D3 function that applies `scaleLinear` and `extent` to compute the scales. The D3 `select` and `join` methods create the circle elements.

JJose Morales
93% match
Loading thumbnail…

Fork of Basic Scatter Plot with React & D3

A scatter plot visualizes the musical characteristics of Spotify's top songs from 2010 to 2019, positioning each track by its danceability and energy scores while sizing circles by popularity. A categorical color scale distinguishes genres, with a legend identifying the ten genre categories. The React app uses D3's `csv` loader to parse song attributes from a local CSV file, then leverages D3's `scaleLinear`, `axisBottom`, and `axisLeft` to construct the chart and its axes.

VVinayak Revankar
84% match
Loading thumbnail…

Spotify_D3ScatterPlot

This animated scatter plot visualizes Spotify listening habits, showing the relationship between total songs played and several rotating attributes including average listening time, new artists discovered, skipped songs, and mood score. Built with D3.js 6.7.0, it uses `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` to map data from a CSV file loaded via `csv()`. The visualization cycles through four x-axis variables every two seconds using `setInterval`, redrawing circles with updated `cx` positions as the x-axis changes.

Cchandanamahesh0215@gmail.com
83% match
Loading thumbnail…

Data

This example presents an interactive scatter plot of Spotify song attributes, allowing users to compare musical features such as Tempo, Energy, and Danceability across tracks from 2010 to 2020. A year slider filters the dataset, while dropdown menus let users select which attributes to plot on the x- and y-axes and which categorical field, either Top Genre or Artist, to use for color encoding. The visualization is built with React, using D3 v6 and v5 APIs including `scaleLinear` and `scaleOrdinal` for axes and color, alongside ReactDropdown for menu selection, and it renders SVG circles with native tooltips. Data is loaded via `d3.csv` from a GitHub gist containing the yearly music trend dataset.

Nnikki81140
82% match
Loading thumbnail…

SPOTIFY - Scatter Plot

This scatter plot visualizes Spotify listening habits by plotting numeric columns from a CSV dataset, with axes automatically selected based on the most complete fields. The visualization loads data via `d3.csv` and uses reusable `d3.scaleLinear` scales, `d3.axisLeft` and `d3.axisBottom` for axes, and SVG circles for marks. It responsively redraws on window resize, applying custom margins and labels. The chart highlights relationships between behavioral metrics like newly discovered artists and mood scores, rendered with the D3 v6 library.

Cchandanamahesh0215@gmail.com
82% match
Loading thumbnail…

Spotify_Interactive Polished Scatter Plot with Menus and Colors and Color Legend

This interactive scatter plot visualizes a Spotify song dataset, mapping selected audio features like danceability, energy, and popularity against each other on the X and Y axes. A color legend and dropdown menus allow users to switch the categories and attributes being compared, while hovering over legend items fades unrelated data points. The visualization is built with React and D3 v7, using SVG for rendering, and includes filtering capabilities to narrow the data by a popularity range.

Sstephanie0215
81% match
Loading thumbnail…

Spotify

This interactive scatterplot visualizes a dataset of Spotify tracks, plotting pairs of audio features such as acousticness, energy, and valence on x- and y-axes. Users select features from two dropdown menus, and the visualization updates the axes and data points accordingly. Rendered as an SVG using D3.js version 5, the chart uses `d3.scaleLinear` for both axes and draws semi-transparent circles to indicate track distributions. The code reads from a local CSV file and updates the chart dynamically via a polling function that checks selector changes.

Gginnyching
81% match
Loading thumbnail…

music vis

This interactive scatterplot maps Spotify track attributes against one another, letting users select any two numeric columns from the dataset to compare on the x- and y-axes. The visualization updates dynamically through D3 v7's `scaleLinear`, `axisBottom`, and `axisLeft` APIs, rendering circles positioned by the chosen metrics. The code assigns CSS classes to each point based on a track genre field, although the dataset’s example rows lack a genre column, so the legend and color-coding rely on that optional data. The SVG plot includes labeled axes and a title reflecting the current variable pair.

Aa15923647
81% match