In this project, we would like to explore how music has changed over time. From timeless classics to era-defining hits and contemporary tracks, by analyzing listening trends and genres, we can reveal how the world’s mood and environment may have shaped the sounds of its time.
The purpose of this project is to explore how each generation and the world’s climate may have shaped the sounds of its time.
Our data set (“Spotify Global Music Dataset (2009–2025), authored by Warda Bilal) was found on Kaggle (Spotify Dataset), which contains 8,583 records and 15 variables offering comprehensive information about songs and artists.
library(tidyverse)
library(knitr)
# Variable table
data.frame(Variable_Names = names(spotify)) %>%
knitr::kable(
caption = "Variable Names in Spotify Dataset"
)| Variable_Names |
|---|
| track_id |
| track_name |
| track_number |
| track_popularity |
| explicit |
| artist_name |
| artist_popularity |
| artist_followers |
| artist_genres |
| album_id |
| album_name |
| album_release_date |
| album_total_tracks |
| album_type |
| track_duration_min |
Out of the 15 variables, we chose to focus on these six variables:
album_release_date
track_name
artist_name
track_popularity
artist_genre
artist_name
You can interact with the data using the search box, such as limiting results to Bruno Mars or a chosen year.
#install.packages("DT")
library(DT)Warning: package 'DT' was built under R version 4.5.3datatable(head(spotify, 100))