Context The Cure is one of my favourites groups, that's why I decided to analyze their discography. Content Popularity and audio features for every song and album: * `track_popularity`. The value will be between 0 and 100, with 100 being the most popular. * `duration_ms`. The duration of the track in milliseconds. * `valence`. A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. * `danceability`. Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity.A value of 0.0 is least danceable and 1.0 is most danceable. * `energy`. Represents a perceptual measure of intensity and activity (from 0.0 to 1.0). * `acousticness`. A confidence measure from 0.0 to 1.0 of whether the track is acoustic. * `loudness`. The overall loudness of a track in decibels (typical range between -60 and 0 db). * `speechiness`. Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording, the closer to 1.0 the attribute value. * `instrumentalness`. Predicts whether a track contains no vocals.The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. * `liveness`. Detects the presence of an audience in the recording. A value above 0.8 provides strong likelihood that the track is live. * `key_mode`. The key the track is in. If you want more information about the metrics, please check [here](https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/) Acknowledgements I used the function `get_artist_audio_features()` from the `spotifyr` package, in order to retrieve the popularity and audio features for every song and album for a given artist on Spotify. If you want more information about this package, please check [here](https://github.com/charlie86/spotifyr). Inspiration * Exploratory Data Analysis * Data Visualization