Higher liveness values represent an increased probability that the track was performed live. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.ĭetects the presence of an audience in the recording. For example, death metal has high energy, while a Bach prelude scores low on the scale. Typically, energetic tracks feel fast, loud, and noisy. A value of 0.0 is least danceable and 1.0 is most danceable.Įnergy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Song Popularity (0-100) where higher is betterĭanceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. The metadata for these variables is provided below: Variable Name Now, we have shorten our variables list from 23 to 13. # 'playlist_genre' 'playlist_subgenre' 'danceability' # 'track_popularity' 'track_album_name' 'playlist_name' Sp_data$playlist_subgenre <- as.factor(sp_data$playlist_subgenre) #Selecting the interesting variables Sp_data$playlist_genre <- as.factor(sp_data$playlist_genre) Sp_data$playlist_name <- as.factor(sp_data$playlist_name) Sp_data$track_album_name <- as.factor(sp_data$track_album_name) Sp_data$track_name <- as.factor(sp_data$track_name) Sp_data$track_artist <- as.factor(sp_data$track_artist) Sp_data$track_id <- as.factor(sp_data$track_id) #Converting the non-numerical variables into categorical variables Hence, We will prune our variables’ list and explore the dataset further with respect to these variables only. The variable “playlist_genre” contains 6 distinct categories and “playlist_subgenre” contains 24 distinct categories respectively, so it converted to factor type it would be easier to analyze. Firstly, There are these 7 variables which should better be cast in factor datatypes for better analysis results. Not all of the 23 variables are relevant for our analysis. # 'track_album_release_date' 'playlist_name' Names of the variables are below: colnames(sp_data) # 'track_id' 'track_name' The dataset contains 32833 observations and 23 variables.

#Loading the datasetĭata Description #Display the dimensions of raw dataset

This package was authored to make it easily accesible for anyone to get their own data or general metadata around songs from the Spotify’s API. This dataset comes originally from spotifyr package. I still have it saved to my library, so it disappears when it’s unavailable and reappears when it feels like it.The spotify_songs data file can be downloaded directly from the Spotify. I have seen it exactly twice in the last week but when the time comes for it to update, it vanishes again.

Gone but not forgotten.ĮDIT #3: AS OF 3/29/23, IT IS BACK UP AND RUNNING, but only very occasionally. Hopefully coming soon to a region near you 🤞🏼Įdit #2: as of 12/28, the playlist shows as deleted or privated by the creator, even in the US.

I would love to know what genres you get if you want to share in the comments! All of this is fascinating to me, I’m having a great time!Įdit to add: Apologies to international friends, looks like this is a US only feature for now. Here is a sample of genres that have shown up for my friends and I: hippie San Francisco, angst y2k, avoidant soda pop, nervous Glastonbury, chill pop softie, boy band tween. Spotify has soft launched a new user-generated playlist that curates a playlist of 50 songs and changes every two hours based on what type of music you listen to throughout the day! I was only able to access it because a link was sent to me by a friend, so I’ll share it here.

Spotify music moods meaning