Our final dataset after removing missing observations contains 32828 observations with 13 variables. We do not want to keep the missing values and hence we will remove the rows. These corresppond to same 5 observations which are not even 1% of the entire dataset. There are 5 missing values each in track_name, track_artist and track_album_name. # track_danceability track_energy_level live_performed musical_positivity # track_album_name playlist_name playlist_genre playlist_subgenre Missing # track_id track_name track_artist track_popularity Renaming the columns: sp_songs = sp_songs %>% rename(track_danceability = danceability,įinding missing values #Finding missing values Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).ĭata Cleaning #Dimensions of the updated dataset A value above 0.8 provides strong likelihood that the track is live.Ī measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. 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. The spotify_songs data file can be downloaded directly from the Spotify.

Spotify music moods meaning