Shiny App for Billboard Music Charts Data
The Billboard Hot 100 is the music industry standard record chart in the United States for songs, published weekly by Billboard magazine. Chart rankings are based on sales (physical and digital), radio play, and online streaming in the United States.
The Billboard Hot 100 was first released in August 1958, so for this analysis the data includes every week of every year between 1959 and 2019.
The data contains two files.
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billboard.csv
variable class
description
url character
Billboard Chart URL
week_id character
week_position double
Week position 1: 100
song character
performer character
Performer name
song_id character
Song ID, combo of song/singer
instance double
Instance (this is used to separate breaks on the chart for a given song. Example, an instance of 6 tells you that this is the sixth time this song has appeared on the chart)
previous_week_position double
Previous week position
peak_position double
Peak position as of that week
weeks_on_chart double
Weeks on chart as of that week
audio_features.csv
variable class
description
song_id character
performer character
Performer name
song character
spotify_genre character
spotify_track_id character
spotify_track_preview_url character
Spotify URL
spotify_track_duration_ms double
Duration in ms
spotify_track_explicit logical
Is explicit
spotify_track_album character
Album name
danceability double
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 double
Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
key double
The estimated overall key of the track. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.
variable class
description
loudness double
The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
mode double
Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
speechiness double
Speechiness detects the presence of spoken words in a track. The more exclusively speech- like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
acousticness double
A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
instrumentalness double
Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
liveness double
Detects the presence of an audience in the recording. Higher liveness values represent an
variable class
description
increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
valence double
A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. 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).
tempo double
The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
time_signature double
Time signature
spotify_track_popularity double
Popularity
We are hoping to get started with a simple shiny app that allows a user to explore the data a bit on their own. Each question should have an executable Shiny app code chunk that works when run (so make sure the data is loaded from a relative file path).
1. Top Songs
For the input, allow the user to select the years to include in a slider range.
As the output, present an interactive datatable (DT package) with the Top 20 songs (ranked by total weeks in the billboard Top 100) along with their artists, their peak billboard position, and the total weeks they were in the Billboard Top 100.
2. Top Artists
Start a new shiny app from scratch. As the single input, implement a full text search bar (hint: here is an elegant implementation) to select one artist.
Subset the dataset to the selected artist and show a visualization of the billboard ranking of all songs of the artist over time (i.e. a rank chart or bump chart).
3. Audio Features
Join the billboard data (billboard.csv) with the Spotify audio features (audio_features.csv). In your shiny app, allow the user to select as inputs:
• a genre (spotify_genre) [allow multiple selection]
• a year range
• an artist (with the full-text search board)
• an indicator whether the song reached Top 1, Top 10, or Top 20 of the billboard charts
For the visualization, create a plotly scatter plot with two of the audio features as axes. Add a hover text that show the artist, song title, the year, and the billboard peak position.
If you run into issues with the app being slow when the selection is large, feel free to select a subset for the visualization (e.g. max 500 songs) or to adjust in other ways.
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