Spotify Project: Exploratory Data Analysis
Anılcan Atik
Dost Karaahmetli
Kutay Akalın
Tunahan Kılıç
November 28th, 2019
1. Data Explanation
Our data obtained directly from Spotify Web API. For API connection, we created “Client ID” and “Client Secret” from Spotify for Developers Website. For this purpose, “spotifyr” package used for making connection.
2. Accessing Spotifty Web API
library(httpuv)
library(spotifyr)
library(tidyverse)
library(knitr)
library(lubridate)
library(ggalt)
library(plotly)
library(scales)
options(max.print=1000000)When connection is made successfully, we can access lots of difrent type data such as aritst, albums, tracks, user profile etc. Here is the Spotify API References. In our project, we will usually use playlist, artist and track data.
3. Gathering Turkey, USA, Japan and Brazil Top 50 Playlists
#Get Turkey Top 50
turkey_top_50_id="37i9dQZEVXbIVYVBNw9D5K"
turkey_top_50_audio_features <- get_playlist_audio_features("spotifycharts", turkey_top_50_id)
#Get USA Top 50
usa_top_50_id = "37i9dQZEVXbLRQDuF5jeBp"
usa_top_50_audio_features <- get_playlist_audio_features("spotifycharts", usa_top_50_id)
#Get Japan Top 50
japan_top_50_id = "37i9dQZEVXbKXQ4mDTEBXq"
japan_top_50_audio_features <- get_playlist_audio_features("spotifycharts", japan_top_50_id)
#Get Brazil Top 50
brazil_top_50_id = "37i9dQZEVXbMXbN3EUUhlg"
brazil_top_50_audio_features <- get_playlist_audio_features("spotifycharts", brazil_top_50_id)
#Combining TR, USA, Japan and Brazil top 50 lists
combined_lists <- bind_rows(turkey_top_50_audio_features, usa_top_50_audio_features, japan_top_50_audio_features, brazil_top_50_audio_features)
glimpse(combined_lists)## Observations: 200
## Variables: 61
## $ playlist_id <chr> "37i9dQZEVXbIVYVBNw9D5K", "...
## $ playlist_name <chr> "Turkey Top 50", "Turkey To...
## $ playlist_img <chr> "https://charts-images.scdn...
## $ playlist_owner_name <chr> "spotifycharts", "spotifych...
## $ playlist_owner_id <chr> "spotifycharts", "spotifych...
## $ danceability <dbl> 0.628, 0.801, 0.810, 0.743,...
## $ energy <dbl> 0.725, 0.688, 0.631, 0.680,...
## $ key <int> 7, 9, 4, 5, 6, 7, 11, 9, 9,...
## $ loudness <dbl> -7.387, -6.620, -7.855, -4....
## $ mode <int> 1, 1, 0, 0, 0, 0, 0, 0, 0, ...
## $ speechiness <dbl> 0.1100, 0.1130, 0.3270, 0.1...
## $ acousticness <dbl> 0.0266, 0.2780, 0.1170, 0.1...
## $ instrumentalness <dbl> 0.00e+00, 8.88e-03, 0.00e+0...
## $ liveness <dbl> 0.0549, 0.1500, 0.1190, 0.1...
## $ valence <dbl> 0.458, 0.410, 0.407, 0.694,...
## $ tempo <dbl> 173.952, 158.003, 144.978, ...
## $ track.id <chr> "36ulbeGLdspdIYSFKXIlmN", "...
## $ analysis_url <chr> "https://api.spotify.com/v1...
## $ time_signature <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
## $ added_at <chr> "1970-01-01T00:00:00Z", "19...
## $ is_local <lgl> FALSE, FALSE, FALSE, FALSE,...
## $ primary_color <lgl> NA, NA, NA, NA, NA, NA, NA,...
## $ added_by.href <chr> "https://api.spotify.com/v1...
## $ added_by.id <chr> "", "", "", "", "", "", "",...
## $ added_by.type <chr> "user", "user", "user", "us...
## $ added_by.uri <chr> "spotify:user:", "spotify:u...
## $ added_by.external_urls.spotify <chr> "https://open.spotify.com/u...
## $ track.artists <list> [<data.frame[1 x 6]>, <dat...
## $ track.available_markets <list> [<"AD", "AE", "AR", "AT", ...
## $ track.disc_number <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ track.duration_ms <int> 269002, 154251, 185566, 196...
## $ track.episode <lgl> FALSE, FALSE, FALSE, FALSE,...
## $ track.explicit <lgl> FALSE, TRUE, TRUE, FALSE, F...
## $ track.href <chr> "https://api.spotify.com/v1...
## $ track.is_local <lgl> FALSE, FALSE, FALSE, FALSE,...
## $ track.name <chr> "Toz Taneleri", "Wir sind K...
## $ track.popularity <int> 66, 60, 78, 70, 81, 78, 62,...
## $ track.preview_url <chr> "https://p.scdn.co/mp3-prev...
## $ track.track <lgl> TRUE, TRUE, TRUE, TRUE, TRU...
## $ track.track_number <int> 1, 11, 1, 1, 1, 1, 2, 3, 6,...
## $ track.type <chr> "track", "track", "track", ...
## $ track.uri <chr> "spotify:track:36ulbeGLdspd...
## $ track.album.album_type <chr> "single", "album", "single"...
## $ track.album.artists <list> [<data.frame[1 x 6]>, <dat...
## $ track.album.available_markets <list> [<"AD", "AE", "AR", "AT", ...
## $ track.album.href <chr> "https://api.spotify.com/v1...
## $ track.album.id <chr> "4abQKohVs72OlJci0C94pl", "...
## $ track.album.images <list> [<data.frame[3 x 3]>, <dat...
## $ track.album.name <chr> "Sarkastik", "Lights Out", ...
## $ track.album.release_date <chr> "2019-11-29", "2019-11-14",...
## $ track.album.release_date_precision <chr> "day", "day", "day", "day",...
## $ track.album.total_tracks <int> 5, 12, 1, 1, 1, 1, 5, 5, 12...
## $ track.album.type <chr> "album", "album", "album", ...
## $ track.album.uri <chr> "spotify:album:4abQKohVs72O...
## $ track.album.external_urls.spotify <chr> "https://open.spotify.com/a...
## $ track.external_ids.isrc <chr> "TR0681900389", "DECE719007...
## $ track.external_urls.spotify <chr> "https://open.spotify.com/t...
## $ video_thumbnail.url <lgl> NA, NA, NA, NA, NA, NA, NA,...
## $ key_name <chr> "G", "A", "E", "F", "F#", "...
## $ mode_name <chr> "major", "major", "minor", ...
## $ key_mode <chr> "G major", "A major", "E mi...4. Adding Sentiments in Each Track
The purpose of this function named “classify_track_sentiment” is important for us to work primarily to reveal the mood of songs and song lists along these lines. Energy and valence are two important factors in terms of interpreting emotion in music. The variations of these two factors, which have values between 0 and 1, in this range determine the songs to be turbulent/angry, happy/joyful, sad/depressing and chill/peaceful.
According to Get Audio Features for a Track, explanations of the corresponding factors are as follows.
4.1. Energy
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.
4.2. Valence
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).
classify_track_sentiment <- function(valence, energy) {
if (is.na(valence) | is.na(energy)) {
return(NA)
}
else if (valence >= .5) {
if (energy >= .5) {
return('Happy/Joyful')
} else {
return('Chill/Peaceful')
}
} else {
if (energy >= .5) {
return('Turbulent/Angry')
} else {
return('Sad/Depressing')
}
}
}
track_sentiment = c()
for (i in 1:200){
track_sentiment[i] = classify_track_sentiment(combined_lists[[15]][[i]], combined_lists[[7]][[i]])
}
#Adding sentiment column to Combined of four countries
combined_lists<-cbind(combined_lists,track_sentiment)
#Adding Artist Column to Combined Music List
track_audio_combined <- combined_lists %>%
select(track.name,track.id,track.artists,track.album.release_date,track.popularity,danceability:tempo,track_sentiment,track.duration_ms)
head(track_audio_combined)## track.name track.id
## 1 Toz Taneleri 36ulbeGLdspdIYSFKXIlmN
## 2 Wir sind Kral 2KlbLTnQ5Wch2oOelW0Y2k
## 3 Arkadas 6bBnnrknLbDoOCUdKMkmnq
## 4 AYA 4IJEw3fDvS6XF4sDc3bvjK
## 5 Dance Monkey 1rgnBhdG2JDFTbYkYRZAku
## 6 Nalan 1LNUxWJifZNEPpd273N2le
## track.artists
## 1 https://api.spotify.com/v1/artists/1KXTegXtnCPKXjRaX1llcD, 1KXTegXtnCPKXjRaX1llcD, Sagopa Kajmer, artist, spotify:artist:1KXTegXtnCPKXjRaX1llcD, https://open.spotify.com/artist/1KXTegXtnCPKXjRaX1llcD
## 2 https://api.spotify.com/v1/artists/5pVRwX5ZQR7hfJ18w8ZYkl, https://api.spotify.com/v1/artists/6LnJKrtFnTEGdbWQ2riWCL, 5pVRwX5ZQR7hfJ18w8ZYkl, 6LnJKrtFnTEGdbWQ2riWCL, Ufo361, Ezhel, artist, artist, spotify:artist:5pVRwX5ZQR7hfJ18w8ZYkl, spotify:artist:6LnJKrtFnTEGdbWQ2riWCL, https://open.spotify.com/artist/5pVRwX5ZQR7hfJ18w8ZYkl, https://open.spotify.com/artist/6LnJKrtFnTEGdbWQ2riWCL
## 3 https://api.spotify.com/v1/artists/2kS0jWMkkFBL0mrl0VotD0, 2kS0jWMkkFBL0mrl0VotD0, Ben Fero, artist, spotify:artist:2kS0jWMkkFBL0mrl0VotD0, https://open.spotify.com/artist/2kS0jWMkkFBL0mrl0VotD0
## 4 https://api.spotify.com/v1/artists/2y1VzMKAa5nmfXKtJL9jnj, https://api.spotify.com/v1/artists/6LnJKrtFnTEGdbWQ2riWCL, 2y1VzMKAa5nmfXKtJL9jnj, 6LnJKrtFnTEGdbWQ2riWCL, Murda, Ezhel, artist, artist, spotify:artist:2y1VzMKAa5nmfXKtJL9jnj, spotify:artist:6LnJKrtFnTEGdbWQ2riWCL, https://open.spotify.com/artist/2y1VzMKAa5nmfXKtJL9jnj, https://open.spotify.com/artist/6LnJKrtFnTEGdbWQ2riWCL
## 5 https://api.spotify.com/v1/artists/2NjfBq1NflQcKSeiDooVjY, 2NjfBq1NflQcKSeiDooVjY, Tones and I, artist, spotify:artist:2NjfBq1NflQcKSeiDooVjY, https://open.spotify.com/artist/2NjfBq1NflQcKSeiDooVjY
## 6 https://api.spotify.com/v1/artists/4XP7cGw4t8BqZ8Du5q3bHg, 4XP7cGw4t8BqZ8Du5q3bHg, Emir Can Igrek, artist, spotify:artist:4XP7cGw4t8BqZ8Du5q3bHg, https://open.spotify.com/artist/4XP7cGw4t8BqZ8Du5q3bHg
## track.album.release_date track.popularity danceability energy key
## 1 2019-11-29 66 0.628 0.725 7
## 2 2019-11-14 60 0.801 0.688 9
## 3 2019-11-08 78 0.810 0.631 4
## 4 2019-09-20 70 0.743 0.680 5
## 5 2019-05-10 81 0.825 0.593 6
## 6 2019-09-06 78 0.540 0.418 7
## loudness mode speechiness acousticness instrumentalness liveness valence
## 1 -7.387 1 0.1100 0.0266 0.000000 0.0549 0.458
## 2 -6.620 1 0.1130 0.2780 0.008880 0.1500 0.410
## 3 -7.855 0 0.3270 0.1170 0.000000 0.1190 0.407
## 4 -4.344 0 0.1030 0.1130 0.123000 0.1830 0.694
## 5 -6.401 0 0.0988 0.6880 0.000161 0.1700 0.540
## 6 -15.570 0 0.0761 0.1900 0.685000 0.1980 0.189
## tempo track_sentiment track.duration_ms
## 1 173.952 Turbulent/Angry 269002
## 2 158.003 Turbulent/Angry 154251
## 3 144.978 Turbulent/Angry 185566
## 4 180.059 Happy/Joyful 196583
## 5 98.078 Happy/Joyful 209754
## 6 91.042 Sad/Depressing 199958artist_names = c()
for (i in 1:200){
artist_names[i] <- track_audio_combined[[3]][[i]][[3]]
}
combined_lists <-cbind(combined_lists, artist_names)
glimpse(combined_lists)## Observations: 200
## Variables: 63
## $ playlist_id <chr> "37i9dQZEVXbIVYVBNw9D5K", "...
## $ playlist_name <chr> "Turkey Top 50", "Turkey To...
## $ playlist_img <chr> "https://charts-images.scdn...
## $ playlist_owner_name <chr> "spotifycharts", "spotifych...
## $ playlist_owner_id <chr> "spotifycharts", "spotifych...
## $ danceability <dbl> 0.628, 0.801, 0.810, 0.743,...
## $ energy <dbl> 0.725, 0.688, 0.631, 0.680,...
## $ key <int> 7, 9, 4, 5, 6, 7, 11, 9, 9,...
## $ loudness <dbl> -7.387, -6.620, -7.855, -4....
## $ mode <int> 1, 1, 0, 0, 0, 0, 0, 0, 0, ...
## $ speechiness <dbl> 0.1100, 0.1130, 0.3270, 0.1...
## $ acousticness <dbl> 0.0266, 0.2780, 0.1170, 0.1...
## $ instrumentalness <dbl> 0.00e+00, 8.88e-03, 0.00e+0...
## $ liveness <dbl> 0.0549, 0.1500, 0.1190, 0.1...
## $ valence <dbl> 0.458, 0.410, 0.407, 0.694,...
## $ tempo <dbl> 173.952, 158.003, 144.978, ...
## $ track.id <chr> "36ulbeGLdspdIYSFKXIlmN", "...
## $ analysis_url <chr> "https://api.spotify.com/v1...
## $ time_signature <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
## $ added_at <chr> "1970-01-01T00:00:00Z", "19...
## $ is_local <lgl> FALSE, FALSE, FALSE, FALSE,...
## $ primary_color <lgl> NA, NA, NA, NA, NA, NA, NA,...
## $ added_by.href <chr> "https://api.spotify.com/v1...
## $ added_by.id <chr> "", "", "", "", "", "", "",...
## $ added_by.type <chr> "user", "user", "user", "us...
## $ added_by.uri <chr> "spotify:user:", "spotify:u...
## $ added_by.external_urls.spotify <chr> "https://open.spotify.com/u...
## $ track.artists <list> [<data.frame[1 x 6]>, <dat...
## $ track.available_markets <list> [<"AD", "AE", "AR", "AT", ...
## $ track.disc_number <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ track.duration_ms <int> 269002, 154251, 185566, 196...
## $ track.episode <lgl> FALSE, FALSE, FALSE, FALSE,...
## $ track.explicit <lgl> FALSE, TRUE, TRUE, FALSE, F...
## $ track.href <chr> "https://api.spotify.com/v1...
## $ track.is_local <lgl> FALSE, FALSE, FALSE, FALSE,...
## $ track.name <chr> "Toz Taneleri", "Wir sind K...
## $ track.popularity <int> 66, 60, 78, 70, 81, 78, 62,...
## $ track.preview_url <chr> "https://p.scdn.co/mp3-prev...
## $ track.track <lgl> TRUE, TRUE, TRUE, TRUE, TRU...
## $ track.track_number <int> 1, 11, 1, 1, 1, 1, 2, 3, 6,...
## $ track.type <chr> "track", "track", "track", ...
## $ track.uri <chr> "spotify:track:36ulbeGLdspd...
## $ track.album.album_type <chr> "single", "album", "single"...
## $ track.album.artists <list> [<data.frame[1 x 6]>, <dat...
## $ track.album.available_markets <list> [<"AD", "AE", "AR", "AT", ...
## $ track.album.href <chr> "https://api.spotify.com/v1...
## $ track.album.id <chr> "4abQKohVs72OlJci0C94pl", "...
## $ track.album.images <list> [<data.frame[3 x 3]>, <dat...
## $ track.album.name <chr> "Sarkastik", "Lights Out", ...
## $ track.album.release_date <chr> "2019-11-29", "2019-11-14",...
## $ track.album.release_date_precision <chr> "day", "day", "day", "day",...
## $ track.album.total_tracks <int> 5, 12, 1, 1, 1, 1, 5, 5, 12...
## $ track.album.type <chr> "album", "album", "album", ...
## $ track.album.uri <chr> "spotify:album:4abQKohVs72O...
## $ track.album.external_urls.spotify <chr> "https://open.spotify.com/a...
## $ track.external_ids.isrc <chr> "TR0681900389", "DECE719007...
## $ track.external_urls.spotify <chr> "https://open.spotify.com/t...
## $ video_thumbnail.url <lgl> NA, NA, NA, NA, NA, NA, NA,...
## $ key_name <chr> "G", "A", "E", "F", "F#", "...
## $ mode_name <chr> "major", "major", "minor", ...
## $ key_mode <chr> "G major", "A major", "E mi...
## $ track_sentiment <fct> Turbulent/Angry, Turbulent/...
## $ artist_names <fct> Sagopa Kajmer, Ufo361, Ben ...5. Plot Analysis
5.1. Country Playlists by Key
country_by_key <- combined_lists%>%
select(playlist_name, key_name, track.name)%>%
group_by(playlist_name) %>% count(key_name, sort = TRUE)
country_by_key## # A tibble: 47 x 3
## # Groups: playlist_name [4]
## playlist_name key_name n
## <chr> <chr> <int>
## 1 United States Top 50 G 11
## 2 Japan Top 50 C# 8
## 3 Turkey Top 50 A 8
## 4 Brazil Top 50 B 7
## 5 Brazil Top 50 F# 7
## 6 Japan Top 50 G 7
## 7 United States Top 50 C# 7
## 8 Brazil Top 50 D 6
## 9 Turkey Top 50 F 6
## 10 Turkey Top 50 F# 6
## # ... with 37 more rowsggplot(country_by_key, aes(x = key_name, y = n, fill = playlist_name)) +
geom_bar(stat = "identity") +
labs(title = "Playlists by Key Name", x = "Key Name", y = "Total Number of Keys") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"), legend.title = element_blank())
5.2. Common Songs in Playlists
common_songs <- combined_lists %>% group_by(track.name, artist_names) %>%
summarise(n_songs = n()) %>%
filter(n_songs >= 2) %>%
arrange(desc(n_songs))
common_songs## # A tibble: 7 x 3
## # Groups: track.name [7]
## track.name artist_names n_songs
## <chr> <fct> <int>
## 1 bad guy Billie Eilish 3
## 2 Dance Monkey Tones and I 3
## 3 everything i wanted Billie Eilish 3
## 4 Don't Start Now Dua Lipa 2
## 5 Heartless The Weeknd 2
## 6 Memories Maroon 5 2
## 7 Señorita Shawn Mendes 2ggplot(common_songs, aes(x = reorder(track.name, n_songs), y = n_songs, fill = artist_names)) +
geom_bar(stat = "identity") +
labs(title = "Common Songs on Playlists", x = "Song Name", y = "Number of Songs") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"), legend.title = element_blank()) +
coord_flip()
5.3 .Danceability Density of Playlists
ggplot(combined_lists, aes(x = danceability, fill = playlist_name)) +
geom_density(alpha = 0.7, color = NA)+
labs(x = "Danceability", y = "Density") +
guides(fill = guide_legend(title = "Playlist"))+
theme_minimal()+
ggtitle("Distribution of Danceability Data") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"), legend.title = element_blank())
5.4. Energy and Valence Range of Playlists
playlist_feature_range <- combined_lists %>%
group_by(playlist_name)%>%
mutate(max_energy=max(energy), max_valence = max(valence))%>%
mutate(min_energy=min(energy), min_valence = min(valence))%>%
select(playlist_name, min_energy, max_energy, min_valence, max_valence)%>%
unique()
playlist_feature_range## # A tibble: 4 x 5
## # Groups: playlist_name [4]
## playlist_name min_energy max_energy min_valence max_valence
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Turkey Top 50 0.191 0.931 0.105 0.928
## 2 United States Top 50 0.158 0.816 0.0605 0.947
## 3 Japan Top 50 0.225 0.979 0.184 0.962
## 4 Brazil Top 50 0.426 0.958 0.152 0.9645.4.1. Energy and Valence Range of Playlists with Dumbbell Plot
energy_range_plot <- plot_ly(playlist_feature_range, color = I("gray80"),
hoverinfo = 'text') %>%
add_segments(x = ~max_energy, xend = ~min_energy, y = ~playlist_name, yend = ~playlist_name, showlegend = FALSE) %>%
add_segments(x = ~max_valence, xend = ~min_valence, y = ~playlist_name, yend = ~playlist_name, showlegend = FALSE) %>%
add_markers(x = ~max_energy, y = ~playlist_name, name = "Maximum Energy Value", color = I("red"), size = 2.5, text=~paste('Max Energy: ', max_energy)) %>%
add_markers(x = ~min_energy, y = ~playlist_name, name = "Minimum Energy Value", color = I("blue"), size = 2.5, text=~paste('Min Energy: ', min_energy))%>%
add_markers(x = ~max_valence, y = ~playlist_name, name = "Maximum Valence Value", color = I("#395B74"), size = 2.5, text=~paste('Max Valence: ', max_valence)) %>%
add_markers(x = ~min_valence, y = ~playlist_name, name = "Minimum Valence Value", color = I("#F7BC08"), size = 2.5, text=~paste('Min Valence: ', min_valence))%>%
layout(
title = "Playlist Energy and Valence Range",
xaxis = list(title = "Energy and Valence"),
yaxis= list(title="Country Lists"))
ggplotly(energy_range_plot)5.5. Excitement of Playlists
excitement_of_playlist <- combined_lists %>% group_by(playlist_name) %>%
select(playlist_name, track.name, valence, energy, loudness, danceability, tempo) %>%
mutate(excitement = loudness + tempo + (energy*100) + (danceability*100) + (valence*100), excitement_mean = mean(excitement))
ggplot(excitement_of_playlist, aes(x = excitement, fill = playlist_name, color = playlist_name)) +
geom_histogram(binwidth = 30, position = "identity", alpha = 0.7) +
geom_vline(data = excitement_of_playlist, aes(xintercept = excitement_mean, color = playlist_name),
linetype = "dashed") +
labs(title = "Excitement Distribution of Playlists", y = "Count", x = "Excitement Scale") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"), axis.title.y = element_text(size = 14, face = "bold"),
legend.title = element_blank())
5.6. Mean Excitement of Playlists
excitement_mean <- excitement_of_playlist %>% group_by(playlist_name) %>% select(excitement_mean) %>% unique()
excitement_mean## # A tibble: 4 x 2
## # Groups: playlist_name [4]
## playlist_name excitement_mean
## <chr> <dbl>
## 1 Turkey Top 50 300.
## 2 United States Top 50 289.
## 3 Japan Top 50 306.
## 4 Brazil Top 50 344.ggplot(excitement_mean, aes(x = reorder(playlist_name, excitement_mean), y = excitement_mean, fill = playlist_name)) +
geom_bar(stat ="identity") +
labs(title = "Excitement Comparison of Playlists", x = "Country Playlist Names", y = "Means of Excitement", fill = "Country Charts",
caption = "The low score shows that the list is boring. \n Excitement Formula: (loudness + tempo + (energy*100) + (danceability*100) + (valence*100))") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
legend.title = element_blank())
5.7. Sentiment Analysis of Country Playlists with Gradient Chart
sentiment_by_countries <- combined_lists %>% group_by(playlist_name) %>%
select(playlist_name, track.name, artist_names, valence, energy, track_sentiment)
sentiment_by_countries## # A tibble: 200 x 6
## # Groups: playlist_name [4]
## playlist_name track.name artist_names valence energy track_sentiment
## <chr> <chr> <fct> <dbl> <dbl> <fct>
## 1 Turkey Top 50 Toz Taneleri Sagopa Kajmer 0.458 0.725 Turbulent/Angry
## 2 Turkey Top 50 Wir sind Kral Ufo361 0.41 0.688 Turbulent/Angry
## 3 Turkey Top 50 Arkadas Ben Fero 0.407 0.631 Turbulent/Angry
## 4 Turkey Top 50 AYA Murda 0.694 0.68 Happy/Joyful
## 5 Turkey Top 50 Dance Monkey Tones and I 0.54 0.593 Happy/Joyful
## 6 Turkey Top 50 Nalan Emir Can Igr~ 0.189 0.418 Sad/Depressing
## 7 Turkey Top 50 Neyse Sagopa Kajmer 0.317 0.776 Turbulent/Angry
## 8 Turkey Top 50 Vazgeçtim In~ Sagopa Kajmer 0.507 0.638 Happy/Joyful
## 9 Turkey Top 50 Yemin Olsun Ufo361 0.31 0.582 Turbulent/Angry
## 10 Turkey Top 50 Neresi? BEGE 0.652 0.562 Happy/Joyful
## # ... with 190 more rows5.7.1. Group of sentiment count by each contry
sentiment_by_countries %>% count(track_sentiment, sort = TRUE)## # A tibble: 16 x 3
## # Groups: playlist_name [4]
## playlist_name track_sentiment n
## <chr> <fct> <int>
## 1 Brazil Top 50 Happy/Joyful 45
## 2 Japan Top 50 Happy/Joyful 26
## 3 Turkey Top 50 Turbulent/Angry 23
## 4 Japan Top 50 Turbulent/Angry 19
## 5 Turkey Top 50 Happy/Joyful 19
## 6 United States Top 50 Happy/Joyful 16
## 7 United States Top 50 Turbulent/Angry 14
## 8 United States Top 50 Sad/Depressing 11
## 9 United States Top 50 Chill/Peaceful 9
## 10 Turkey Top 50 Sad/Depressing 6
## 11 Japan Top 50 Sad/Depressing 3
## 12 Brazil Top 50 Sad/Depressing 2
## 13 Brazil Top 50 Turbulent/Angry 2
## 14 Japan Top 50 Chill/Peaceful 2
## 15 Turkey Top 50 Chill/Peaceful 2
## 16 Brazil Top 50 Chill/Peaceful 1ggplot(sentiment_by_countries,aes(x = valence, y = energy, color = track_sentiment)) + geom_point() +
labs(color = "", title = "Sentiment Analysis by Each Country") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"), legend.title = element_blank()) +
scale_x_continuous(expand = c(0, 0), limits = c(0, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, 1)) +
geom_label(aes(x = 0.12, y = 0.98, label = "Turbulent/Angry"), label.padding = unit(1, "mm"), fill = "grey", color="white") +
geom_label(aes(x = 0.90, y = 0.98, label = "Happy/Joyful"), label.padding = unit(1, "mm"), fill = "grey", color="white") +
geom_label(aes(x = 0.12, y = 0.025, label = "Sad/Depressing"), label.padding = unit(1, "mm"), fill = "grey", color="white") +
geom_label(aes(x = 0.895, y = 0.025, label = "Chill/Peaceful"), label.padding = unit(1, "mm"), fill = "grey", color="white") +
geom_segment(aes(x = 1, y = 0, xend = 1, yend = 1)) +
geom_segment(aes(x = 0, y = 0, xend = 0, yend = 1)) +
geom_segment(aes(x = 0, y = 0, xend = 1, yend = 0)) +
geom_segment(aes(x = 0, y = 0.5, xend = 1, yend = 0.5)) +
geom_segment(aes(x = 0.5, y = 0, xend = 0.5, yend = 1)) +
geom_segment(aes(x = 0, y = 1, xend = 1, yend = 1)) +
facet_wrap(~ playlist_name)
6. Turkey Top 200 Daily Data Between 2017-2019
Data obtained from Spotify Charts.
topturkey200<-readRDS(url("https://github.com/pjournal/mef03g-spo-R-ify/blob/master/turkeytop200.rds?raw=true"))
glimpse(topturkey200)## Observations: 211,400
## Variables: 6
## $ Position <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", ...
## $ Track.Name <chr> "Gece Gölgenin Rahatina Bak", "Starboy", "Affet", "...
## $ Artist <chr> "Çagatay Akman", "The Weeknd", "Müslüm Gürses", "Cl...
## $ Streams <chr> "80607", "44427", "34889", "28400", "25425", "23032...
## $ URL <chr> "https://open.spotify.com/track/3P31rcl0ym5paqRdwSi...
## $ Date <date> 2017-01-01, 2017-01-01, 2017-01-01, 2017-01-01, 20...6.1. Monthly Change in Total Streams
#First cal
#topturkey200 %>% group_by(Artist)%>% summarise(Total_number=n()) %>% arrange(desc(Total_number))
change<-topturkey200 %>% mutate(Year_Month = format(Date,"%Y/%m")) %>% group_by(Year_Month) %>% summarise(Total_Stream=sum(as.numeric(Streams)))
ggplot(change, aes(x = Year_Month,y=Total_Stream,group=1)) + geom_point() + geom_smooth() + theme(axis.text.x = element_text(angle = 90),title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5)) + labs(x = "Month", y = "Total Streams",title = "Total Stream Change") + scale_y_continuous(labels = comma)
6.2. Most Streamed 20 Tracks
rank<-topturkey200 %>% group_by(Artist,Track.Name) %>% summarise(Total_Stream=sum(as.numeric(Streams))) %>% arrange(desc(Total_Stream))
head(rank,n=20L)## # A tibble: 20 x 3
## # Groups: Artist [12]
## Artist Track.Name Total_Stream
## <chr> <chr> <dbl>
## 1 Ezhel Geceler 44258676
## 2 Ezhel Felaket 43699884
## 3 Ufuk Beydemir Ay Tenli Kadin 35480606
## 4 Norm Ender Mekanin Sahibi 34730473
## 5 Ben Fero Biladerim Için 33648205
## 6 Ezhel Imkansizim 32607042
## 7 Ben Fero 3 2 1 32086343
## 8 Ben Fero Demet Akalin 28807007
## 9 Anil Piyanci KAFA10 28704551
## 10 Reynmen Ela 28246604
## 11 Ezhel Kazidik Tirnaklarla 27783484
## 12 Yüzyüzeyken Konusuruz Ne Farkeder 27540693
## 13 Ceza Neyim Var Ki (feat. Sagopa K) 27067016
## 14 Murda AYA 26111866
## 15 Yüzyüzeyken Konusuruz Dinle Beni Bi' 25431753
## 16 Feride Hilal Akin Yok Yok 24360307
## 17 MERO Olabilir 23990740
## 18 Ed Sheeran Shape of You 23958838
## 19 Anil Piyanci Birakman Dogru Mu 23643712
## 20 Reynmen Derdim Olsun 235455146.3. Sentiment Analysis of Tracks
6.3.1. Data Preparation
top_200_audio_features<-topturkey200 %>% mutate(id=substring(topturkey200$URL,32))
top_200_audio_features<-top_200_audio_features[!duplicated(top_200_audio_features[,c('id')]),]
glimpse(top_200_audio_features)## Observations: 2,874
## Variables: 7
## $ Position <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", ...
## $ Track.Name <chr> "Gece Gölgenin Rahatina Bak", "Starboy", "Affet", "...
## $ Artist <chr> "Çagatay Akman", "The Weeknd", "Müslüm Gürses", "Cl...
## $ Streams <chr> "80607", "44427", "34889", "28400", "25425", "23032...
## $ URL <chr> "https://open.spotify.com/track/3P31rcl0ym5paqRdwSi...
## $ Date <date> 2017-01-01, 2017-01-01, 2017-01-01, 2017-01-01, 20...
## $ id <chr> "3P31rcl0ym5paqRdwSiZps", "5aAx2yezTd8zXrkmtKl66Z",...Id_list=top_200_audio_features$id
#Tracks feautres obtaining code is below. Because of the process time data frame downloaded from github repository.
#a<-unique(Id_list)
#tracks_features=get_track_audio_features(a[1])
#for (x in 2:length(a)){
# tracks_features <- rbind(tracks_features,get_track_audio_features(a[x]))
#}
#tracks_features<-tracks_features%>%slice(-1)
tracks_features<-readRDS(url("https://github.com/pjournal/mef03g-spo-R-ify/blob/master/top200_tracks_features.rds?raw=true"))
glimpse(tracks_features)## Observations: 2,874
## Variables: 18
## $ danceability <dbl> 0.769, 0.681, 0.424, 0.720, 0.476, 0.748, 0.4...
## $ energy <dbl> 0.837, 0.594, 0.666, 0.763, 0.718, 0.524, 0.3...
## $ key <int> 6, 7, 7, 9, 8, 8, 4, 4, 1, 5, 6, 6, 0, 8, 7, ...
## $ loudness <dbl> -4.057, -7.028, -6.683, -4.068, -5.309, -5.59...
## $ mode <int> 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, ...
## $ speechiness <dbl> 0.1400, 0.2820, 0.0473, 0.0523, 0.0576, 0.033...
## $ acousticness <dbl> 0.65200, 0.16500, 0.39600, 0.40600, 0.07840, ...
## $ instrumentalness <dbl> 0.00e+00, 3.49e-06, 1.69e-04, 0.00e+00, 1.02e...
## $ liveness <dbl> 0.0986, 0.1340, 0.1200, 0.1800, 0.1220, 0.111...
## $ valence <dbl> 0.8190, 0.5350, 0.2750, 0.7420, 0.1420, 0.661...
## $ tempo <dbl> 100.962, 186.054, 160.079, 101.965, 199.864, ...
## $ type <chr> "audio_features", "audio_features", "audio_fe...
## $ id <chr> "3P31rcl0ym5paqRdwSiZps", "5aAx2yezTd8zXrkmtK...
## $ uri <chr> "spotify:track:3P31rcl0ym5paqRdwSiZps", "spot...
## $ track_href <chr> "https://api.spotify.com/v1/tracks/3P31rcl0ym...
## $ analysis_url <chr> "https://api.spotify.com/v1/audio-analysis/3P...
## $ duration_ms <int> 163960, 230453, 279475, 251088, 205947, 24496...
## $ time_signature <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...top_200_audio_features <- inner_join(top_200_audio_features,tracks_features,by="id")
Sentiment=c()
for (i in 1:nrow(top_200_audio_features)){
Sentiment[i]=classify_track_sentiment(valence=top_200_audio_features$valence[i],energy=top_200_audio_features$energy[i])
}
top_200_audio_features<-cbind(top_200_audio_features,Sentiment)
glimpse(top_200_audio_features)## Observations: 2,874
## Variables: 25
## $ Position <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", ...
## $ Track.Name <chr> "Gece Gölgenin Rahatina Bak", "Starboy", "Aff...
## $ Artist <chr> "Çagatay Akman", "The Weeknd", "Müslüm Gürses...
## $ Streams <chr> "80607", "44427", "34889", "28400", "25425", ...
## $ URL <chr> "https://open.spotify.com/track/3P31rcl0ym5pa...
## $ Date <date> 2017-01-01, 2017-01-01, 2017-01-01, 2017-01-...
## $ id <chr> "3P31rcl0ym5paqRdwSiZps", "5aAx2yezTd8zXrkmtK...
## $ danceability <dbl> 0.769, 0.681, 0.424, 0.720, 0.476, 0.748, 0.4...
## $ energy <dbl> 0.837, 0.594, 0.666, 0.763, 0.718, 0.524, 0.3...
## $ key <int> 6, 7, 7, 9, 8, 8, 4, 4, 1, 5, 6, 6, 0, 8, 7, ...
## $ loudness <dbl> -4.057, -7.028, -6.683, -4.068, -5.309, -5.59...
## $ mode <int> 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, ...
## $ speechiness <dbl> 0.1400, 0.2820, 0.0473, 0.0523, 0.0576, 0.033...
## $ acousticness <dbl> 0.65200, 0.16500, 0.39600, 0.40600, 0.07840, ...
## $ instrumentalness <dbl> 0.00e+00, 3.49e-06, 1.69e-04, 0.00e+00, 1.02e...
## $ liveness <dbl> 0.0986, 0.1340, 0.1200, 0.1800, 0.1220, 0.111...
## $ valence <dbl> 0.8190, 0.5350, 0.2750, 0.7420, 0.1420, 0.661...
## $ tempo <dbl> 100.962, 186.054, 160.079, 101.965, 199.864, ...
## $ type <chr> "audio_features", "audio_features", "audio_fe...
## $ uri <chr> "spotify:track:3P31rcl0ym5paqRdwSiZps", "spot...
## $ track_href <chr> "https://api.spotify.com/v1/tracks/3P31rcl0ym...
## $ analysis_url <chr> "https://api.spotify.com/v1/audio-analysis/3P...
## $ duration_ms <int> 163960, 230453, 279475, 251088, 205947, 24496...
## $ time_signature <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
## $ Sentiment <fct> Happy/Joyful, Happy/Joyful, Turbulent/Angry, ...6.3.2. Sentiment Bar Graph
sent_count <- top_200_audio_features %>% group_by(Sentiment) %>% count()
ggplot(sent_count, aes(x=Sentiment, y=n, fill=Sentiment)) +
geom_bar(stat="identity") +
labs(title = "Sentiment Count", x = "Sentiment Distribution", y = "Count of Sentiments") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"), legend.title = element_blank())
6.3.3. Sentiment Gradient Chart
ggplot(top_200_audio_features,aes(x = valence, y = energy, color = Sentiment)) + geom_point() +
labs(color = "", title = "Sentiment Analysis of Turkey Top 200 Chart Between 2017 and 2019") +
theme(title = element_text(size = 16, face = "bold"), plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"), legend.title = element_blank()) +
scale_x_continuous(expand = c(0, 0), limits = c(0, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, 1)) +
geom_label(aes(x = 0.25, y = 0.97, label = "Turbulent/Angry"), label.padding = unit(2, "mm"), fill = "darkgrey", color="white") +
geom_label(aes(x = 0.75, y = 0.97, label = "Happy/Joyful"), label.padding = unit(2, "mm"), fill = "darkgrey", color="white") +
geom_label(aes(x = 0.25, y = 0.03, label = "Sad/Depressing"), label.padding = unit(2, "mm"), fill = "darkgrey", color="white") +
geom_label(aes(x = 0.75, y = 0.03, label = "Chill/Peaceful"), label.padding = unit(2, "mm"), fill = "darkgrey", color="white") +
geom_segment(aes(x = 1, y = 0, xend = 1, yend = 1)) +
geom_segment(aes(x = 0, y = 0, xend = 0, yend = 1)) +
geom_segment(aes(x = 0, y = 0, xend = 1, yend = 0)) +
geom_segment(aes(x = 0, y = 0.5, xend = 1, yend = 0.5)) +
geom_segment(aes(x = 0.5, y = 0, xend = 0.5, yend = 1)) +
geom_segment(aes(x = 0, y = 1, xend = 1, yend = 1)) 