Spotify Data Analysis

Spotify has become the most populer and widely used music streaming platform today. It offere a variety collections of songs, genres, and artist from around the world which listener can enjoy and have access to. We are going to analyse and try to do some clustering songs on Spotify based on its audio features.

1 Import Package

Let’s import package

library(dplyr)
library(lubridate)
library(GGally)
library(factoextra)
library(FactoMineR)

2 Data Preprocessing

First of all, we read the data

spotify <- read.csv("SpotifyFeatures.csv")
head(spotify)

the data has 232.725 rows and 18 columns, then we can check type of data

glimpse(spotify)

#> Rows: 232,725
#> Columns: 18
#> $ genre            <chr> "Movie", "Movie", "Movie", "Movie", "Movie", "Movie",…
#> $ artist_name      <chr> "Henri Salvador", "Martin & les fées", "Joseph Willia…
#> $ track_name       <chr> "C'est beau de faire un Show", "Perdu d'avance (par G…
#> $ track_id         <chr> "0BRjO6ga9RKCKjfDqeFgWV", "0BjC1NfoEOOusryehmNudP", "…
#> $ popularity       <int> 0, 1, 3, 0, 4, 0, 2, 15, 0, 10, 0, 2, 4, 3, 0, 0, 0, …
#> $ acousticness     <dbl> 0.61100, 0.24600, 0.95200, 0.70300, 0.95000, 0.74900,…
#> $ danceability     <dbl> 0.389, 0.590, 0.663, 0.240, 0.331, 0.578, 0.703, 0.41…
#> $ duration_ms      <int> 99373, 137373, 170267, 152427, 82625, 160627, 212293,…
#> $ energy           <dbl> 0.9100, 0.7370, 0.1310, 0.3260, 0.2250, 0.0948, 0.270…
#> $ instrumentalness <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.123…
#> $ key              <chr> "C#", "F#", "C", "C#", "F", "C#", "C#", "F#", "C", "G…
#> $ liveness         <dbl> 0.3460, 0.1510, 0.1030, 0.0985, 0.2020, 0.1070, 0.105…
#> $ loudness         <dbl> -1.828, -5.559, -13.879, -12.178, -21.150, -14.970, -…
#> $ mode             <chr> "Major", "Minor", "Minor", "Major", "Major", "Major",…
#> $ speechiness      <dbl> 0.0525, 0.0868, 0.0362, 0.0395, 0.0456, 0.1430, 0.953…
#> $ tempo            <dbl> 166.969, 174.003, 99.488, 171.758, 140.576, 87.479, 8…
#> $ time_signature   <chr> "4/4", "4/4", "5/4", "4/4", "4/4", "4/4", "4/4", "4/4…
#> $ valence          <dbl> 0.8140, 0.8160, 0.3680, 0.2270, 0.3900, 0.3580, 0.533…

our data has 2 columns as.factor , 4 character the rest is desimal. Let’s check unique value from time signature and genre

unique(spotify$time_signature)

#> [1] "4/4" "5/4" "3/4" "1/4" "0/4"

unique(spotify$genre)

#>  [1] "Movie"            "R&B"              "A Capella"        "Alternative"     
#>  [5] "Country"          "Dance"            "Electronic"       "Anime"           
#>  [9] "Folk"             "Blues"            "Opera"            "Hip-Hop"         
#> [13] "Children's Music" "Children’s Music" "Rap"              "Indie"           
#> [17] "Classical"        "Pop"              "Reggae"           "Reggaeton"       
#> [21] "Jazz"             "Rock"             "Ska"              "Comedy"          
#> [25] "Soul"             "Soundtrack"       "World"

the result unique value time signature is 5 value, and genre has 27 type unique value.

Now we’re going to clustering of feature the songs, columns such as track_id, key, artist_name, track_name are not required.

spotify_clean <- spotify %>% 
  select(-c(track_id,key,artist_name,track_name))%>% 
  mutate(genre = as.factor(genre)) %>% 
  mutate_if(is.character, as.factor)
spotify_clean

length(unique(spotify_clean$track_name))

#> [1] 0

no missing value now

colSums(is.na(spotify_clean))

#>            genre       popularity     acousticness     danceability 
#>                0                0                0                0 
#>      duration_ms           energy instrumentalness         liveness 
#>                0                0                0                0 
#>         loudness             mode      speechiness            tempo 
#>                0                0                0                0 
#>   time_signature          valence 
#>                0                0

we can see summary after cleaning data

summary(spotify_clean)

#>         genre          popularity      acousticness     danceability   
#>  Comedy    :  9681   Min.   :  0.00   Min.   :0.0000   Min.   :0.0569  
#>  Soundtrack:  9646   1st Qu.: 29.00   1st Qu.:0.0376   1st Qu.:0.4350  
#>  Indie     :  9543   Median : 43.00   Median :0.2320   Median :0.5710  
#>  Jazz      :  9441   Mean   : 41.13   Mean   :0.3686   Mean   :0.5544  
#>  Pop       :  9386   3rd Qu.: 55.00   3rd Qu.:0.7220   3rd Qu.:0.6920  
#>  Electronic:  9377   Max.   :100.00   Max.   :0.9960   Max.   :0.9890  
#>  (Other)   :175651                                                     
#>   duration_ms          energy          instrumentalness       liveness      
#>  Min.   :  15387   Min.   :0.0000203   Min.   :0.0000000   Min.   :0.00967  
#>  1st Qu.: 182857   1st Qu.:0.3850000   1st Qu.:0.0000000   1st Qu.:0.09740  
#>  Median : 220427   Median :0.6050000   Median :0.0000443   Median :0.12800  
#>  Mean   : 235122   Mean   :0.5709577   Mean   :0.1483012   Mean   :0.21501  
#>  3rd Qu.: 265768   3rd Qu.:0.7870000   3rd Qu.:0.0358000   3rd Qu.:0.26400  
#>  Max.   :5552917   Max.   :0.9990000   Max.   :0.9990000   Max.   :1.00000  
#>                                                                             
#>     loudness          mode         speechiness         tempo       
#>  Min.   :-52.457   Major:151744   Min.   :0.0222   Min.   : 30.38  
#>  1st Qu.:-11.771   Minor: 80981   1st Qu.:0.0367   1st Qu.: 92.96  
#>  Median : -7.762                  Median :0.0501   Median :115.78  
#>  Mean   : -9.570                  Mean   :0.1208   Mean   :117.67  
#>  3rd Qu.: -5.501                  3rd Qu.:0.1050   3rd Qu.:139.05  
#>  Max.   :  3.744                  Max.   :0.9670   Max.   :242.90  
#>                                                                    
#>  time_signature    valence      
#>  0/4:     8     Min.   :0.0000  
#>  1/4:  2608     1st Qu.:0.2370  
#>  3/4: 24111     Median :0.4440  
#>  4/4:200760     Mean   :0.4549  
#>  5/4:  5238     3rd Qu.:0.6600  
#>                 Max.   :1.0000  
#> 

3 PCA

Principle component analysis is used in exploratory data analysis and for making predictive models. It is a dimensionality reduction method that is used to reduce the dimension of large datasets into smaller ones which can be easily analyzed and visualized. Althought the dimension is reduced, most of the information is still retained in the dataset.

firstly, we are going to change numerik columns into numerik and categoric columns into factor and made into colnames.

# nama kolom numerik (quantitative)
quanti <- spotify_clean %>% 
  select_if(is.numeric) %>% 
  colnames()

# indeks kolom numerik
quantivar <- which(colnames(spotify_clean) %in% quanti)

# nama kolom kategorik (qualitative)
quali <- spotify_clean %>% 
  select_if(is.factor) %>% 
  colnames()

# indeks kolom kategorik
qualivar <- which(colnames(spotify_clean) %in% quali)

spotify_num <- spotify_clean %>% 
  select_if(is.numeric)

then we check again strukture data. we use numeric data only

glimpse(spotify_num)

#> Rows: 232,725
#> Columns: 11
#> $ popularity       <int> 0, 1, 3, 0, 4, 0, 2, 15, 0, 10, 0, 2, 4, 3, 0, 0, 0, …
#> $ acousticness     <dbl> 0.61100, 0.24600, 0.95200, 0.70300, 0.95000, 0.74900,…
#> $ danceability     <dbl> 0.389, 0.590, 0.663, 0.240, 0.331, 0.578, 0.703, 0.41…
#> $ duration_ms      <int> 99373, 137373, 170267, 152427, 82625, 160627, 212293,…
#> $ energy           <dbl> 0.9100, 0.7370, 0.1310, 0.3260, 0.2250, 0.0948, 0.270…
#> $ instrumentalness <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.123…
#> $ liveness         <dbl> 0.3460, 0.1510, 0.1030, 0.0985, 0.2020, 0.1070, 0.105…
#> $ loudness         <dbl> -1.828, -5.559, -13.879, -12.178, -21.150, -14.970, -…
#> $ speechiness      <dbl> 0.0525, 0.0868, 0.0362, 0.0395, 0.0456, 0.1430, 0.953…
#> $ tempo            <dbl> 166.969, 174.003, 99.488, 171.758, 140.576, 87.479, 8…
#> $ valence          <dbl> 0.8140, 0.8160, 0.3680, 0.2270, 0.3900, 0.3580, 0.533…

the data have difference scale we need to scaling data first. Scaling use in this case is Z Score (mean = 0, standar deviasi = 1)

spotify_scale <- scale(spotify_num)

we use the PCA() method to transform the dataset to find and retain the most important track feature.

pca_spotify <- PCA(X = spotify_clean,
                  scale.unit = T,
                  quali.sup = qualivar,
                  graph = F)
pca_spotify$eig

#>         eigenvalue percentage of variance cumulative percentage of variance
#> comp 1   3.6104585              32.822350                          32.82235
#> comp 2   1.7100322              15.545747                          48.36810
#> comp 3   1.1712478              10.647707                          59.01580
#> comp 4   0.9998348               9.089407                          68.10521
#> comp 5   0.8617223               7.833839                          75.93905
#> comp 6   0.7567566               6.879605                          82.81866
#> comp 7   0.6378557               5.798688                          88.61734
#> comp 8   0.4853785               4.412532                          93.02988
#> comp 9   0.3751915               3.410832                          96.44071
#> comp 10  0.2767450               2.515864                          98.95657
#> comp 11  0.1147772               1.043429                         100.00000

glimpse(spotify_clean)

#> Rows: 232,725
#> Columns: 14
#> $ genre            <fct> Movie, Movie, Movie, Movie, Movie, Movie, Movie, Movi…
#> $ popularity       <int> 0, 1, 3, 0, 4, 0, 2, 15, 0, 10, 0, 2, 4, 3, 0, 0, 0, …
#> $ acousticness     <dbl> 0.61100, 0.24600, 0.95200, 0.70300, 0.95000, 0.74900,…
#> $ danceability     <dbl> 0.389, 0.590, 0.663, 0.240, 0.331, 0.578, 0.703, 0.41…
#> $ duration_ms      <int> 99373, 137373, 170267, 152427, 82625, 160627, 212293,…
#> $ energy           <dbl> 0.9100, 0.7370, 0.1310, 0.3260, 0.2250, 0.0948, 0.270…
#> $ instrumentalness <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.123…
#> $ liveness         <dbl> 0.3460, 0.1510, 0.1030, 0.0985, 0.2020, 0.1070, 0.105…
#> $ loudness         <dbl> -1.828, -5.559, -13.879, -12.178, -21.150, -14.970, -…
#> $ mode             <fct> Major, Minor, Minor, Major, Major, Major, Major, Majo…
#> $ speechiness      <dbl> 0.0525, 0.0868, 0.0362, 0.0395, 0.0456, 0.1430, 0.953…
#> $ tempo            <dbl> 166.969, 174.003, 99.488, 171.758, 140.576, 87.479, 8…
#> $ time_signature   <fct> 4/4, 4/4, 5/4, 4/4, 4/4, 4/4, 4/4, 4/4, 4/4, 4/4, 4/4…
#> $ valence          <dbl> 0.8140, 0.8160, 0.3680, 0.2270, 0.3900, 0.3580, 0.533…

summary(pca_spotify)

#> 
#> Call:
#> PCA(X = spotify_clean, scale.unit = T, quali.sup = qualivar,  
#>      graph = F) 
#> 
#> 
#> Eigenvalues
#>                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
#> Variance               3.610   1.710   1.171   1.000   0.862   0.757   0.638
#> % of var.             32.822  15.546  10.648   9.089   7.834   6.880   5.799
#> Cumulative % of var.  32.822  48.368  59.016  68.105  75.939  82.819  88.617
#>                        Dim.8   Dim.9  Dim.10  Dim.11
#> Variance               0.485   0.375   0.277   0.115
#> % of var.              4.413   3.411   2.516   1.043
#> Cumulative % of var.  93.030  96.441  98.957 100.000
#> 
#> Individuals (the 10 first)
#>                      Dist    Dim.1    ctr   cos2    Dim.2    ctr   cos2  
#> 1                |  4.033 |  0.990  0.000  0.060 |  0.999  0.000  0.061 |
#> 2                |  3.489 |  1.210  0.000  0.120 |  0.273  0.000  0.006 |
#> 3                |  3.503 | -2.112  0.001  0.364 |  0.353  0.000  0.010 |
#> 4                |  3.875 | -1.955  0.000  0.254 | -0.187  0.000  0.002 |
#> 5                |  4.023 | -2.936  0.001  0.532 |  0.392  0.000  0.009 |
#> 6                |  3.520 | -2.261  0.001  0.413 |  0.701  0.000  0.040 |
#> 7                |  5.377 | -0.757  0.000  0.020 |  3.417  0.003  0.404 |
#> 8                |  2.867 | -1.934  0.000  0.455 |  0.073  0.000  0.001 |
#> 9                |  3.115 |  0.768  0.000  0.061 | -0.056  0.000  0.000 |
#> 10               |  2.492 |  0.730  0.000  0.086 |  0.723  0.000  0.084 |
#>                   Dim.3    ctr   cos2  
#> 1                -0.160  0.000  0.002 |
#> 2                -0.684  0.000  0.038 |
#> 3                -1.867  0.001  0.284 |
#> 4                 0.251  0.000  0.004 |
#> 5                -1.123  0.000  0.078 |
#> 6                -1.731  0.001  0.242 |
#> 7                -1.498  0.001  0.078 |
#> 8                -0.452  0.000  0.025 |
#> 9                -1.178  0.001  0.143 |
#> 10               -0.614  0.000  0.061 |
#> 
#> Variables (the 10 first)
#>                     Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3    ctr
#> popularity       |  0.449  5.588  0.202 | -0.390  8.908  0.152 |  0.108  0.989
#> acousticness     | -0.798 17.659  0.638 |  0.245  3.524  0.060 | -0.226  4.349
#> danceability     |  0.635 11.172  0.403 |  0.079  0.362  0.006 | -0.488 20.314
#> duration_ms      | -0.114  0.360  0.013 | -0.043  0.107  0.002 |  0.642 35.227
#> energy           |  0.848 19.917  0.719 |  0.127  0.939  0.016 |  0.267  6.100
#> instrumentalness | -0.611 10.356  0.374 | -0.239  3.345  0.057 |  0.076  0.488
#> liveness         |  0.056  0.088  0.003 |  0.810 38.356  0.656 |  0.275  6.438
#> loudness         |  0.887 21.812  0.788 | -0.032  0.061  0.001 |  0.166  2.354
#> speechiness      |  0.056  0.088  0.003 |  0.844 41.675  0.713 |  0.031  0.081
#> tempo            |  0.299  2.471  0.089 | -0.195  2.231  0.038 |  0.280  6.707
#>                    cos2  
#> popularity        0.012 |
#> acousticness      0.051 |
#> danceability      0.238 |
#> duration_ms       0.413 |
#> energy            0.071 |
#> instrumentalness  0.006 |
#> liveness          0.075 |
#> loudness          0.028 |
#> speechiness       0.001 |
#> tempo             0.079 |
#> 
#> Supplementary categories (the 10 first)
#>                        Dist      Dim.1     cos2   v.test      Dim.2     cos2
#> A Capella        |    2.859 |   -2.125    0.552  -12.204 |    0.192    0.004
#> Alternative      |    1.130 |    0.918    0.659   47.433 |   -0.362    0.103
#> Anime            |    1.302 |   -0.103    0.006   -5.237 |   -0.155    0.014
#> Blues            |    0.764 |    0.232    0.092   11.829 |   -0.019    0.001
#> Children's Music |    2.657 |   -0.606    0.052  -23.705 |    0.616    0.054
#> Children’s Music |    1.255 |    0.971    0.598   50.427 |   -0.444    0.125
#> Classical        |    3.779 |   -3.695    0.956 -190.944 |   -0.413    0.012
#> Comedy           |    5.077 |   -0.539    0.011  -28.528 |    4.912    0.936
#> Country          |    0.954 |    0.780    0.669   38.962 |   -0.363    0.145
#> Dance            |    1.487 |    1.314    0.781   65.741 |   -0.462    0.096
#>                    v.test      Dim.3     cos2   v.test  
#> A Capella           1.600 |   -0.654    0.052   -6.599 |
#> Alternative       -27.226 |    0.402    0.127   36.504 |
#> Anime             -11.420 |    0.350    0.072   31.138 |
#> Blues              -1.390 |    0.016    0.000    1.470 |
#> Children's Music   35.063 |   -1.744    0.431 -119.867 |
#> Children’s Music  -33.500 |    0.410    0.106   37.353 |
#> Classical         -30.990 |    0.241    0.004   21.841 |
#> Comedy            377.530 |    0.311    0.004   28.883 |
#> Country           -26.336 |   -0.099    0.011   -8.714 |
#> Dance             -33.560 |    0.036    0.001    3.206 |

we are also plotting variance of principal components to get the optimum value principal components.

plot.PCA(
  x = pca_spotify,   # objek pca        
  choix = "ind",  # individual factor map
  select = "contrib 5",   # show outlier sebanyak 5
)

From plot above we can see * 4 outlier terluar: 218541, 210710, 210094, 217130.

as.data.frame(pca_spotify$ind$coord)

fviz_contrib(pca_spotify, choice = "var", axes = 1)

• To retain at least 75% of our information, we use comp 1-comp 5. fviz_contrib: a function of factoextra to indicate the order in which variables contribute to a PC. Order Contribution is :

1. Loudness
2. Energy
3. Acousticness
4. Danceability
5. Valence
6. Instrumentalness
7. Popularity
8. Tempo
9. Duration_ms
10. Speechiness
11. Liveness

spotify_keep <- as.data.frame(pca_spotify$ind$coord[,1:5])
spotify_keep

plot.PCA(
  x = pca_spotify,   # objek pca        
  choix = "ind",  # individual factor map
  select = "contrib 5",   # menunjukkan outlier sebanyak 5
  invisible = "quali" ,
  axes = 1:2 # menghilangkan label kolom kategorik
)

* 5 outlier retiared are 218541, 210710, 210094, 217130.

# verifikasi koordinat 218541 pada plot

pca_spotify$ind$coord[218541,c("Dim.1","Dim.2")]

#>     Dim.1     Dim.2 
#> -8.794427 -1.426402

plot.PCA(x = pca_spotify,  # objek pca
         choix = "var") # variable factor map

From plot PCA above we can see : * Variables that were highly contributed to PC1 are loudness, energy, valence, and danceability. * Variables that were highly contributed to PC2 are speechiness, liveness * Pairs of variables that are positively correlated with each other:loudness-loudness,valence-energy`. * Variables acousticness possibly has a negative correlation with tempo * Variables acousticness and tempo

An alternative way to extract loading information is to use the dimdesc() function for the pca_spotify object. Save the result as pca_dimdesc. Check the loading information of the first dimension/PC by calling pca_dimdesc$Dim.1 since the first dimension/PC is the PC that summarizes the most information.

pca_dimdesc <- dimdesc(pca_spotify)
pca_dimdesc$Dim.1

#> 
#> Link between the variable and the continuous variables (R-square)
#> =================================================================================
#>                  correlation       p.value
#> loudness          0.88742278  0.000000e+00
#> energy            0.84799976  0.000000e+00
#> danceability      0.63509756  0.000000e+00
#> valence           0.61536866  0.000000e+00
#> popularity        0.44917562  0.000000e+00
#> tempo             0.29865947  0.000000e+00
#> speechiness       0.05644642 1.563996e-163
#> liveness          0.05634070 6.302335e-163
#> duration_ms      -0.11406412  0.000000e+00
#> instrumentalness -0.61147590  0.000000e+00
#> acousticness     -0.79848490  0.000000e+00
#> 
#> Link between the variable and the categorical variable (1-way anova)
#> =============================================
#>                         R2
#> genre          0.621656196
#> time_signature 0.126396390
#> mode           0.001477995
#>                                                                                              p.value
#> genre          0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000
#> time_signature 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000
#> mode           0.00000000000000000000000000000000000000000000000000000000000000000000000000007699283
#> 
#> Link between variable and the categories of the categorical variables
#> ================================================================
#>                           Estimate       p.value
#> time_signature=4/4      1.62863113  0.000000e+00
#> genre=Ska               1.30381110  0.000000e+00
#> genre=Rock              1.06496543  0.000000e+00
#> genre=Reggaeton         1.56072701  0.000000e+00
#> genre=Reggae            1.15942873  0.000000e+00
#> genre=Rap               1.38321063  0.000000e+00
#> genre=Pop               1.29971082  0.000000e+00
#> genre=Hip-Hop           1.37912286  0.000000e+00
#> genre=Dance             1.40702188  0.000000e+00
#> genre=Country           0.87354531  0.000000e+00
#> genre=Children’s Music  1.06377605  0.000000e+00
#> genre=Alternative       1.01075034  0.000000e+00
#> genre=R&B               0.75091573 2.406405e-246
#> genre=Electronic        0.66927192 9.591409e-198
#> genre=Indie             0.51456553 1.395972e-108
#> mode=Minor              0.07668023  7.699283e-77
#> genre=Soul              0.38289731  8.709763e-50
#> genre=Blues             0.32511764  2.701293e-32
#> time_signature=0/4     -0.53279568  4.850277e-03
#> genre=Anime            -0.01009634  1.633355e-07
#> genre=Folk             -0.07532830  2.644042e-18
#> genre=A Capella        -2.03206500  2.892541e-34
#> mode=Major             -0.07668023  7.699283e-77
#> genre=Children's Music -0.51250608 2.270546e-124
#> genre=Comedy           -0.44621875 2.594499e-179
#> genre=Jazz             -0.51869810 2.371174e-224
#> time_signature=5/4     -0.16593899  0.000000e+00
#> time_signature=3/4     -0.33853048  0.000000e+00
#> time_signature=1/4     -0.59136598  0.000000e+00
#> genre=World            -0.73566097  0.000000e+00
#> genre=Soundtrack       -3.44787439  0.000000e+00
#> genre=Opera            -3.38839696  0.000000e+00
#> genre=Movie            -1.37969923  0.000000e+00
#> genre=Classical        -3.60229419  0.000000e+00

The result PC1 variable positive correlation and p- value < 0.01 are : 1. loudness 88,7% 2. energy 84,7% 3. danceability 63,5% 4. valence 61,5% 5. popularity 44,9% 6. Tempo 29,8%

4 K-Mean Clustering

K- Means clustering is an iterative algorithm that tries to partition the dataset into K pre-defined distinct non overlapping subgroups (clusters) where each data point belongs to only one group. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid is at the minimum. The less variation we have within clusters, the more homegeneous (similar) the data points are within the same cluster

# function
RNGkind(sample.kind = "Rounding")
kmeansTunning <- function(data, maxK) {
  withinall <- NULL
  total_k <- NULL
  for (i in 1:maxK) {
    set.seed(4)
    temp <- kmeans(data,i)$tot.withinss
    withinall <- append(withinall, temp)
    total_k <- append(total_k,i)
  }
  plot(x = total_k, y = withinall, type = "o", xlab = "Number of Cluster", ylab = "Total within")
}

# apply function
kmeansTunning(spotify_scale, maxK = 5)

The graph above shows that k=3 isn’t a bad choice. Let’s take 3 as our optimum number of k and then apply it to the k-means clustering.

RNGkind(sample.kind = "Rounding")
set.seed(101)
# your code here
spotify_cluster <-  kmeans(x = spotify_scale, centers = 3)
spotify_cluster

#> K-means clustering with 3 clusters of sizes 10347, 51965, 170413
#> 
#> Cluster means:
#>   popularity acousticness danceability duration_ms     energy instrumentalness
#> 1 -1.1225212    1.1835743   0.04146072  0.07412107  0.3346875       -0.4851339
#> 2 -0.6517258    1.2899362  -1.01855096  0.08518122 -1.3699753        1.0002059
#> 3  0.2668908   -0.4652109   0.30807513 -0.03047521  0.3974330       -0.2755425
#>      liveness   loudness speechiness      tempo    valence
#> 1  2.58008878 -0.4063925   4.0199624 -0.6222912 -0.1471545
#> 2 -0.24937893 -1.3471891  -0.3759417 -0.3703210 -0.8972973
#> 3 -0.08061124  0.4354810  -0.1294428  0.1507078  0.2825528
#> 
#> Clustering vector:
#>     [1] 3 3 2 2 2 2 1 2 3 3 2 2 3 2 2 1 1 3 3 3 2 3 3 3 3 1 3 3 3 3 3 1 2 3 3 3
#>    [37] 3 1 2 3 2 2 2 3 1 3 3 3 3 3 2 3 1 2 1 2 2 2 2 3 2 3 2 3 3 3 3 2 3 3 3 2
#>    [73] 2 2 1 2 2 2 2 1 3 3 1 2 3 3 3 2 3 3 3 3 3 1 3 2 1 3 2 3 3 2 2 2 3 3 2 3
#>   [109] 2 2 3 3 2 2 1 3 3 2 3 1 2 1 3 3 2 3 3 3 2 2 2 2 2 2 2 3 3 3 3 3 3 3 2 3
#>   [145] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [181] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [217] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [253] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 2 2 2 2 2 2 2 2
#>   [289] 1 3 2 2 3 1 2 2 3 2 3 3 2 2 2 2 1 3 1 2 1 3 2 2 2 3 2 2 2 2 2 2 3 3 2 3
#>   [325] 2 2 1 3 1 3 3 3 1 3 2 2 1 2 2 3 2 2 2 3 2 3 2 2 3 1 1 2 3 2 1 2 3 3 2 3
#>   [361] 3 2 3 2 2 3 3 1 2 1 3 3 2 2 3 3 2 2 1 1 2 3 1 3 2 3 3 2 3 3 3 3 2 3 3 1
#>   [397] 2 3 3 2 2 2 2 2 2 2 3 3 3 2 2 3 2 2 3 2 3 3 2 2 2 2 3 3 2 3 2 2 1 3 2 3
#>   [433] 3 3 3 3 1 3 3 3 2 3 3 2 2 3 3 3 3 2 1 3 3 2 2 3 2 2 2 3 3 2 2 3 3 1 3 3
#>   [469] 3 2 1 2 3 2 2 3 2 2 3 3 2 3 3 3 3 2 3 2 3 2 3 3 3 2 1 1 3 2 3 3 3 3 3 3
#>   [505] 2 3 3 2 2 2 2 2 3 3 3 2 2 3 2 3 2 2 3 3 2 1 2 2 2 2 2 2 3 2 2 3 3 3 3 2
#>   [541] 1 2 3 3 2 3 3 3 3 2 1 3 3 2 3 3 2 2 3 3 2 3 3 2 3 2 2 2 3 2 2 2 3 2 2 2
#>   [577] 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2 2 2 2 2 2 3 2 3 2 2 2 2 2 2
#>   [613] 2 2 2 2 2 2 2 3 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 3 2 3 3
#>   [649] 3 2 3 3 3 2 2 2 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [685] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [721] 3 3 2 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
#>   [757] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [793] 3 3 2 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>   [829] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2
#>   [865] 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [901] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
#>   [937] 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>   [973] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3
#>  [1009] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3
#>  [1045] 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#>  [1081] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1117] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3
#>  [1153] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3
#>  [1189] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1225] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3
#>  [1261] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1297] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1333] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3
#>  [1369] 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
#>  [1405] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1441] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1477] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1513] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1549] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#>  [1585] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1621] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
#>  [1657] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1693] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1729] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
#>  [1765] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#>  [1801] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>  [1837] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1873] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [1909] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3
#>  [1945] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 2
#>  [1981] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
#>  [2017] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 2 3 3 3 3 3
#>  [2053] 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 2
#>  [2089] 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#>  [2125] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 2 3 3
#>  [2161] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 2
#>  [2197] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 2 3 3 2 2 2 3 3 3 3 3 3 3 3 2 2 3
#>  [2233] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
#>  [2269] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
#>  [2305] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3
#>  [2341] 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2377] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3
#>  [2413] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2449] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#>  [2485] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2521] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
#>  [2557] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2593] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2629] 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2665] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2
#>  [2701] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2737] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2773] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3
#>  [2809] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2845] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2881] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2917] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [2953] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 2 3 3 3
#>  [2989] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3025] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#>  [3061] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
#>  [3097] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3133] 2 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3
#>  [3169] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#>  [3205] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3241] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3277] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3313] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
#>  [3349] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>  [3385] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>  [3421] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3457] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3493] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
#>  [3529] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3565] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3601] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#>  [3637] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3
#>  [3673] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2
#>  [3709] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3745] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3781] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3
#>  [3817] 2 3 3 3 3 3 3 2 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3853] 3 3 3 1 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3
#>  [3889] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2
#>  [3925] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#>  [3961] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [3997] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4033] 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
#>  [4069] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4105] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>  [4141] 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 2 3 3
#>  [4177] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3
#>  [4213] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
#>  [4249] 3 2 2 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4285] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 2 3
#>  [4321] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 2 3 3
#>  [4357] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4393] 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2
#>  [4429] 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4465] 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2
#>  [4501] 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4537] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
#>  [4573] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3
#>  [4609] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3
#>  [4645] 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3
#>  [4681] 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4717] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4753] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>  [4789] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>  [4825] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4861] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4897] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4933] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [4969] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5005] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
#>  [5041] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5077] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5113] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
#>  [5185] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5221] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3
#>  [5257] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5293] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5329] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5365] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#>  [5401] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3
#>  [5437] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
#>  [5473] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5509] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5545] 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#>  [5581] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5617] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5653] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#>  [5689] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5725] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [5761] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
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#> [98209] 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
#> [98245] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#> [98281] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98317] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
#> [98353] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
#> [98389] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#> [98425] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3
#> [98461] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98497] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98533] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3
#> [98569] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98605] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98641] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98677] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98713] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98749] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
#> [98785] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
#> [98821] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98857] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3
#> [98893] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#> [98929] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [98965] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99001] 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99037] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3
#> [99073] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
#> [99109] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#> [99145] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 1 3 3
#> [99181] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99217] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#> [99253] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
#> [99289] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99325] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#> [99361] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99397] 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99433] 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
#> [99469] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3
#> [99505] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
#> [99541] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99577] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
#> [99613] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
#> [99649] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
#> [99685] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99721] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99757] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99793] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99829] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99865] 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99901] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99937] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [99973] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#>  [ reached getOption("max.print") -- omitted 132726 entries ]
#> 
#> Within cluster sum of squares by cluster:
#> [1]  114723.6  491560.0 1055292.7
#>  (between_SS / total_SS =  35.1 %)
#> 
#> Available components:
#> 
#> [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
#> [6] "betweenss"    "size"         "iter"         "ifault"

for each clusters, there’s a total of * 10347 songs in cluster 1 * 51965 songs in cluster 2 * 170413 songs in cluster 3

BSS/TSS = 35.1%

data.frame(spotify_cluster$centers)

spotifyclus <- as.data.frame(spotify_cluster$cluster)
spotifyclus

spotify_clean$cluster <- spotify_cluster$cluster
spotify_clean

library(ggiraphExtra)

ggRadar(data=spotify_clean, 
        aes(colour=cluster), 
        interactive=TRUE)

Audio feature characteristics in each cluster * cluster 1 : songs that were identified with high in speechiness, liveness, acousticness * cluster 2 : songs that were identified with high in acousticness instrumentalness, loudness * cluster 3 : songs that were identified with high in loudness, energy, dancebility, valence

5 Evaluating the Model

To check whether the clusters I created are good enough, I can see the value in the cluster. The goodness of the clustering results can be seen from the following 4 values:

1. Within Sum of Squares ($withinss): Sum of the distance squared from each observation to the centroid of each cluster.

spotify_cluster$withinss

#> [1]  114723.6  491560.0 1055292.7

2. Between Sum of Squares ($betweenss): Sum of the weighted square distances of each centroid to the global average. Weighted based on the number of observations in the cluster.

spotify_cluster$betweenss

#> [1] 898387.7

3. Total Sum of Squares ($totss) :Sum of the distances squared from each observation to the global average.

spotify_cluster$totss

#> [1] 2559964

4. Total Within Sum of Square ($tot.withinss): Number of withinss for each cluster.

spotify_cluster$tot.withinss

#> [1] 1661576

Result

spotify_cluster$betweenss/spotify_cluster$tot.withinss

#> [1] 0.540684

From the evaluation of model, the betweenss/tot.withinss value is 0.54 which is somewhat close to 1 so it can be said that the model is decent.

6 Summary

• cluster 1 : songs that were identified with high in speechiness, liveness, acousticness.This means that these tracks are live recorded songs which are mainly instrumental and high in energy and tempo. Live recordings also tend to be longer in duration and have less lyrics
• cluster 2 : songs that were identified with high in acousticness instrumentalness, loudness.This means that these tracks are live recorded songs which are mainly instrumental and high in energy and tempo. Live recordings also tend to be longer in duration and have less lyrics
• cluster 3 : songs that were identified with high in loudness, energy, dancebility, valence this means that tracks which are dance numbers, have a happy vibe and like a rock