Last updated: 2020-07-02

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File Version Author Date Message
Rmd 6c85c34 John Blischak 2020-07-02 Add spotify analysis

This analysis attempts to classify songs into their correct musical genre using audio features. It is inspired by the original analysis by Kaylin Pavlik (@kaylinquest) in her 2019 blog post Understanding + classifying genres using Spotify audio features.

spotify <- read.csv("data/spotify.csv", stringsAsFactors = FALSE)
dim(spotify)
[1] 32833    15
head(spotify)
  genre danceability energy key loudness mode speechiness acousticness
1   pop        0.748  0.916   6   -2.634    1      0.0583       0.1020
2   pop        0.726  0.815  11   -4.969    1      0.0373       0.0724
3   pop        0.675  0.931   1   -3.432    0      0.0742       0.0794
4   pop        0.718  0.930   7   -3.778    1      0.1020       0.0287
5   pop        0.650  0.833   1   -4.672    1      0.0359       0.0803
6   pop        0.675  0.919   8   -5.385    1      0.1270       0.0799
  instrumentalness liveness valence   tempo duration_ms           artist
1         0.00e+00   0.0653   0.518 122.036      194754       Ed Sheeran
2         4.21e-03   0.3570   0.693  99.972      162600         Maroon 5
3         2.33e-05   0.1100   0.613 124.008      176616     Zara Larsson
4         9.43e-06   0.2040   0.277 121.956      169093 The Chainsmokers
5         0.00e+00   0.0833   0.725 123.976      189052    Lewis Capaldi
6         0.00e+00   0.1430   0.585 124.982      163049       Ed Sheeran
                               song
1 I Don't Care (with Justin Bieber)
2                          Memories
3                      All the Time
4                     Call You Mine
5                 Someone You Loved
6   Beautiful People (feat. Khalid)
table(spotify[, 1])

  edm latin   pop   r&b   rap  rock 
 6043  5155  5507  5431  5746  4951 
spotify <- spotify[, 1:13]

Split the data into training and testing sets.

numTrainingSamples <- 3 / 4 * nrow(spotify)
trainingSet <- sample(seq_len(nrow(spotify)), size = numTrainingSamples)
spotifyTraining <- spotify[trainingSet, ]
spotifyTesting <- spotify[-trainingSet, ]

Build classification model with decision tree from the rpart package.

library(rpart)
model <- rpart(genre ~ ., data = spotifyTraining)
plot(model)
text(model)

Calculate prediction accuracy of the model on the training and testing sets.

predictTraining <- predict(model, type = "class")
(accuracyTraining <- mean(spotifyTraining[, 1] == predictTraining))
[1] 0.3920159
predictTesting <- predict(model, newdata = spotifyTesting[, -1], type = "class")
(accuracyTesting <- mean(spotifyTesting[, 1] == predictTesting))
[1] 0.3857961

Evaluate prediction performance using a confusion matrix.

table(predicted = predictTesting, observed = spotifyTesting[, 1])
         observed
predicted edm latin pop r&b rap rock
    edm   621   112 171  56  52   60
    latin 178   469 419 270 190  120
    pop     0     0   0   0   0    0
    r&b    55   134  93 284  91  114
    rap   309   396 305 487 984  168
    rock  382   143 368 261 108  809

How does the model compare to random guessing?

predictRandom <- sample(unique(spotifyTesting[, 1]),
                        size = nrow(spotifyTesting),
                        replace = TRUE,
                        prob = table(spotifyTesting[, 1]))
(accuracyRandom <- mean(spotifyTesting[, 1] == predictRandom))
[1] 0.1678645

sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/atlas-base/atlas/libblas.so.3.0
LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] rpart_4.1-15    workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6    rprojroot_1.3-2 digest_0.6.25   later_1.1.0.1  
 [5] R6_2.4.1        backports_1.1.8 git2r_0.27.1    magrittr_1.5   
 [9] evaluate_0.14   stringi_1.4.6   rlang_0.4.6     fs_1.4.2       
[13] promises_1.1.1  whisker_0.4     rmarkdown_2.3   tools_4.0.0    
[17] stringr_1.4.0   glue_1.4.1      httpuv_1.5.4    xfun_0.15      
[21] yaml_2.2.1      compiler_4.0.0  htmltools_0.5.0 knitr_1.29