The analysis of the 10,000 Apple Music Tracks dataset aims to explore various attributes and trends within the collection of songs. This dataset contains information such as artist names, track names, release dates, genres, and more. Analyzing this dataset can provide valuable insights into the music industry, popular genres, artist popularity, and consumer preferences.
Throughout this analysis, we’ll utilize tools like R, ggplot2 for visualization, and statistical modeling techniques to uncover patterns and relationships within the dataset.
February 12, 2024
Introduction
Setup
To conduct our analysis, we’ll be using the R programming language along with essential packages for data manipulation and visualization. Here are the key components: ggplot2 for creating visualizations to illustrate trends and patterns in the dataset. dplyr for data manipulation tasks such as filtering, summarizing, and arranging data. stats package for statistical analysis and modeling.
Loading and Preparing Data
Before diving into the analysis, we need to load the dataset and perform necessary data preparation steps such as cleaning and organizing the data.
## 'data.frame': 10000 obs. of 24 variables: ## $ artistId : int 46087 20044 486597 156987 46087 20044 889327 4488522 138226712 20044 ... ## $ artistName : chr "Erick Sermon" "Madonna" "Journey" "Jason Mraz" ... ## $ collectionCensoredName: chr "Music" "Music" "Greatest Hits (2024 Remaster)" "We Sing. We Dance. We Steal Things" ... ## $ collectionId : int 298321651 80815197 169003304 277635758 298429528 329064696 155658405 545398133 486040153 329043011 ... ## $ collectionName : chr "Music" "Music" "Greatest Hits (2024 Remaster)" "We Sing. We Dance. We Steal Things" ... ## $ collectionPrice : num 9.99 9.99 10.99 11.99 9.99 ... ## $ contentAdvisoryRating : chr "Explicit" "" "" "" ... ## $ country : chr "USA" "USA" "USA" "USA" ... ## $ currency : chr "USD" "USD" "USD" "USD" ... ## $ discCount : int 1 1 1 1 1 1 1 1 1 1 ... ## $ discNumber : int 1 1 1 1 1 1 1 1 1 1 ... ## $ isStreamable : chr "True" "True" "True" "True" ... ## $ kind : chr "song" "song" "song" "song" ... ## $ previewUrl : chr "https://audio-ssl.itunes.apple.com/itunes-assets/AudioPreview125/v4/64/61/b5/6461b58b-c65e-a48b-30fe-1c73224277"| __truncated__ "https://audio-ssl.itunes.apple.com/itunes-assets/AudioPreview115/v4/f7/3a/72/f73a728e-5e89-8c8d-69f2-578e6e2d18"| __truncated__ "https://audio-ssl.itunes.apple.com/itunes-assets/AudioPreview126/v4/5c/72/97/5c72974f-6022-f760-ad82-35964fb636"| __truncated__ "https://audio-ssl.itunes.apple.com/itunes-assets/AudioPreview116/v4/31/eb/0d/31eb0dab-85d5-84db-009c-14b17db984"| __truncated__ ... ## $ primaryGenreName : chr "Hip-Hop/Rap" "Pop" "Rock" "Pop" ... ## $ releaseDate : chr "2001-08-27T12:00:00Z" "2000-08-21T07:00:00Z" "1981-06-03T07:00:00Z" "2008-02-12T08:00:00Z" ... ## $ trackCensoredName : chr "Music (feat. Marvin Gaye)" "Music" "Don't Stop Believin' (2024 Remaster)" "I'm Yours" ... ## $ trackCount : int 16 10 16 12 16 68 13 15 11 37 ... ## $ trackExplicitness : chr "explicit" "notExplicit" "notExplicit" "notExplicit" ... ## $ trackId : int 298321904 80815173 169003415 277635828 298429596 329064769 155658510 545398139 486040194 329043268 ... ## $ trackName : chr "Music" "Music" "Don't Stop Believin' (2024 Remaster)" "I'm Yours" ... ## $ trackNumber : int 4 1 2 2 4 59 3 4 2 2 ... ## $ trackPrice : num 1.29 1.29 1.29 1.29 1.29 1.99 1.29 1.29 1.29 1.29 ... ## $ trackTimeMillis : int 223133 225973 250835 242947 223133 286687 218093 242721 277040 225813 ...
Data Cleaning Steps
## artistId artistName collectionCensoredName ## Min. :1.196e+04 Length:9919 Length:9919 ## 1st Qu.:4.689e+05 Class :character Class :character ## Median :6.766e+06 Mode :character Mode :character ## Mean :1.688e+08 ## 3rd Qu.:2.756e+08 ## Max. :1.669e+09 ## collectionId collectionName collectionPrice contentAdvisoryRating ## Min. :9.529e+05 Length:9919 Min. : -1.00 Length:9919 ## 1st Qu.:2.598e+08 Class :character 1st Qu.: 9.99 Class :character ## Median :4.201e+08 Mode :character Median : 10.99 Mode :character ## Mean :5.837e+08 Mean : 11.05 ## 3rd Qu.:9.096e+08 3rd Qu.: 12.99 ## Max. :1.728e+09 Max. :149.99 ## country currency discCount discNumber ## Length:9919 Length:9919 Min. : 1.000 Min. : 1.000 ## Class :character Class :character 1st Qu.: 1.000 1st Qu.: 1.000 ## Mode :character Mode :character Median : 1.000 Median : 1.000 ## Mean : 1.094 Mean : 1.041 ## 3rd Qu.: 1.000 3rd Qu.: 1.000 ## Max. :28.000 Max. :14.000 ## isStreamable kind previewUrl primaryGenreName ## Length:9919 Length:9919 Length:9919 Length:9919 ## Class :character Class :character Class :character Class :character ## Mode :character Mode :character Mode :character Mode :character ## ## ## ## releaseDate trackCensoredName trackCount trackExplicitness ## Length:9919 Length:9919 Min. : 1.00 Length:9919 ## Class :character Class :character 1st Qu.: 12.00 Class :character ## Mode :character Mode :character Median : 14.00 Mode :character ## Mean : 14.66 ## 3rd Qu.: 17.00 ## Max. :100.00 ## trackId trackName trackNumber trackPrice ## Min. :9.526e+05 Length:9919 Min. : 1.000 Min. :-1.000 ## 1st Qu.:2.598e+08 Class :character 1st Qu.: 2.000 1st Qu.: 1.290 ## Median :4.201e+08 Mode :character Median : 6.000 Median : 1.290 ## Mean :5.837e+08 Mean : 6.878 Mean : 1.196 ## 3rd Qu.:9.096e+08 3rd Qu.:10.000 3rd Qu.: 1.290 ## Max. :1.728e+09 Max. :75.000 Max. : 1.990 ## trackTimeMillis ## Min. : 8192 ## 1st Qu.:197728 ## Median :225328 ## Mean :232864 ## 3rd Qu.:258867 ## Max. :943529
Exploratory Data Analysis
Let’s begin by exploring the dataset to get a better understanding of its structure and contents.
## ## Adult Alternative Adult Contemporary Afrobeats Alternative ## 1 3 1 1107 ## Blues CCM Children's Music Christian ## 3 3 45 174 ## Christmas Christmas: Classic Christmas: Country Christmas: Pop ## 1 9 6 80 ## Christmas: R&B Christmas: Rock Classical Classical Crossover ## 1 1 67 1 ## Comedy Country Dance Dirty South ## 13 1365 155 3 ## Easy Listening Electronic Fitness & Workout Folk ## 11 62 14 5 ## Hard Rock Hip-Hop Hip-Hop/Rap Holiday ## 545 17 1012 75 ## Indie Rock Jazz Latin Metal ## 3 7 12 111 ## Música tropical Musicals New Wave Outlaw Country ## 24 76 5 1 ## Pop Pop Latino Pop/Rock Psychedelic ## 2185 15 2 2 ## Punk R&B/Soul Rap Reggae ## 6 444 29 13 ## Rock Rock y Alternativo Singer/Songwriter Soft Rock ## 1325 2 156 16 ## Soundtrack Southern Rock Traditional Country Urbano latino ## 688 1 1 6 ## Video Game Vocal Vocal Pop Worldwide ## 1 2 1 5
Visualizing Trends
Visualizing trends in the dataset can provide insights into popular genres, artist distribution, and other patterns.
Analyzing Relationships
We can analyze relationships between variables such as track duration, release date, and genre popularity.
Apply Statistical Procedures
Hypothesis Testing
Let’s perform a hypothesis test to compare the means of track durations between different primary genres. We’ll use ANOVA (Analysis of Variance) to test whether there are significant differences in mean track durations among the genres.
## Df Sum Sq Mean Sq F value Pr(>F) ## primaryGenreName 55 5.295e+12 9.626e+10 25.66 <2e-16 *** ## Residuals 9863 3.700e+13 3.751e+09 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Regression Modeling
Next, let’s fit a regression model to examine the relationship between track duration and release date. We’ll use linear regression for this analysis.
## ## Call: ## lm(formula = trackTimeMillis ~ as.Date(releaseDate), data = songs_data) ## ## Residuals: ## Min 1Q Median 3Q Max ## -223399 -34555 -6962 26098 713247 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.385e+05 1.603e+03 148.849 < 2e-16 *** ## as.Date(releaseDate) -4.551e-01 1.173e-01 -3.879 0.000105 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 65250 on 9917 degrees of freedom ## Multiple R-squared: 0.001515, Adjusted R-squared: 0.001415 ## F-statistic: 15.05 on 1 and 9917 DF, p-value: 0.0001054
Correlation Analysis
We can also calculate correlation coefficients to quantify the strength and direction of relationships between variables. Let’s calculate the correlation coefficient between track duration and popularity.
## Correlation between track duration and track price: -0.0524848
Conclusion
The analysis of the 10,000 Apple Music Tracks dataset provides valuable insights into various aspects of the music industry. From exploring genre distribution to analyzing relationships between track attributes, this analysis offers a comprehensive understanding of the dataset.
The statistical procedures applied, including hypothesis testing, regression modeling, and correlation analysis, further enhance our understanding of the dataset and provide insights into relationships between variables.
Further exploration and analysis can be conducted to delve deeper into specific aspects of the dataset and uncover more insights.