Description

This dataset includes data of 50.000 users from Spotify. The data includes user’s general information, their activity and bevaviour towards ads. The source of the data is the following link: Spotify User Behavior And Pattern

Reason of choice and what it could solve

This dataset is helpful for Spotify to deduce a strategy for the company as they can notice trends and habits among users. The result, for example, could be:

  • optimal pricing
  • optimazation of suggetions
  • adjust the marketing strategy

Variables

1.User Demographics

  • user_id: A unique numerical identifier for each user.
  • country: The user’s location.
  • age: The age of the user, ranging from 16 to 60 years (Mean: ~38).

2.Subscription & Account Details

  • signup_date: The date the user first registered (format: YYYY-MM-DD).

  • subscription_type: The tier of the user’s account. Categories include:

    1. Free (most common)
    2. Premium Individual
    3. Student, Premium Family, and Premium Duo.

  • subscription_status: Whether the account is currently Active or Inactive.

  • ad_interaction: Indicates if the user interacts with advertisements (Yes/No).

  • ad_conversion_to_subscription: Indicates if an ad interaction specifically led the user to upgrade to a premium plan (Yes/No).

3. Engagement & Behavior Metrics

  • avg_listening_hours_per_week: A continuous numeric variable representing weekly listening.
  • avg_skips_per_day: The average number of times a user skips a track daily.
  • playlists_created: The total number of playlists the user has built.
  • primary_device: The hardware most used for streaming. months_inactive: How many months the user has been away from the platform.
  • inactive_3_months_flag: A binary indicator (0 or 1) where 1 signifies the user has been inactive for at least 3 months.

4. Preferences & Feedback

  • favorite_genre: The user’s top music genre.
  • music_suggestion_rating_1_to_5: An ordinal scale (1 to 5) reflecting how satisfied the user is with Spotify’s recommendations.
  • most_liked_feature: The current feature the user enjoys most, such as Lyrics, Radio, Offline Mode, or Discover Weekly.
  • desired_future_feature: What the user wants to see next, such as Mood-based Auto Playlists, Concert Alerts, or Social Listening.

Load data

# Loading data from the CSV file
# Ensure the file is in the same folder as this .Rmd file
df <- read.csv("spotify_user_behavior_realistic_50000_rows.csv")

Satistics

# Function for the Mode (as R doesn't have a built-in one)
get_mode <- function(v) {
   uniqv <- unique(v)
   uniqv[which.max(tabulate(match(v, uniqv)))]
}

# Calculations for Age
mean_age   <- mean(df$age, na.rm = TRUE)
median_age <- median(df$age, na.rm = TRUE)
mode_age   <- get_mode(df$age)
sd_age     <- sd(df$age, na.rm = TRUE)
var_age    <- var(df$age, na.rm = TRUE)
range_age  <- max(df$age, na.rm = TRUE) - min(df$age, na.rm = TRUE)

# Presenting the results
print(paste("Mean Age:", round(mean_age, 2)))
## [1] "Mean Age: 38.01"
print(paste("Median Age:", median_age))
## [1] "Median Age: 38"
print(paste("Mode Age:", mode_age))
## [1] "Mode Age: 47"
print(paste("Standard Deviation (Age):", round(sd_age, 2)))
## [1] "Standard Deviation (Age): 12.98"
print(paste("Variance (Age):", round(var_age, 2)))
## [1] "Variance (Age): 168.61"
print(paste("Range (Age):", range_age))
## [1] "Range (Age): 44"

Correlation Analysis

# Calculating the Pearson Correlation Coefficient
cor_value <- cor(df$age, df$avg_listening_hours_per_week, use = "complete.obs")
print(paste("Correlation Coefficient (Age vs Hours):", round(cor_value, 4)))
## [1] "Correlation Coefficient (Age vs Hours): -0.0023"

Data Visualization

# Histogram
ggplot(df, aes(x = age)) +
  geom_histogram(binwidth = 2, fill = "#1DB954", color = "white") +
  labs(title = "Distribution of User Age", x = "Age", y = "Frequency") +
  theme_minimal()

# Boxplot
ggplot(df, aes(x = subscription_type, y = avg_listening_hours_per_week, fill = subscription_type)) +
  geom_boxplot() +
  labs(title = "Listening Hours by Subscription Type", x = "Subscription", y = "Hours/Week") +
  theme_minimal()

# Scatterplot
ggplot(df, aes(x = age, y = avg_listening_hours_per_week)) +
  geom_point(alpha = 0.1, color = "blue") +
  geom_smooth(method = "lm", color = "red") +
  labs(title = "Relationship: Age vs. Weekly Hours", x = "Age", y = "Weekly Hours") +
  theme_light()
## `geom_smooth()` using formula = 'y ~ x'

# Bar Chart
ggplot(df, aes(x = favorite_genre, fill = favorite_genre)) +
  geom_bar() +
  coord_flip() +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(title = "Most Popular Music Genres", x = "Genre", y = "Number of Users")

Session Information

sessionInfo()
## R version 4.5.3 (2026-03-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: Europe/Athens
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_4.0.2 dplyr_1.2.0  
## 
## loaded via a namespace (and not attached):
##  [1] Matrix_1.7-4       gtable_0.3.6       jsonlite_2.0.0     compiler_4.5.3    
##  [5] tidyselect_1.2.1   jquerylib_0.1.4    splines_4.5.3      scales_1.4.0      
##  [9] yaml_2.3.12        fastmap_1.2.0      lattice_0.22-9     R6_2.6.1          
## [13] labeling_0.4.3     generics_0.1.4     knitr_1.51         tibble_3.3.1      
## [17] bslib_0.10.0       pillar_1.11.1      RColorBrewer_1.1-3 rlang_1.1.7       
## [21] cachem_1.1.0       xfun_0.57          sass_0.4.10        S7_0.2.1          
## [25] cli_3.6.5          withr_3.0.2        magrittr_2.0.4     mgcv_1.9-4        
## [29] digest_0.6.39      grid_4.5.3         lifecycle_1.0.5    nlme_3.1-168      
## [33] vctrs_0.7.2        evaluate_1.0.5     glue_1.8.0         farver_2.1.2      
## [37] rmarkdown_2.30     tools_4.5.3        pkgconfig_2.0.3    htmltools_0.5.9