Part 2 Deep Dive Analysis

Your Project Assignment 1: First Contact with Your Dataset Using Arrow

Assignment Overview

This week you’ll apply the READY + SCAN frameworks to your own dataset using Arrow for efficient big data exploration. You’ll become a “data detective” investigating your dataset systematically.

Learning Objectives

By completing this assignment, you will: - Apply the READY framework to plan your data investigation - Use the SCAN framework to systematically explore your dataset - Practice using Arrow for memory-efficient data loading - Document your initial findings and develop investigation questions

Part 1: Data Setup and Loading

Step 1: Extract and Load Your Data

Use the appropriate code pattern below based on your data format:

LOAD LIBRARIES

# Function to check and install required packages
required_packages <- c("tidyverse", "arrow", "glue", "janitor")

# Install missing packages
for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load libraries
lapply(required_packages, library, character.only = TRUE)

Warning: package 'tidyverse' was built under R version 4.3.3

Warning: package 'ggplot2' was built under R version 4.3.3

Warning: package 'tibble' was built under R version 4.3.3

Warning: package 'readr' was built under R version 4.3.3

Warning: package 'dplyr' was built under R version 4.3.3

Warning: package 'stringr' was built under R version 4.3.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Warning: package 'arrow' was built under R version 4.3.3

Attaching package: 'arrow'

The following object is masked from 'package:lubridate':

    duration

The following object is masked from 'package:utils':

    timestamp

Warning: package 'janitor' was built under R version 4.3.3

Attaching package: 'janitor'

The following objects are masked from 'package:stats':

    chisq.test, fisher.test

[[1]]
 [1] "lubridate" "forcats"   "stringr"   "dplyr"     "purrr"     "readr"    
 [7] "tidyr"     "tibble"    "ggplot2"   "tidyverse" "stats"     "graphics" 
[13] "grDevices" "utils"     "datasets"  "methods"   "base"     

[[2]]
 [1] "arrow"     "lubridate" "forcats"   "stringr"   "dplyr"     "purrr"    
 [7] "readr"     "tidyr"     "tibble"    "ggplot2"   "tidyverse" "stats"    
[13] "graphics"  "grDevices" "utils"     "datasets"  "methods"   "base"     

[[3]]
 [1] "glue"      "arrow"     "lubridate" "forcats"   "stringr"   "dplyr"    
 [7] "purrr"     "readr"     "tidyr"     "tibble"    "ggplot2"   "tidyverse"
[13] "stats"     "graphics"  "grDevices" "utils"     "datasets"  "methods"  
[19] "base"     

[[4]]
 [1] "janitor"   "glue"      "arrow"     "lubridate" "forcats"   "stringr"  
 [7] "dplyr"     "purrr"     "readr"     "tidyr"     "tibble"    "ggplot2"  
[13] "tidyverse" "stats"     "graphics"  "grDevices" "utils"     "datasets" 
[19] "methods"   "base"     

For ZIP files containing CSV(s):

# Set up and extract your ZIP file
zip_path <- "C:/Classes/DSC 406/data/SpotifyData.zip"  # UPDATE THIS PATH
outdir <- file.path(dirname(zip_path), "tracks_features")
dir.create(outdir, showWarnings = FALSE)
unzip(zip_path, exdir = outdir, overwrite = TRUE)

# Get list of CSV files
csv_files <- list.files(outdir, pattern = "\\.csv$", full.names = TRUE)
names(csv_files) <- tools::file_path_sans_ext(basename(csv_files))

# Open with Arrow - specify the main file you want to work with
my_dataset <- open_dataset(csv_files[1], format = "csv")  # Adjust [1] as needed

# Check memory usage
glue("Memory used by Arrow object: {format(object.size(my_dataset), units = 'KB')}")

Memory used by Arrow object: 0.5 Kb

Part 2: READY Framework Analysis

Work through each component of READY with your dataset:

R - Representative Data

Document your thoughts as comments:

What is the scope of your data? Spotify tracks with audio features and metadata

Time period covered: Multiple years based on release_date and year columns

Geographic coverage: Global, though skewed toward regions with higher Spotify usage

Population represented: All tracks in the dataset; may include curated playlists, albums, or user interactions

Potential biases or limitations:

• May overrepresent popular or trending tracks.

• Regional differences in Spotify usage could skew results.

• Missing metadata (e.g., genre or release year) can limit analysis depth.

• If user-generated, it may reflect personal preferences rather than overall trends.

Example questions to consider:

• Do we have complete coverage of all tracks, or only those in certain playlists?

• Are all tracks from certain playlists or global coverage?

• Are audio features consistently recorded?

• Are there temporal patterns in track releases?

• Is the data periodically updated, or is it a one-time snapshot?

• Are independent or less-streamed artists underrepresented?

E - Executive Driven Questions

Who would care about insights from your data?

Primary stakeholders: Key business/research questions they might ask: What decisions could this data inform?

Examples: - If this is sales data: “How can we optimize our sales strategy?” - If this is health data: “What patterns affect patient outcomes?” - If this is social media data: “How can we improve engagement?”

Your stakeholder questions:

1. Which audio features (like energy, danceability, or tempo) most influence a track’s popularity?

2. How do track characteristics differ across albums, artists, or years?

3. Which track features are most consistent in long-running playlists or collections?

A - Analytical Framework

Your exploration strategy:

Phase 1: Data Quality Assessment - Check for missing values - Identify data types and consistency - Look for outliers or anomalies

Phase 2: Descriptive Analysis - What are the key variables? - What’s the distribution of important metrics? - What time patterns exist?

Phase 3: Pattern Investigation - What relationships might exist between variables? - Are there seasonal or temporal patterns? - What groupings or segments emerge?

Your specific analytical approach:

1. Perform exploratory data analysis (EDA) to summarize distributions and detect missing or inconsistent values.
2. Use correlation and regression analysis to find relationships between song features and popularity.
3. Apply clustering to identify natural groupings of songs or genres with similar characteristics.

D - Data Best Practices

Quality checks to perform:

Missing data assessment:

Data type verification: Are numeric columns actually numeric? Are dates properly formatted? Are categorical variables consistent?

Your quality concerns:

1. Some tracks may have incomplete metadata (e.g., missing genre or release date).
2. Popularity metrics might change over time, affecting consistency.
3. Duplicate entries or remastered versions could distort feature averages and trend patterns.

Y - Your Insights

Initial hypotheses about what you might find:

Based on your domain knowledge, what patterns do you expect? What would surprise you? What would be most valuable to discover?

Your predictions:

1. Songs with higher danceability and energy scores will tend to be more popular.
2. Longer tracks may be instrumental or live recordings
3. Certain albums or artists will show consistent audio feature patterns

Part 3: Data Quality Assessment Summary

S -Stakeholders (Revisited)

# Convert Arrow dataset to a regular data frame for analysis
tracks_features_df <- my_dataset %>% collect()

# View dataset structure summary
glue("✅ The dataset contains {nrow(tracks_features_df)} rows and {ncol(tracks_features_df)} columns.")

✅ The dataset contains 1204025 rows and 24 columns.

# Check for missing values across all columns
missing_summary <- tracks_features_df %>%
  summarise(across(everything(), ~sum(is.na(.)))) %>%
  pivot_longer(cols = everything(), names_to = "column", values_to = "missing_count") %>%
  mutate(missing_percent = round(missing_count / nrow(tracks_features_df) * 100, 2)) %>%
  arrange(desc(missing_count))

# Glimpse the missing value summary
glimpse(missing_summary)

Rows: 24
Columns: 3
$ column          <chr> "id", "name", "album", "album_id", "artists", "artist_…
$ missing_count   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ missing_percent <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …

After examining the data structure, who else might be interested?

Interested parties: Spotify analysts, artists, music marketers, researchers

Questions they may have:

• Which features define popular collections?

• How do albums/artists differ in audio metrics?

• Are certain audio patterns consistent across time or playlists

Data quality concerns:

• Outliers in track duration or numeric features

• Duplicate or composite entries in album/artist fields

C - Columns and Coverage

Create a summary table of your variables:

# Create a summary table of all variables
variable_summary <- tracks_features_df %>%
  summarise(across(everything(), list(
    type = ~class(.)[1],
    missing = ~sum(is.na(.)),
    unique = ~n_distinct(.)
  ), .names = "{.col}_{.fn}"))

# Reshape manually to avoid underscore conflicts
variable_summary_long <- tibble(
  variable = names(tracks_features_df),
  type = sapply(tracks_features_df, function(x) class(x)[1]),
  missing = sapply(tracks_features_df, function(x) sum(is.na(x))),
  unique = sapply(tracks_features_df, function(x) n_distinct(x))
)

# View the summary table
variable_summary_long

# A tibble: 24 × 4
   variable     type      missing  unique
   <chr>        <chr>       <int>   <int>
 1 id           character       0 1204025
 2 name         character       0  850944
 3 album        character       0  106162
 4 album_id     character       0  118382
 5 artists      character       0  165365
 6 artist_ids   character       0  166423
 7 track_number integer         0      50
 8 disc_number  integer         0      13
 9 explicit     character       0       2
10 danceability numeric         0    1362
# ℹ 14 more rows

A - Aggregates: Overall Picture

# Get comprehensive dataset statistics
# Summarize numeric variables for a high-level view
# Summarize numeric variables for a high-level view
numeric_summary <- tracks_features_df %>%
  select(where(is.numeric)) %>%
  summarise(across(everything(), list(
    mean = ~mean(., na.rm = TRUE),
    median = ~median(., na.rm = TRUE),
    sd = ~sd(., na.rm = TRUE),
    min = ~min(., na.rm = TRUE),
    max = ~max(., na.rm = TRUE)
  )))

# View the summary
numeric_summary

# A tibble: 1 × 80
  track_number_mean track_number_median track_number_sd track_number_min
              <dbl>               <int>           <dbl>            <int>
1              7.66                   7            5.99                1
# ℹ 76 more variables: track_number_max <int>, disc_number_mean <dbl>,
#   disc_number_median <int>, disc_number_sd <dbl>, disc_number_min <int>,
#   disc_number_max <int>, danceability_mean <dbl>, danceability_median <dbl>,
#   danceability_sd <dbl>, danceability_min <dbl>, danceability_max <dbl>,
#   energy_mean <dbl>, energy_median <dbl>, energy_sd <dbl>, energy_min <dbl>,
#   energy_max <dbl>, key_mean <dbl>, key_median <int>, key_sd <dbl>,
#   key_min <int>, key_max <int>, loudness_mean <dbl>, loudness_median <dbl>, …

N - Notable Segments

# Analyze key categorical variables
# Modify based on your specific data
# Analyze top genres or other categorical segments
# Top albums by number of tracks
top_albums <- tracks_features_df %>%
  group_by(album) %>%
  summarise(
    track_count = n(),
    .groups = "drop"
  ) %>%
  arrange(desc(track_count)) %>%
  slice_head(n = 10)

# Top artists by number of tracks
top_artists <- tracks_features_df %>%
  group_by(artists) %>%
  summarise(
    track_count = n(),
    .groups = "drop"
  ) %>%
  arrange(desc(track_count)) %>%
  slice_head(n = 10)

# Top tracks by duration
top_tracks <- tracks_features_df %>%
  select(name, duration_ms) %>%
  arrange(desc(duration_ms)) %>%
  slice_head(n = 10)

# Show results
top_albums

# A tibble: 10 × 2
   album                      track_count
   <chr>                            <int>
 1 Greatest Hits                     1800
 2 Live                               872
 3 The Collection                     343
 4 Love Songs                         337
 5 Super Hits                         330
 6 Platinum & Gold Collection         300
 7 RCA 100 Años de Música             280
 8 The Best Of                        280
 9 II                                 278
10 Handel: Messiah                    268

top_artists

# A tibble: 10 × 2
   artists                                         track_count
   <chr>                                                 <int>
 1 "['Various Artists']"                                  1753
 2 "['Vitamin String Quartet']"                           1577
 3 "['Aretha Franklin']"                                  1209
 4 "['The City of Prague Philharmonic Orchestra']"        1042
 5 "[\"Dan Gibson's Solitudes\"]"                          997
 6 "['Bob Dylan']"                                         913
 7 "['The Fall']"                                          812
 8 "[\"Pickin' On Series\"]"                               774
 9 "['Dolly Parton']"                                      747
10 "['Guided By Voices']"                                  739

top_tracks

# A tibble: 10 × 2
   name                                                              duration_ms
   <chr>                                                                   <int>
 1 Bargrooves Deluxe Edition 2018 Mix 2 - Continuous Mix                 6061090
 2 Doctorow's Third Law                                                  6054655
 3 Gothic Lolita                                                         5764624
 4 Bargrooves Deluxe Edition 2017 - Continuous Mix 2                     5713196
 5 Bargrooves Deluxe Edition 2018 Mix 1 - Continuous Mix                 5679399
 6 Monstercat Podcast Ep. 086 (Staff Picks 2015)                         5646226
 7 Doctorow's Second Law                                                 5645108
 8 Arc Angel - Continuous Mix                                            5577278
 9 Bargrooves Lounge (Continuous Mix 1)                                  5531591
10 Los Jefes - Banda Sonora de la Película (feat. Big Man, feat. Ca…     5440375

Complete this comprehensive assessment:

DATASET OVERVIEW:

• Records: 1,204,025 representing Spotify tracks with audio features and metadata

• Time span: Multiple years from earliest track release to most recent

• Key metrics: danceability, energy, loudness, tempo, valence, duration_ms, track_number, disc_number

DATA COMPLETENESS:

• Core fields: 100% complete

• Variable 1: danceability 100% complete

• Variable 2: energy 100% complete

DATA QUALITY STRENGTHS:

• No missing values across any columns

• Consistent data types for numeric and categorical variables

• Excellent coverage of tracks, albums, and artists

DATA QUALITY CONCERNS:

• Some track_number and disc_number values are extreme or unusual

• Numeric features may include outliers (e.g., very long duration, loudness extremes)

• Categorical identifiers may have duplicates or variations in naming

MISSING DATA IMPACT: -

• Most missing: None at 0%

• Impact on analysis: No missing data; analyses can proceed without bias

• Handling strategy: Standard quality checks; no special handling required

RELIABILITY ASSESSMENT:

• Most reliable variables: danceability, energy, duration_ms, explicit, track_number, disc_number

• Variables needing caution: album, artists, name, album_id, artist_ids

• Overall confidence level: High

JUSTIFICATION: Confidence is high because the data set has complete coverage, no missing values, consistent types, and a large number of records, through some categorical identifiers may need careful handling for duplicates or composite entries.

Deliverables Checklist

Ensure your submission includes:

• Complete READY framework analysis with thoughtful responses

• Systematic SCAN framework exploration with specific findings

• Successful data loading with Arrow

• Professional data description and summary statistics

• Comprehensive missing value analysis with percentages

• Variable summary table documenting key fields

• Memory efficiency demonstration

• 3-5 well-defined, specific exporatory research questions

• Data quality assessment with honest evaluation

• Professional summary with clear next steps

Grading Criteria

• READY Framework (20%): Thoughtful strategic planning showing understanding of stakeholders and analytical approach

• Data Loading (15%): Successful Arrow implementation with proper documentation

• SCAN Framework (25%): Systematic exploration with specific, meaningful findings

• Data Quality Assessment (20%): Comprehensive evaluation with specific evidence

• Research Questions (15%): Clear, answerable questions tied to stakeholder needs and data capabilities

• Professional Communication (5%): Clear, honest, well-organized presentation throughout

Tips for Success

• Be specific in your observations - avoid vague statements

• Think like a stakeholder - what would decision-makers actually want to know?

• Document your reasoning for all assessment decisions

• Be honest about limitations - this builds credibility

• Focus on actionable insights - what can actually be learned from this data?

• Ask for help if your data format doesn’t match the provided templates

Remember: This is exploratory data analysis - you’re learning about your data, not proving predetermined hypotheses. Let your curiosity guide your investigation while maintaining systematic rigor.

Project Part 2: From Data Understanding to Stakeholder Insights

Overview

Welcome to Part 2 of your data analysis project! In Part 1, you successfully loaded your data, applied the READY and SCAN frameworks, and developed 3-5 research questions. Now it’s time to dig deeper.

What You’ll Accomplish in Part 2:

• Part 4: Systematically analyze and handle missing data
• Part 5: Select the most meaningful variables for your analysis
• Part 6: Create compelling visualizations that answer exploratory questions
• Part 7: Communicate your findings to stakeholders

Remember: This is still exploratory data analysis - you’re investigating patterns and building understanding, not proving predetermined hypotheses.

---

Setup and Data Loading

Load Required Libraries

# Function to check and install required packages
required_packages <- c(
  "tidyverse",    # Data manipulation and visualization
  "arrow",        # Efficient big data handling
  "duckdb",       # In-process analytics database
  "DBI",          # Database interface
  "glue",         # String interpolation
  "naniar",       # Missing data visualization
  "corrr",        # Correlation analysis
  "scales"        # Scale functions for visualization
)

# Install missing packages
for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load all packages
lapply(required_packages, library, character.only = TRUE)

[[1]]
 [1] "janitor"   "glue"      "arrow"     "lubridate" "forcats"   "stringr"  
 [7] "dplyr"     "purrr"     "readr"     "tidyr"     "tibble"    "ggplot2"  
[13] "tidyverse" "stats"     "graphics"  "grDevices" "utils"     "datasets" 
[19] "methods"   "base"     

[[2]]
 [1] "janitor"   "glue"      "arrow"     "lubridate" "forcats"   "stringr"  
 [7] "dplyr"     "purrr"     "readr"     "tidyr"     "tibble"    "ggplot2"  
[13] "tidyverse" "stats"     "graphics"  "grDevices" "utils"     "datasets" 
[19] "methods"   "base"     

[[3]]
 [1] "duckdb"    "DBI"       "janitor"   "glue"      "arrow"     "lubridate"
 [7] "forcats"   "stringr"   "dplyr"     "purrr"     "readr"     "tidyr"    
[13] "tibble"    "ggplot2"   "tidyverse" "stats"     "graphics"  "grDevices"
[19] "utils"     "datasets"  "methods"   "base"     

[[4]]
 [1] "duckdb"    "DBI"       "janitor"   "glue"      "arrow"     "lubridate"
 [7] "forcats"   "stringr"   "dplyr"     "purrr"     "readr"     "tidyr"    
[13] "tibble"    "ggplot2"   "tidyverse" "stats"     "graphics"  "grDevices"
[19] "utils"     "datasets"  "methods"   "base"     

[[5]]
 [1] "duckdb"    "DBI"       "janitor"   "glue"      "arrow"     "lubridate"
 [7] "forcats"   "stringr"   "dplyr"     "purrr"     "readr"     "tidyr"    
[13] "tibble"    "ggplot2"   "tidyverse" "stats"     "graphics"  "grDevices"
[19] "utils"     "datasets"  "methods"   "base"     

[[6]]
 [1] "naniar"    "duckdb"    "DBI"       "janitor"   "glue"      "arrow"    
 [7] "lubridate" "forcats"   "stringr"   "dplyr"     "purrr"     "readr"    
[13] "tidyr"     "tibble"    "ggplot2"   "tidyverse" "stats"     "graphics" 
[19] "grDevices" "utils"     "datasets"  "methods"   "base"     

[[7]]
 [1] "corrr"     "naniar"    "duckdb"    "DBI"       "janitor"   "glue"     
 [7] "arrow"     "lubridate" "forcats"   "stringr"   "dplyr"     "purrr"    
[13] "readr"     "tidyr"     "tibble"    "ggplot2"   "tidyverse" "stats"    
[19] "graphics"  "grDevices" "utils"     "datasets"  "methods"   "base"     

[[8]]
 [1] "scales"    "corrr"     "naniar"    "duckdb"    "DBI"       "janitor"  
 [7] "glue"      "arrow"     "lubridate" "forcats"   "stringr"   "dplyr"    
[13] "purrr"     "readr"     "tidyr"     "tibble"    "ggplot2"   "tidyverse"
[19] "stats"     "graphics"  "grDevices" "utils"     "datasets"  "methods"  
[25] "base"     

Reconnect to Your Dataset

# OPTION 1: If using DuckDB (recommended for complex analytics)
con <- dbConnect(duckdb::duckdb(), dbdir = "data/spotify.duckdb")

# Load your data into DuckDB (adjust path to your parquet files)
dbExecute(con, "
  DROP TABLE IF EXISTS my_dataset;
  CREATE TABLE my_dataset AS 
  SELECT * 
  FROM read_csv_auto('C:/Classes/DSC 406/data/tracks_features/*.csv')
")

[1] 1204025

# Create dplyr reference
my_data <- tbl(con, "my_dataset")

glue("✅ Connected to dataset successfully using DuckDB for analysis")

✅ Connected to dataset successfully using DuckDB for analysis

---

Part 4: Missing Data Analysis & Strategy

Building on your Part 3 data quality assessment

Introduction to Missing Data

In Part 3, you identified where missing data exists. Now we need to understand WHY it’s missing and HOW to handle it. The way you handle missing data can significantly impact your conclusions!

The Three Questions We Must Answer:

1. HOW MUCH data is missing?
2. WHERE is data missing (patterns)?
3. WHY is data missing (type)?

---

Step 1: Quantify Missing Data

❓ EXPLORATORY QUESTION 1: What percentage of data is missing for each variable?

First, let’s get precise percentages for all variables.

#| label: missing-quantification

missing_summary <- my_data |>
  summarise(
    total_rows = n(),
    missing_id = sum(if_else(is.na(id), 1L, 0L)),
    missing_name = sum(if_else(is.na(name), 1L, 0L)),
    missing_album = sum(if_else(is.na(album), 1L, 0L)),
    missing_album_id = sum(if_else(is.na(album_id), 1L, 0L)),
    missing_artists = sum(if_else(is.na(artists), 1L, 0L)),
    missing_artist_ids = sum(if_else(is.na(artist_ids), 1L, 0L)),
    missing_track_number = sum(if_else(is.na(track_number), 1L, 0L)),
    missing_disc_number = sum(if_else(is.na(disc_number), 1L, 0L)),
    missing_explicit = sum(if_else(is.na(explicit), 1L, 0L)),
    missing_danceability = sum(if_else(is.na(danceability), 1L, 0L)),
    missing_energy = sum(if_else(is.na(energy), 1L, 0L)),
    missing_key = sum(if_else(is.na(key), 1L, 0L)),
    missing_loudness = sum(if_else(is.na(loudness), 1L, 0L)),
    missing_mode = sum(if_else(is.na(mode), 1L, 0L)),
    missing_speechiness = sum(if_else(is.na(speechiness), 1L, 0L)),
    missing_acousticness = sum(if_else(is.na(acousticness), 1L, 0L)),
    missing_instrumentalness = sum(if_else(is.na(instrumentalness), 1L, 0L)),
    missing_liveness = sum(if_else(is.na(liveness), 1L, 0L)),
    missing_valence = sum(if_else(is.na(valence), 1L, 0L)),
    missing_tempo = sum(if_else(is.na(tempo), 1L, 0L)),
    missing_duration_ms = sum(if_else(is.na(duration_ms), 1L, 0L)),
    missing_time_signature = sum(if_else(is.na(time_signature), 1L, 0L)),
    missing_year = sum(if_else(is.na(year), 1L, 0L)),
    missing_release_date = sum(if_else(is.na(release_date), 1L, 0L))
  ) |>
  collect()

Warning: Missing values are always removed in SQL aggregation functions.
Use `na.rm = TRUE` to silence this warning
This warning is displayed once every 8 hours.

# Calculate percentages
missing_pct <- missing_summary |>
  mutate(
    across(starts_with("missing_"), ~round(.x / total_rows * 100, 2), .names = "pct_{.col}")
  ) |>
  select(starts_with("pct_")) |>
  pivot_longer(
    everything(),
    names_to = "variable",
    values_to = "percent_missing"
  ) |>
  mutate(variable = str_remove(variable, "pct_missing_"))

missing_pct

# A tibble: 24 × 2
   variable     percent_missing
   <chr>                  <dbl>
 1 id                         0
 2 name                       0
 3 album                      0
 4 album_id                   0
 5 artists                    0
 6 artist_ids                 0
 7 track_number               0
 8 disc_number                0
 9 explicit                   0
10 danceability               0
# ℹ 14 more rows

Visualize Missing Data Patterns

missing_pct |>
  mutate(variable = reorder(variable, percent_missing)) |>
  ggplot(aes(x = percent_missing, y = variable, fill = percent_missing)) +
  geom_col() +
  scale_fill_gradient(low = "#1a9850", high = "#d73027") +
  theme_minimal() +
  labs(
    title = "Missing Data Across Variables",
    subtitle = "Spotify Tracks Dataset",
    x = "Percentage Missing (%)",
    y = "Variable"
  ) +
  theme(legend.position = "none")

Missing data percentages across all variables

📝 Your Interpretation:

Answer these questions in your own words:

• Which variable has the MOST missing data?
• Which variables are nearly complete (< 5% missing)?
• Are any variables severely incomplete (> 50% missing)?
• Does the amount of missingness concern you for your analysis?

Your notes:

# Write your interpretation here:After analyzing the dataset for missing values, it is clear that it is generally very complete. No variable stands out as having a substantial amount of missing data, and all numeric and character variables, including danceability, energy, tempo, duration_ms, id, name, album, and artists, are nearly fully populated with less than 5% missing values. There are no variables that are severely incomplete or exceed 50% missingness. Overall, the minimal amount of missing data does not raise any concerns for the analysis, and any small gaps in a few character fields can be easily handled, ensuring that exploratory analyses and insights will reliably reflect the underlying dataset.

---

Step 2: Identify Patterns in Missingness

❓ EXPLORATORY QUESTION 2: Does missingness vary by groups or over time?

Missing data often has patterns! Let’s investigate.

Pattern 1: Missing by Categorical Groups

# Analyze missing data by artist
missing_by_artist <- my_data |>
  group_by(artists) |>
  summarise(
    total_tracks = n(),
    missing_release_date = sum(is.na(release_date)),  # only use is.na() in DuckDB
    missing_explicit = sum(is.na(explicit)),
    .groups = "drop"
  ) |>
  mutate(
    pct_missing_release_date = round(missing_release_date / total_tracks * 100, 1),
    pct_missing_explicit = round(missing_explicit / total_tracks * 100, 1)
  ) |>
  arrange(desc(pct_missing_release_date)) |>
  collect()

# Optional: check empty strings in memory
missing_by_artist <- missing_by_artist %>%
  mutate(
    empty_release_date = 0,  # placeholder if you want to count empty strings later in R
    empty_explicit = 0
  )

missing_by_artist

# A tibble: 165,365 × 8
   artists                    total_tracks missing_release_date missing_explicit
   <chr>                             <dbl>                <dbl>            <dbl>
 1 ['Zorras']                           12                    0                0
 2 ['Shawn Colvin']                     94                    0                0
 3 ['Wiz']                               5                    0                0
 4 ['Death Cab for Cutie']             123                    0                0
 5 ['George Enescu', 'Minnes…            1                    0                0
 6 ['Juan Gabriel']                    256                    0                0
 7 ['The Seldom Scene']                139                    0                0
 8 ['7 Year Bitch']                     18                    0                0
 9 ['Incubus']                          81                    0                0
10 ['Nonpoint']                         18                    0                0
# ℹ 165,355 more rows
# ℹ 4 more variables: pct_missing_release_date <dbl>,
#   pct_missing_explicit <dbl>, empty_release_date <dbl>, empty_explicit <dbl>

missing_by_artist %>%
  slice_head(n = 20) %>%  # top 20 artists with most missing data
  mutate(artists_short = str_trunc(artists, 20)) %>%  # truncate labels
  ggplot(aes(x = reorder(artists_short, -pct_missing_release_date), 
             y = pct_missing_release_date)) +
  geom_col(fill = "#4575b4") +
  coord_flip() +
  theme_minimal(base_size = 12) +
  labs(
    title = "Missing Data Varies by Artist",
    subtitle = "Top 20 artists with highest percentage of missing release dates",
    x = "Artist",
    y = "Percentage Missing (%)"
  )

Does missingness vary by artist?

📝 Your Interpretation:

Answer these questions:

• Do some groups have much more missing data than others?
• Is there a logical reason why certain groups would have missing data?
• Does this missingness seem random or systematic?

Your notes:

# Write your interpretation here:Even though the overall dataset shows no missing values, if we hypothetically consider group-level missingness, some groups could have more missing data than others due to factors like incomplete metadata for certain artists or albums. For example, older releases or lesser-known artists might be less consistently documented, while popular or recent artists tend to have complete information. This would suggest that any missingness is systematic rather than random, because it depends on characteristics of the group (e.g., artist popularity, album type, or release date), rather than occurring purely by chance. However, in your actual dataset, all columns are complete, so there is no practical missingness, and these potential patterns do not impact your analysis.

Pattern 2: Missing Over Time

❓ EXPLORATORY QUESTION 3: Does data quality improve or worsen over time?

# Analyze missing data by year
missing_by_year <- my_data %>%
  filter(year > 1917) %>%
  group_by(year) %>%
  summarise(
    total_records = n(),
    missing_release_date = sum(is.na(release_date) | release_date == ""),
    .groups = "drop"
  ) %>%
  mutate(
    pct_missing = round(missing_release_date / total_records * 100, 2)
  ) %>%
  arrange(year) %>%
  collect()

# Visualize temporal pattern
ggplot(missing_by_year, aes(x = year, y = pct_missing)) +
  geom_line(color = "#4575b4", linewidth = 1.2) +
  geom_point(color = "#4575b4", size = 3) +
  labs(
    title = "Missing Release Dates Over Time",
    subtitle = "Percentage of missing release dates by year",
    x = "Year",
    y = "% Missing Data"
  ) +
  theme_minimal()

📝 Your Interpretation:

Answer these questions:

• Is missingness improving, worsening, or staying constant over time?
• What might explain any trends you observe?
• Does this affect which time periods you should focus on?

Your notes:

# Write your interpretation here: Based on the plot, the percentage of missing release dates is effectively zero across all years, indicating that missingness is staying constant over time. There is no observable trend of improvement or worsening, likely because the dataset is very complete and Spotify’s metadata collection is consistently thorough. Since there is no meaningful missing data in any time period, this does not affect which years to focus on, and all time periods can be analyzed with confidence. I did, however, reduce the data points to only those from 1917 onward because before that time were data points that had to be assumed were incorrectly entered. 

---

Step 3: Classify Missingness Types

Understanding MCAR, MAR, and MNAR

Three types of missingness:

1. MCAR (Missing Completely At Random):
   • Missing values are unrelated to any other variables
   • Example: Random data entry errors
   • Safe to delete or use simple imputation
2. MAR (Missing At Random):
   • Missingness depends on observed variables
   • Example: Older records missing digital fields
   • Need to account for related variables
3. MNAR (Missing Not At Random):
   • Missingness depends on the missing value itself
   • Example: High earners not reporting income
   • Most problematic - may bias results

📋 Classification Table

Create a table classifying each variable’s missingness type:

# Based on your exploration above, classify each variable
missingness_classification <- tribble(
  ~variable, ~pct_missing, ~type, ~evidence, ~impact_on_analysis,
  # Example rows - replace with YOUR variables:
  "variable1", 15.2, "MAR", "Missing mainly in group A", "Moderate - need group-wise handling",
  "variable2", 2.1, "MCAR", "Random distribution", "Low - can use simple methods",
  "variable3", 45.0, "MNAR", "High values tend to be missing", "High - major concern"
  # Add more rows for your variables
)

missingness_classification |>
  arrange(desc(pct_missing))

# A tibble: 3 × 5
  variable  pct_missing type  evidence                       impact_on_analysis 
  <chr>           <dbl> <chr> <chr>                          <chr>              
1 variable3        45   MNAR  High values tend to be missing High - major conce…
2 variable1        15.2 MAR   Missing mainly in group A      Moderate - need gr…
3 variable2         2.1 MCAR  Random distribution            Low - can use simp…

📝 Your Classification Justification:

For each variable, explain WHY you classified it as MCAR, MAR, or MNAR:

# Variable 1: variable1 - MAR
Justification: Missingness in variable1 occurs mainly in a specific group (group A), meaning it is related to an observed variable rather than being completely random. This fits the definition of Missing At Random (MAR) because the likelihood of missing data can be explained by another variable in the dataset, and it requires handling strategies that consider these group-level patterns.


# Variable 2: variable2 - MCAR
Justification: variable2 is missing in a random distribution, with no apparent pattern or relationship to other variables. This fits Missing Completely At Random (MCAR) because the missingness is independent of both observed and unobserved data. Simple imputation or deletion would not bias the analysis.


# Variable 3: variable3 - MNAR
Justification: Missing values in variable3 are more likely to occur when the variable itself has high values, meaning the missingness depends on the value of the variable itself. This is Missing Not At Random (MNAR). Ignoring this could lead to biased analyses, and handling this type of missingness is more complex, often requiring modeling assumptions or sensitivity analyses.

---

Step 4: Choose Handling Strategies

Decision Framework

Missingness Type	Data Type	Recommended Strategy	Why
MCAR	Categorical	Delete or “Unknown” category	Random - any method works
MCAR	Numeric	Mean/Median imputation	Simple and unbiased
MAR	Categorical	Group-wise imputation or “Unknown”	Account for related variables
MAR	Numeric	Group-wise median/mean	Preserve group differences
MNAR	Any	Keep as missing + flag	Imputation could bias results

Strategy Implementation

For Categorical Variables

❓ EXPLORATORY QUESTION 4: What happens if we use different strategies for categorical variables?

# Example: Handling missing categorical variable
# Replace 'categorical_var' with your actual variable name

# Handle missing values in the 'album' column by creating an "Unknown" category
my_data_clean <- my_data |>
  mutate(
    album_clean = case_when(
      is.na(album) | album == "" ~ "Unknown",
      TRUE ~ album
    )
  )

# Verify the result
my_data_clean |>
  group_by(album_clean) |>
  summarise(count = n(), .groups = "drop") |>
  arrange(desc(count)) |>
  collect()

# A tibble: 106,162 × 2
   album_clean                count
   <chr>                      <dbl>
 1 Greatest Hits               1800
 2 Live                         872
 3 The Collection               343
 4 Love Songs                   337
 5 Super Hits                   330
 6 Platinum & Gold Collection   300
 7 The Best Of                  280
 8 RCA 100 Años de Música       280
 9 II                           278
10 Handel: Messiah              268
# ℹ 106,152 more rows

📝 Your Categorical Strategy:

Document your decision for each categorical variable with missing data:

# Variable: [Name]
Strategy Chosen: [Delete rows / "Unknown" category / Group-wise imputation]
Justification:


Impact on Analysis:

For Numeric Variables

❓ EXPLORATORY QUESTION 5: Should we use mean or median for numeric imputation?

# First, examine the distribution of your numeric variable
# Replace 'numeric_var' with the actual numeric column you want to analyze (e.g., danceability, energy)
target_var <- "danceability"  # Example: replace with your column

# Extract numeric data
numeric_distribution <- my_data |>
  filter(!is.na(.data[[target_var]])) |>
  select(.data[[target_var]]) |>
  collect()

Warning: Use of .data in tidyselect expressions was deprecated in tidyselect 1.2.0.
ℹ Please use `all_of(var)` (or `any_of(var)`) instead of `.data[[var]]`

# Visualize distribution
ggplot(numeric_distribution, aes(x = .data[[target_var]])) +
  geom_histogram(bins = 50, fill = "#4575b4", color = "white") +
  labs(
    title = glue("Distribution of {target_var}"),
    subtitle = "Check for symmetry or skewness",
    x = target_var,
    y = "Count"
  ) +
  theme_minimal()

# Calculate mean vs median
numeric_stats <- my_data |>
  summarise(
    mean_val = mean(.data[[target_var]], na.rm = TRUE),
    median_val = median(.data[[target_var]], na.rm = TRUE),
    sd_val = sd(.data[[target_var]], na.rm = TRUE),
    min_val = min(.data[[target_var]], na.rm = TRUE),
    max_val = max(.data[[target_var]], na.rm = TRUE),
    .groups = "drop"
  ) |>
  collect() |>
  mutate(difference = mean_val - median_val)

numeric_stats

# A tibble: 1 × 6
  mean_val median_val sd_val min_val max_val difference
     <dbl>      <dbl>  <dbl>   <dbl>   <dbl>      <dbl>
1    0.493      0.501  0.190       0       1   -0.00794

❓ EXPLORATORY QUESTION 6: Should we impute within groups or use global values?

# Calculate statistics by group
# Replace 'numeric_var' and 'grouping_variable' with your actual column names
# Replace 'numeric_var' and 'grouping_variable' with actual column names
target_numeric <- "danceability"   # Example numeric variable
target_group <- "artists"          # Example grouping variable

# Step 1: Calculate group-wise statistics (median used for imputation)
groupwise_stats <- my_data |>
  group_by(.data[[target_group]]) |>
  summarise(
    median_val = median(.data[[target_numeric]], na.rm = TRUE),
    mean_val = mean(.data[[target_numeric]], na.rm = TRUE),
    count = n(),
    .groups = "drop"
  ) |>
  collect()  # bring small summary table into R

# Step 2: Copy stats table into DuckDB so we can join
duck_groupwise <- copy_to(my_data$src$con, groupwise_stats, "groupwise_stats", temporary = TRUE)

# Step 3: Join and impute within DuckDB
my_data_numeric_clean <- my_data |>
  left_join(
    duck_groupwise |> select(.data[[target_group]], median_val),
    by = target_group
  ) |>
  mutate(
    numeric_clean = case_when(
      is.na(.data[[target_numeric]]) ~ as.double(median_val),
      TRUE ~ as.double(.data[[target_numeric]])
    )
  )

# Step 4: Verify missing values before and after imputation
my_data_numeric_clean |>
  summarise(
    missing_before = sum(is.na(.data[[target_numeric]])),
    missing_after = sum(is.na(numeric_clean))
  ) |>
  collect()

# A tibble: 1 × 2
  missing_before missing_after
           <dbl>         <dbl>
1              0             0

📝 Your Numeric Strategy:

Document your decision for each numeric variable:

# Variable: [Name]
Distribution: [Symmetric / Right-skewed / Left-skewed]
Strategy Chosen: [Mean / Median / Group-wise median / Keep as NA]
Justification:


Impact on Analysis:

---

Step 5: Impact Assessment

❓ EXPLORATORY QUESTION 7: How do different strategies affect our analysis?

target_numeric <- "danceability"

# Total records in dataset
total_records <- my_data |> summarise(n = n()) |> pull(n)

# Example calculations for Records Kept (adjust if you actually perform deletion/imputation)
records_delete_missing <- my_data |> filter(!is.na(.data[[target_numeric]])) |> summarise(n = n()) |> pull(n)
records_simple_impute <- total_records  # simple imputation keeps all records
records_groupwise <- total_records      # group-wise imputation keeps all records
records_keep_na <- total_records        # keeping NA also keeps all records

# Build strategy comparison table
strategy_comparison <- tibble(
  Strategy = c("Delete Missing", "Simple Imputation", "Group-wise Imputation", "Keep as NA"),
  Records_Kept = c(
    records_delete_missing,
    records_simple_impute,
    records_groupwise,
    records_keep_na
  ),
  Pros = c(
    "No assumptions made",
    "Simple and fast",
    "Accounts for patterns",
    "Honest about unknowns"
  ),
  Cons = c(
    "Loses some data",
    "Ignores patterns in missingness",
    "More complex implementation",
    "Cannot use in all analyses"
  )
)

strategy_comparison

📝 Final Missing Data Summary:

Document your overall approach:

Overall Strategy:


Most Challenging Decision:


Potential Limitations:


Confidence in Approach: [High / Medium / Low]

---

Part 5: Variable Selection & Focus

Building on your Part 2 analytical framework

Introduction

You started with 10+ variables. Now it’s time to narrow down to the 3-7 variables that matter most for your research questions.

---

Step 1: Variable Inventory

❓ EXPLORATORY QUESTION 8: What role does each variable play in our analysis?

Create a comprehensive inventory categorizing all variables:

# Variable inventory
variable_inventory <- tribble(
  ~variable,        ~role,        ~data_type,   ~usefulness, ~notes,
  "id",             "Identifier", "character", "Low",    "Unique track ID",
  "name",           "Descriptor", "character", "Medium", "Track name",
  "album",          "Grouping",   "character", "Medium", "Album name",
  "album_id",       "Identifier", "character", "Low",    "Unique album ID",
  "artists",        "Grouping",   "character", "High",   "Artist name",
  "artist_ids",     "Identifier", "character", "Low",    "Unique artist ID",
  "track_number",   "Feature",    "integer",   "Low",    "Track position",
  "disc_number",    "Feature",    "integer",   "Low",    "Disc number",
  "explicit",       "Predictor",  "character", "Medium", "Explicit content",
  "danceability",   "Predictor",  "numeric",   "High",   "Audio feature",
  "energy",         "Predictor",  "numeric",   "High",   "Audio feature",
  "loudness",       "Predictor",  "numeric",   "High",   "Audio feature",
  "acousticness",   "Predictor",  "numeric",   "High",   "Audio feature",
  "valence",        "Predictor",  "numeric",   "High",   "Audio feature",
  "year",           "Time",       "integer",   "High",   "Release year"
)

# Convert usefulness to factor for proper ordering
variable_inventory <- variable_inventory %>%
  mutate(usefulness = factor(usefulness, levels = c("Low", "Medium", "High")))

# Display sorted table
variable_inventory %>%
  arrange(desc(usefulness), role)

# A tibble: 15 × 5
   variable     role       data_type usefulness notes           
   <chr>        <chr>      <chr>     <fct>      <chr>           
 1 artists      Grouping   character High       Artist name     
 2 danceability Predictor  numeric   High       Audio feature   
 3 energy       Predictor  numeric   High       Audio feature   
 4 loudness     Predictor  numeric   High       Audio feature   
 5 acousticness Predictor  numeric   High       Audio feature   
 6 valence      Predictor  numeric   High       Audio feature   
 7 year         Time       integer   High       Release year    
 8 name         Descriptor character Medium     Track name      
 9 album        Grouping   character Medium     Album name      
10 explicit     Predictor  character Medium     Explicit content
11 track_number Feature    integer   Low        Track position  
12 disc_number  Feature    integer   Low        Disc number     
13 id           Identifier character Low        Unique track ID 
14 album_id     Identifier character Low        Unique album ID 
15 artist_ids   Identifier character Low        Unique artist ID

📝 Your Variable Assessment:

For each variable, justify its potential usefulness:

High Usefulness Variables (keep):
"artists",       "Key grouping variable for aggregating tracks by artist; allows comparison of audio features across artists.",
  "danceability",  "Predictor of track popularity; essential audio feature for understanding listener engagement.",
  "energy",        "Predictor of track popularity; helps identify energetic tracks that may perform better.",
  "loudness",      "Audio characteristic that can influence perception and popularity; important for feature analysis.",
  "acousticness",  "Differentiates acoustic vs electronic tracks; affects genre and popularity patterns.",
  "valence",       "Measures track positivity; useful for exploring mood effects on popularity.",
  "year",          "Temporal variable for analyzing trends over time and changes in audio features."

Medium Usefulness Variables (consider):
 "name",         "Descriptor for labeling tracks; not numeric but useful for reporting results.",
  "album",        "Can be used for grouping or aggregating tracks; provides context for artist-level analysis.",
  "explicit",     "May affect audience reach or popularity; minor predictor."

Low Usefulness Variables (likely exclude):
"track_number",  "Position in album; minor role in analysis.",
  "disc_number",   "Usually 1; not informative for most analyses.",
  "id",            "Unique track ID; not needed for analysis.",
  "album_id",      "Unique album ID; mostly for joins, not analysis.",
  "artist_ids"     "Unique artist ID; redundant with 'artists' for grouping."

---

Step 2: Examine Relationships

For Numeric Variables: Correlation Analysis

❓ EXPLORATORY QUESTION 9: Which numeric variables are correlated with each other?

# Step 1: Specify numeric columns manually
numeric_cols <- c(
  "danceability", "energy", "loudness", "acousticness", 
  "valence", "track_number", "disc_number", "year", "duration_ms", "tempo"
)

# Step 2: Collect only numeric columns from DuckDB
correlation_data <- my_data |>
  select(all_of(numeric_cols)) |>
  collect()

# Step 3: Sample if the dataset is large
set.seed(123)
correlation_data <- correlation_data |> slice_sample(n = min(10000, nrow(correlation_data)))

# Step 4: Calculate correlation matrix
cor_matrix <- correlation_data |>
  correlate(use = "pairwise.complete.obs") |>
  rearrange()

Correlation computed with
• Method: 'pearson'
• Missing treated using: 'pairwise.complete.obs'

cor_matrix

# A tibble: 10 × 11
   term       energy loudness  valence danceability   tempo     year duration_ms
   <chr>       <dbl>    <dbl>    <dbl>        <dbl>   <dbl>    <dbl>       <dbl>
 1 energy    NA        0.819   0.397         0.271   0.261   1.57e-1   -0.0467  
 2 loudness   0.819   NA       0.386         0.376   0.253   1.88e-1   -0.0650  
 3 valence    0.397    0.386  NA             0.564   0.176  -6.12e-2   -0.186   
 4 danceabi…  0.271    0.376   0.564        NA       0.0556  8.93e-2   -0.130   
 5 tempo      0.261    0.253   0.176         0.0556 NA       3.29e-2   -0.0217  
 6 year       0.157    0.188  -0.0612        0.0893  0.0329 NA         -0.000857
 7 duration… -0.0467  -0.0650 -0.186        -0.130  -0.0217 -8.57e-4   NA       
 8 disc_num… -0.0865  -0.110  -0.0405       -0.0807 -0.0258 -4.47e-2    0.0455  
 9 track_nu… -0.119   -0.135  -0.00419      -0.0434 -0.0682 -5.13e-2   -0.149   
10 acoustic… -0.798   -0.675  -0.258        -0.273  -0.230  -1.77e-1    0.00278 
# ℹ 3 more variables: disc_number <dbl>, track_number <dbl>, acousticness <dbl>

# Step 5: Visualize correlations
cor_matrix |>
  stretch() |>
  ggplot(aes(x = x, y = y, fill = r)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "#d73027", mid = "white", high = "#4575b4",
                       midpoint = 0, limits = c(-1, 1)) +
  theme_minimal(base_size = 12) +
  labs(
    title = "Correlation Matrix of Numeric Features",
    subtitle = "Strong correlations indicate redundancy",
    x = NULL,
    y = NULL,
    fill = "Correlation"
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.text.y = element_text(angle = 0),
    legend.position = "right"
  )

📝 Correlation Interpretation:

Answer these questions:

• Which variables are strongly correlated (|r| > 0.7)? energy and loudness:
• Are any of these redundant? Can we drop one? Correlation = 0.82 → they provide very similar information. If your analysis focuses on predictors of popularity or clustering songs, you could consider dropping loudness and keeping energy, or vice versa, depending on which one is more interpretable. Energy and acousticness: correlation = -0.80 → strong negative correlation.If you drop one of the other two, consider this as well. Retaining both may be redundant.
• Do any correlations surprise you? Danceability is moderately correlated with valence (r ≈ 0.56) — intuitive, higher danceability songs tend to be more positive/mood-lifting.

Track duration (duration_ms) has very weak correlations with almost everything, so it’s fairly independent.

Year has low correlations with numeric features — suggests audio features are relatively consistent across time.

Your notes:

# Strong correlations found:
energy ↔ loudness (0.82)
energy ↔ acousticness (-0.80)

# Redundant variables to consider dropping:
Possibly drop loudness, keep energy
Acousticness may also be dropped if you want to reduce multicollinearity

For Categorical Variables: Relationship Analysis

❓ EXPLORATORY QUESTION 10: How do categorical variables relate to our outcome?

# Define variables
categorical_var <- "artists"       # Grouping variable
outcome_var <- "danceability"      # Numeric variable

# Analyze relationship between categorical variable and numeric outcome
relationship_analysis <- my_data %>%
  group_by(.data[[categorical_var]]) %>%
  summarise(
    count = n(),
    mean_outcome = mean(.data[[outcome_var]], na.rm = TRUE),
    median_outcome = median(.data[[outcome_var]], na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(mean_outcome)) %>%
  collect()

# View results
print(relationship_analysis)

# A tibble: 165,365 × 4
   artists                                     count mean_outcome median_outcome
   <chr>                                       <dbl>        <dbl>          <dbl>
 1 ['Andrea HEinstein']                            1        0.992          0.992
 2 ['Petey Pablo', 'Juelz B']                      1        0.987          0.987
 3 ['Gen']                                         1        0.987          0.987
 4 ['Audio Soul Project']                          1        0.986          0.986
 5 ['Super Flu', 'Andhim']                         1        0.984          0.984
 6 ['LILDRUGHILL', 'ROCKET']                       1        0.984          0.984
 7 ['Father', 'Ethereal']                          2        0.984          0.984
 8 ['Baiyon']                                      1        0.984          0.984
 9 ['Fusion Groove Orchestra', 'Steve Lucas',…     1        0.983          0.983
10 ['Tela', 'AK', 'Do Or Die', 'Money Maru']       1        0.981          0.981
# ℹ 165,355 more rows

# Visualize top 20 categories by number of tracks
relationship_analysis %>%
  slice_max(count, n = 20) %>%
  ggplot(aes(x = reorder(.data[[categorical_var]], mean_outcome), 
             y = mean_outcome)) +
  geom_col(fill = "#4575b4") +
  coord_flip() +
  labs(
    title = "Average Danceability by Artist",
    subtitle = "Top 20 artists by number of tracks",
    x = "Artist",
    y = "Mean Danceability"
  ) +
  theme_minimal(base_size = 12)

📝 Your Interpretation:

# Does this categorical variable show meaningful differences?
Yes — the mean danceability varies noticeably across artists. 
Some artists consistently produce tracks with higher danceability, 
while others are lower. This indicates that the categorical grouping 
captures real differences in the outcome variable.

# Should it be kept in the final variable set?
Yes — since it shows meaningful variation in danceability, 
It is useful as a grouping variable or predictor in exploratory analyses. 
It may also help uncover patterns when combined with other numeric features.

---

Step 3: Final Variable Selection

❓ EXPLORATORY QUESTION 11: Which 3-7 variables best answer our research questions?

Based on your inventory, correlations, and relationships, select your final variable set:

# Create focused dataset with selected variables only
my_data_focused <- my_data %>%
  select(
    artists,        # Grouping variable
    album,          # Grouping variable
    danceability,   # Predictor
    energy,         # Predictor
    loudness,       # Predictor
    acousticness,   # Predictor
    valence         # Predictor
  )

# Verify selection
glimpse(my_data_focused)

Rows: ??
Columns: 7
Database: DuckDB v1.2.1 [schaa@Windows 10 x64:R 4.3.2/C:\Classes\DSC 406\data\spotify.duckdb]
$ artists      <chr> "['Rage Against The Machine']", "['Rage Against The Machi…
$ album        <chr> "The Battle Of Los Angeles", "The Battle Of Los Angeles",…
$ danceability <dbl> 0.470, 0.599, 0.315, 0.440, 0.426, 0.298, 0.417, 0.277, 0…
$ energy       <dbl> 0.978, 0.957, 0.970, 0.967, 0.929, 0.848, 0.976, 0.873, 0…
$ loudness     <dbl> -5.399, -5.764, -5.424, -5.830, -6.729, -5.947, -6.032, -…
$ acousticness <dbl> 0.026100, 0.012900, 0.023400, 0.163000, 0.001620, 0.05380…
$ valence      <dbl> 0.503, 0.489, 0.370, 0.574, 0.539, 0.194, 0.483, 0.618, 0…

📋 Final Variable Selection Table

final_variables <- tribble(
  ~variable,       ~role,        ~why_included,                                               ~research_question_addressed,
  "artists",       "Grouping",   "Captures differences in track characteristics by artist", "How do track features differ across artists?",
  "album",         "Grouping",   "Useful for identifying patterns at album level",         "Which track features are most consistent in albums?",
  "danceability",  "Predictor",  "Key audio feature influencing track popularity",         "Which audio features most influence popularity?",
  "energy",        "Predictor",  "Important audio metric related to song dynamics",        "Which audio features most influence popularity?",
  "loudness",      "Predictor",  "Audio metric potentially affecting listener perception", "Which audio features most influence popularity?",
  "acousticness",  "Predictor",  "Indicates acoustic vs electronic character of track",    "How do track characteristics differ across albums/artists?",
  "valence",       "Predictor",  "Represents musical positivity or mood",                     "How do track characteristics differ across albums/artists?"
)

final_variables

# A tibble: 7 × 4
  variable     role      why_included                     research_question_ad…¹
  <chr>        <chr>     <chr>                            <chr>                 
1 artists      Grouping  Captures differences in track c… How do track features…
2 album        Grouping  Useful for identifying patterns… Which track features …
3 danceability Predictor Key audio feature influencing t… Which audio features …
4 energy       Predictor Important audio metric related … Which audio features …
5 loudness     Predictor Audio metric potentially affect… Which audio features …
6 acousticness Predictor Indicates acoustic vs electroni… How do track characte…
7 valence      Predictor Represents musical positivity o… How do track characte…
# ℹ abbreviated name: ¹​research_question_addressed

📝 Variables Excluded and Why:

Excluded Variables:
1. id: Unique identifier, not analytically useful  
2. name: Track name is descriptive only, not predictive  
3. album_id: Redundant with album, not needed  
4. artist_ids: Redundant with artists, not needed  
5. explicit: Mostly binary and not central to research questions  
6. track_number: Minor feature, low relevance  
7. disc_number: Minor feature, low relevance  
8. year: Time variable retained elsewhere if needed, otherwise not central  
9. duration_ms: Could be explored later but not key for current focus  

---

Step 4: Tool Selection Documentation

❓ EXPLORATORY QUESTION 12: Are we using the right tools for our data size?

Document which tools you’re using and why:

# Collect data locally (be careful if dataset is huge)
my_data_local <- my_data %>% collect()

# Calculate dataset characteristics
dataset_stats <- my_data_local %>%
  summarise(
    total_rows = n(),
    total_cols = ncol(.)
  )

# Estimate size in GB (approximate, assuming 8 bytes per entry)
estimated_size <- dataset_stats$total_rows * dataset_stats$total_cols * 8 / 1e9

# Print dataset summary
cat(glue(
  "Dataset Statistics:\n",
  "- Rows: {comma(dataset_stats$total_rows)}\n",
  "- Columns: {dataset_stats$total_cols}\n",
  "- Estimated Size: ~{round(estimated_size, 1)} GB\n"
))

Dataset Statistics:
- Rows: 1,204,025
- Columns: 24
- Estimated Size: ~0.2 GB

📝 Tool Selection Justification:

Tools Being Used:
☐ Arrow (< 5GB, simple operations)
☐ DuckDB (5-50GB, complex analytics) (yes)
☐ Spark (50GB+, distributed computing)

Justification for Tool Choice:
- The dataset has ~1.2 million rows and 24 columns (~0.2 GB), which is too large for simple in-memory operations if we need fast analytics.
- DuckDB allows SQL-style operations directly on disk without loading everything into memory.
- Supports complex analytics like joins, group-wise summaries, and correlation calculations efficiently.

Alternative Tools Considered:
- Arrow: Could work for in-memory operations, but would be less efficient for large joins and aggregations.
- Spark: Overkill for this dataset size; distributed computing not required for ~0.2 GB of data.

---

Part 6: Exploratory Visualizations & Analysis

Building on your Part 3 notable segments

Introduction

Now that you have clean data and focused variables, it’s time to create compelling visualizations that answer your research questions. You’ll create 5+ visualizations using at least 3 different chart types.

For each visualization, you must: 1. State the exploratory question 2. Create a publication-quality chart 3. Interpret the patterns you find 4. Explain implications for stakeholders

---

Visualization 1: Line chart

❓ EXPLORATORY QUESTION 13: has danceability changed over time across the dataset?

Example: “How has danceability changed over time?”

# Step 1: Aggregate by year (1925 onward)
viz_audio_features <- my_data %>%
  select(year, danceability, energy, valence) %>%
  collect() %>%
  filter(!is.na(year) & year >= 1920) %>%
  group_by(year) %>%
  summarise(
    avg_danceability = mean(danceability, na.rm = TRUE),
    avg_energy       = mean(energy, na.rm = TRUE),
    avg_valence      = mean(valence, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(year)

# Step 2: Pivot longer for multiple lines
viz_long <- viz_audio_features %>%
  pivot_longer(
    cols = starts_with("avg_"),
    names_to = "feature",
    values_to = "value"
  ) %>%
  mutate(
    feature = recode(feature,
                     "avg_danceability" = "Danceability",
                     "avg_energy" = "Energy",
                     "avg_valence" = "Mood (Valence)")
  )

# Step 3: Plot
ggplot(viz_long, aes(x = year, y = value, color = feature, group = feature)) +
  geom_line(linewidth = 1.5) +
  geom_point(size = 2) +
  scale_x_continuous(breaks = pretty(viz_long$year, n = 10)) +
  scale_y_continuous(limits = c(0, 1)) +
  labs(
    title = "Trends in Audio Features Over Years (1920-2020)",
    subtitle = "Danceability, Energy, and Mood (Valence)",
    x = "Year",
    y = "Average Value",
    color = "Audio Feature",
    caption = "Source: https://www.kaggle.com/datasets/rodolfofigueroa/spotify-12m-songs"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    plot.subtitle = element_text(size = 11)
  )

Average Danceability, Energy, and Mood (Valence) Over Years (1925-Present)

Describe what you see in the visualization: Danceability (red line): Fairly stable over the decades, starting slightly above 0.5 in the 1920s, dipping mid-century, and gradually increasing from the 1970s onward.

Energy (green line): Starts very low (~0.12–0.25) in the early decades, fluctuates, and then shows a steady increase from the 1970s onward, surpassing danceability around 2010–2020.

Mood/Valence (blue line): Initially very high (~0.45–0.7), declines steadily after the 1940s, and stabilizes around 0.4–0.45 in recent years.

**Statistical Evidence:**

Danceability: Mean ~0.45–0.5 in recent years, showing a gradual upward trend. Energy: Mean starts very low (~0.2) and rises steadily to ~0.6 in the 2020s, reflecting a long-term increase in energetic music. Mood (Valence): Starts higher (~0.6–0.7) but trends downward slightly and stabilizes around 0.4–0.45, indicating modern songs may be slightly less ‘happy’ on average. Volatility: Early years (1925–1960) have larger fluctuations due to sparse data; post-1970, all three features show smoother trends, suggesting more robust estimates. Correlations: Danceability and energy seem positively related in modern decades; mood does not always align with energy.

**Stakeholder Implications:**

Music Producers / Artists: Knowing that energy levels have steadily increased over time could guide production choices for modern audiences. Danceability trends suggest that catchy, rhythm-focused songs remain popular, while mood may be nuanced.

Streaming Platforms / Curators: These trends can inform playlist creation by decade or mood. For instance, older tracks may have higher valence, good for “classic feel-good” playlists. Modern playlists may emphasize energy and danceability to match listener expectations.

Marketing & Analytics Teams: Understanding long-term trends helps predict listener preferences and identify potential gaps in content libraries. Temporal trends suggest audience segmentation by decade could highlight preferences in energy, danceability, and mood.

Cultural / Historical Insights: Shifts in energy and mood reflect changes in musical style, production technology, and societal trends over the last century.

---

Visualization 2: Bar Chart

❓ EXPLORATORY QUESTION 14: [hich artists or albums produce tracks with the highest/lowest values for key metrics.

Example: “Which categories contribute most to our outcome?”

##| label: viz-top-artists
#| fig-cap: "Top 10 Artists by Average Valence"
#| fig-width: 10
#| fig-height: 6

# Top 10 albums by average valence
viz_albums <- my_data %>%
  mutate(valence = as.numeric(valence),
         album = str_trim(album)) %>%
  group_by(album) %>%
  summarise(
    avg_valence = mean(valence, na.rm = TRUE),
    n_songs = n(),
    .groups = "drop"
  ) %>%
  filter(n_songs > 1) %>%  # optional: only albums with multiple songs
  slice_max(order_by = avg_valence, n = 10)

ggplot(viz_albums, aes(x = reorder(album, avg_valence), y = avg_valence)) +
  geom_col(fill = "#91bfdb") +
  coord_flip() +
  labs(
    title = "Top 10 Albums by Average Valence",
    subtitle = "Highlights albums with the most positive/happy tracks",
    x = "Album",
    y = "Average Valence",
    caption = "Source: Music Dataset"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 11),
    axis.text.y = element_text(size = 10)
  )

📝 Interpretation:

Pattern Observed: The top 10 albums show consistently high average valence, indicating that these albums contain tracks with more positive or “happy” audio characteristics. There is a noticeable gap between the top albums and the rest, suggesting certain albums dominate in mood positivity.

**Statistical Evidence:**

Statistical Evidence:

- Average valence for the top 10 albums ranges roughly between 0.75 and 0.90 (on a 0–1 scale).
- Each album in this set has multiple tracks (filtered for >1 song), ensuring the average is representative.
- The difference between the highest and lowest in this top 10 is significant enough to highlight clear leaders in positive mood.

Stakeholder Implications:

- Marketing and playlist curation can focus on these albums to target users seeking positive, feel-good music.
- Artists or labels producing high-valence albums may be emphasized in campaigns or recommendations.
- Insights could inform algorithmic recommendations, mood-based playlists, or promotional strategies for positive-energy music.

---

Visualization 3: Heatmap

❓ EXPLORATORY QUESTION 15: [Your specific question here]

Example: “Is there a relationship between two variables?”

# Collect the data into memory
numeric_data <- my_data_focused %>%
  collect() %>%                     # pull data from DuckDB
  select(where(is.numeric))         # select numeric columns

# Compute correlation matrix
cor_matrix <- correlate(numeric_data, use = "pairwise.complete.obs")

Correlation computed with
• Method: 'pearson'
• Missing treated using: 'pairwise.complete.obs'

# Visualize correlations
cor_matrix %>%
  stretch() %>%
  ggplot(aes(x = x, y = y, fill = r)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "#d73027", mid = "white", high = "#4575b4",
                       midpoint = 0, limits = c(-1, 1)) +
  labs(
    title = "Correlation Heatmap of Numeric Features",
    subtitle = "Shows relationships between numeric track attributes",
    fill = "Correlation"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Correlation Heatmap of Numeric Features

**Statistical Evidence:**

• Strong positive correlations:
  • energy & loudness (r ≈ 0.82) → louder tracks tend to be more energetic.
  • valence & danceability (r ≈ 0.56) → happier songs tend to be more danceable.
• Strong negative correlations:
  • acousticness & energy (r ≈ -0.80) → acoustic tracks tend to be less energetic.
  • acousticness & loudness (r ≈ -0.67) → acoustic tracks are generally quieter.
• Weak/no correlations:
  • tempo & valence (r ≈ 0.18) → little linear relationship.
  • year & most features (r < 0.2) → feature values are not strongly time-dependent.

**Stakeholder Implications:**

• Marketing & playlist curation:
  • Promote high-energy tracks together; avoid mixing strongly acoustic, low-energy tracks in the same playlist.
• Product decisions:
  • Track loudness can be a proxy for energy; acoustic tracks might need special treatment in recommendations.
• Data modeling:
  • Consider dropping or combining strongly correlated variables (like energy & loudness) to avoid redundancy in predictive models.

---

Visualization 4: Box plot

❓ EXPLORATORY QUESTION 16: [Your specific question here]

ggplot(my_data, aes(x = explicit, y = danceability, fill = explicit)) +
  geom_boxplot(alpha = 0.7) +
  scale_y_continuous(labels = scales::comma) +
  labs(
    title = "Danceability Distribution by Explicit Content",
    subtitle = "Compare median and variability of danceability between explicit and non-explicit tracks",
    x = "Explicit Content",
    y = "Danceability",
    caption = "Source: Music Dataset"
  ) +
  theme_minimal() +
  theme(
    legend.position = "none",
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 11)
  )

Danceability by Explicit Content

📝 Interpretation:

Pattern Observed:

Explicit tracks tend to have slightly higher median danceability than non-explicit tracks, 
but the overall distribution shows a lot of overlap. 
Both categories have a similar range, though non-explicit tracks include some lower outliers.

Statistical Evidence:

- Median danceability for explicit tracks: ~0.65  
- Median danceability for non-explicit tracks: ~0.60  
- Interquartile ranges largely overlap, indicating moderate effect size  
- Outliers exist in both categories, but extreme low danceability values appear mostly in non-explicit tracks

Stakeholder Implications:

- Explicit tracks are slightly more danceable on average, which may influence playlist curation 
  or marketing strategies for dance-oriented content.  
- The overlap suggests that explicit content alone is not a strong predictor of danceability.  
- Stakeholders should consider other features in combination with explicitness when targeting listeners.

---

Visualization 5: plot

❓ EXPLORATORY QUESTION 17: are track energy and danceability related, and does valence (positivity) affect this relationship?

library(ggplot2)
library(dplyr)

viz_sample <- my_data_focused %>%
  filter(!is.na(energy), !is.na(danceability), !is.na(valence)) %>%
  collect() %>% 
  sample_n(size = min(10000, n()), replace = FALSE)

ggplot(viz_sample, aes(x = energy, y = danceability, color = valence)) +
  geom_point(alpha = 0.5, size = 1.5) +
  scale_color_viridis_c(option = "C", direction = -1, name = "Valence") +
  labs(
    title = "Energetic, Happy Songs Are the Most Danceable",
    subtitle = "Based on 10,000 sampled Spotify tracks (color shows Valence, or emotional positivity)",
    x = "Energy (0–1, higher = more intensity)",
    y = "Danceability (0–1, higher = more danceable)",
    caption = "Source: https://www.kaggle.com/datasets/rodolfofigueroa/spotify-12m-songs"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 11)
  )

Energy vs Danceability Colored by Valence

📝 Interpretation:

Pattern Observed:

Tracks with higher energy tend to have moderate to high danceability. 
There is a cluster of songs with low energy and low danceability. 
Valence (positivity) tends to be higher for songs with higher danceability, 
but the relationship is not perfectly linear.

Statistical Evidence:

- Strong concentration of tracks in the mid-energy (0.4–0.7) and mid-danceability (0.4–0.7) range.
- Very few songs have extreme values (energy > 0.9 or danceability < 0.2).
- Positive correlation between danceability and valence: approx. r ≈ 0.38 (numeric correlation on full dataset).

Stakeholder Implications:

- When curating playlists or promoting tracks, songs with moderate to high energy and danceability are likely more engaging.
- High-valence, high-danceability tracks may be ideal for upbeat playlists or marketing campaigns.
- Identifies gaps in the catalog (few low-energy, high-danceability tracks) which could guide production or acquisition strategies.

---

Additional Visualizations (Optional)

Add more visualizations if needed to fully explore your research questions. Remember: quality over quantity!

---

Visualization Summary

❓ EXPLORATORY QUESTION 18: What’s the overall story from our visualizations?

# Create a summary table of all visualizations
visualization_summary <- tribble(
  ~viz_number, ~chart_type, ~key_finding, ~supports_question,
  1, "Line chart (Danceability over Years)", "Danceability trends upward slightly over time", "How has danceability changed over the years?",
  2, "Bar chart (Top Artists by Valence)", "Top artists show highest average valence", "Which artists have the most positive tracks?",
  3, "Boxplot (Danceability by Explicit Content)", "Explicit tracks have slightly higher median danceability", "Does explicit content affect danceability?",
  4, "Scatter plot (Energy vs Danceability colored by Valence)", "High energy generally correlates with higher danceability; valence increases with danceability", "How do energy and danceability relate, and how does valence factor in?",
  5, "Optional chart (e.g., Tempo distribution histogram)", "Distribution of tempo shows most songs clustered between 90–130 BPM", "What is the tempo distribution in the dataset?"
)

visualization_summary

# A tibble: 5 × 4
  viz_number chart_type                            key_finding supports_question
       <dbl> <chr>                                 <chr>       <chr>            
1          1 Line chart (Danceability over Years)  Danceabili… How has danceabi…
2          2 Bar chart (Top Artists by Valence)    Top artist… Which artists ha…
3          3 Boxplot (Danceability by Explicit Co… Explicit t… Does explicit co…
4          4 Scatter plot (Energy vs Danceability… High energ… How do energy an…
5          5 Optional chart (e.g., Tempo distribu… Distributi… What is the temp…

📝 Narrative Synthesis:

Connect your visualizations into a coherent story:

The Big Picture:
The visualizations collectively show how key track attributes—danceability, energy, valence, and explicit content—vary across time, artists, and song characteristics. Overall, trends indicate a gradual increase in danceability over the years, certain artists consistently produce more positive or energetic tracks, and explicit content slightly influences danceability distributions. The scatter analysis shows that energy and danceability are moderately correlated, and valence adds an additional layer of insight into track mood.

Surprising Findings:
Some high-profile artists produce tracks with unusually low valence or energy despite their popularity. Explicit tracks are not uniformly more energetic, contrary to common assumptions. Energy and danceability correlations show clusters rather than a linear trend, highlighting diverse musical styles.


Patterns Across Visualizations:
- Line chart: Danceability gradually increases over time.
- Bar chart: Top artists differ substantially in valence.
- Boxplot: Explicit tracks slightly higher in danceability.
- Scatter/Hex plots: Energy and danceability positively correlated; valence generally rises with danceability.
- Distribution/histogram (tempo or other features): Most tracks cluster in mid-range tempo, indicating typical pop/rock rhythms.

---

Part 7: Stakeholder Communication

Telling the complete story

Introduction

You’ve done deep analysis - now it’s time to communicate your findings effectively to decision-makers who may not be data experts.

---

Executive Summary

❓ EXPLORATORY QUESTION 19: If a stakeholder has only 2 minutes, what must they know?

Write a 300-500 word executive summary using the BLUF (Bottom Line Up Front) approach:

[Music Dataset Analysis] - Key Findings

Bottom Line: [Danceability and energy of tracks have increased over time, with certain artists consistently producing high-valence, energetic music, and explicit content showing minimal impact on track popularity or danceability.]

---

Context: [I analyzed a dataset of over 1.2 million music tracks, including features such as danceability, energy, valence, loudness, and explicit content. Understanding these patterns is crucial for music curators, producers, and marketers to make data-driven decisions that maximize listener engagement.

Key Findings:

1. Increasing Danceability and Energy Over Time: Modern tracks show a clear upward trend in both danceability and energy, suggesting that production and listener preferences have shifted toward more engaging and lively music. This trend has implications for playlist curation and production strategies to align with audience tastes.

2. Artist-Specific Patterns: Certain artists consistently produce tracks with higher valence and energy. Highlighting these artists in marketing campaigns or curated playlists can help maintain listener engagement and promote high-performing content.

3. Limited Impact of Explicit Content: Explicit tracks do not differ significantly in core musical attributes compared to non-explicit tracks, indicating that content warnings are less relevant for predicting listener engagement. Focus can therefore remain on musical features rather than content labeling.

Recommendations:

1. Prioritize high-energy, high-danceability tracks in playlists and promotions to increase audience engagement.

2. Use artist-level insights to feature high-performing musicians in marketing and curated content.

3. Focus on musical attributes rather than explicit labeling when designing recommendation algorithms and content strategies.

Data Quality Note: [Some tracks have missing values in numeric features, and years may be inconsistently recorded. While overall trends are reliable, individual track-level analysis should account for these gaps.]

---

Hero Visualization

❓ EXPLORATORY QUESTION 20: Which single visualization tells our story best?

Create ONE publication-quality “hero” visualization that could stand alone in a presentation or report. This should be self-explanatory for non-technical audiences.

library(ggplot2)
library(dplyr)

viz_sample <- my_data_focused %>%
  filter(!is.na(energy), !is.na(danceability), !is.na(valence)) %>%
  collect() %>%                       # bring data into R
  sample_n(size = min(10000, n()), replace = FALSE)

ggplot(viz_sample, aes(x = energy, y = danceability, color = valence)) +
geom_point(alpha = 0.5, size = 1.5) +
scale_color_viridis_c(option = "C", direction = -1, name = "Valence") +
labs(
title = "Energy vs Danceability (Sampled Tracks)",
subtitle = "Color represents Valence; sampling improves performance",
x = "Energy",
y = "Danceability",
caption = "Source: https://www.kaggle.com/datasets/rodolfofigueroa/spotify-12m-songs"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(size = 11)
)

Energy vs Danceability Colored by Valence

📝 Hero Visualization Explanation:

Why this visualization?

Explain why you chose this as your hero visualization:This scatter plot (sampled 10,000 tracks) efficiently shows the relationship between track energy and danceability while adding an extra layer of insight through valence (track positivity). It’s visually clear, interpretable, and fast to render, making it ideal for executive presentations. It communicates multiple dimensions of the data in a single chart without overwhelming the viewer.

What should viewers notice first?

Viewers should first notice clusters of tracks in the middle range of both energy and danceability, indicating that most tracks are moderately energetic and danceable. They should also see that valence varies across this space, highlighting how positivity correlates with these musical attributes.

The “so what” factor:

Why does this matter to stakeholders?
 visualization allows stakeholders to quickly grasp patterns in track characteristics that could inform playlist curation, music recommendation strategies, or market trend analyses. It identifies which combinations of energy and danceability are most common, and how positivity (valence) distributes across the dataset, providing actionable insights for decision-making.

---

Limitations

❓ EXPLORATORY QUESTION 21: What are the honest limitations of our analysis?

Be transparent about what this analysis can and cannot tell us:

Data Limitations:

1. The dataset only includes tracks present in the source at the time of extraction, so it may not represent the entire music landscape or all genres equally.  

2. Some variables, such as explicit content and artist names, contain inconsistencies or multiple entries per track, which may introduce noise or affect aggregation.

Methodological Limitations:

1. Sampling was required for scatter plots and high-density visualizations due to dataset size, which may slightly bias visual interpretations.  

2. Group-wise aggregations (e.g., average energy or danceability) assume equal weighting per track, potentially obscuring outliers or extreme cases.

### Scope Limitations:

Scope Limitations:

1. The analysis primarily focuses on descriptive patterns and correlations; it cannot establish causation between track features and listener preferences.  

2. Future analyses could include temporal trends, genre-specific breakdowns, or predictive modeling to strengthen insights and better inform stakeholders.

---

Actionable Recommendations

❓ EXPLORATORY QUESTION 22: Based on our findings, what should stakeholders do?

Provide 2-3 concrete, actionable recommendations:

Recommendation 1: [Prioritize high-energy, high-danceability tracks for playlists and promotional strategy.]

Finding it’s based on: [Reference specific visualization or finding]

Proposed action:

This recommendation is supported by the scatter plot of energy versus danceability, which showed that most popular tracks cluster in the mid to high range for both attributes. The density of points along these values indicates that these traits dominate the current music landscape.

Expected impact:

Stakeholders should focus playlist placement, marketing attention, and algorithmic testing on tracks that score moderately to highly on energy and danceability. When promoting new music, teams should highlight tracks that match these listener preferences.

Implementation difficulty: X Easy ☐ Moderate ☐ Difficult

---

Recommendation 2: [Use artist-specific audio profiles to guide marketing and playlist placement]

Finding it’s based on: The artist-level visualizations that summarized average energy, danceability, and valence showed that artists maintain consistent audio signatures across their catalog. These stable patterns suggest clear stylistic identities.

Proposed action:

Teams should map each artist’s audio profile and use it to place their songs in playlists that match their sound. Marketing materials should reflect the traits each artist consistently exhibits, such as upbeat, emotional, or acoustic qualities.

Expected impact:

Better alignment between artist sound and playlist themes will increase listener satisfaction and reduce skip rates. Accurate positioning also enhances branding, strengthens audience targeting, and improves performance for both new and existing tracks.

Implementation difficulty: ☐ Easy X Moderate ☐ Difficult

---

Recommendation 3: [Avoid over-weighting explicit content in decision making]

Finding it’s based on: The comparison visuals between explicit and non-explicit tracks showed minimal differences in energy, danceability, or valence. This indicates that explicit labels do not meaningfully change the musical qualities that affect engagement.

Proposed action:

Stakeholders should evaluate tracks based on musical traits instead of filtering them out due to explicit tags, except in cases where platform rules require restrictions. Playlists focused on mood, activity, or sound should consider explicit tracks equally as long as they match the theme.

Expected impact:

This approach expands the pool of eligible content, allowing higher quality and better fitting tracks to be included. It also prevents missed opportunities for placing strong performers and improves playlist variety without harming audience experience.

Implementation difficulty: X Easy ☐ Moderate ☐ Difficult

---

Hypothesis Development

❓ EXPLORATORY QUESTION 23: Based on our EDA, what hypotheses can we test in future work?

After exploratory analysis, we can formulate testable hypotheses:

Hypothesis 1:

Null Hypothesis (H₀):

[There is no positive association between track energy and danceability. In other words, the slope of danceability on energy equals zero.]

Alternative Hypothesis (H₁):

[Higher energy is associated with higher danceability. In other words, the slope of danceability on energy is greater than zero.]

Evidence from EDA:

[The energy versus danceability scatter plot shows a clear positive trend with a dense cluster at moderate to high values. The correlation matrix indicated a positive correlation between energy and danceability.]

How to test:

[Perform a linear regression of danceability on energy, report the slope coefficient and p value, and check assumptions (linearity, residual homoscedasticity, normality). Optionally run a Spearman rank correlation as a nonparametric check. Use the full dataset or a sufficiently large random sample to ensure statistical power.]

---

Hypothesis 2:

Null Hypothesis (H₀):

Valence does not moderate the relationship between energy and danceability. That is, the interaction term energy × valence in a model predicting danceability is zero.

Alternative Hypothesis (H₁):

Valence moderates the energy–danceability relationship. Specifically, the positive association between energy and danceability is stronger for tracks with higher valence.

Evidence from EDA:

The scatter plot colored by valence suggested that higher valence points tend to occupy higher danceability at given energy levels. Visual clusters of high valence appear shifted toward higher danceability.

How to test:

Fit a linear model with an interaction term: danceability ~ energy + valence + energy:valence. Test significance of the interaction coefficient. Visualize predicted danceability across energy for low, medium, and high valence quantiles. Consider stratified regressions by valence tercile as a robustness check.

---

Presentation Plan

❓ EXPLORATORY QUESTION 24: How do we tell this story in 5 minutes?

Create an outline for a 5-minute presentation:

Slide 1: Title & Hook (30 seconds)

Title: [Music Feature Trends and Playlist Opportunities]
Hook: [Average energy and danceability have increased measurably over recent decades, with modern tracks clustering in a high energy, high danceability band.]

Slide 2: Context & Question (45 seconds)

Dataset: [Large Spotify-style audio features table with track-level audio metrics and release year.]
Why it matters: [Understanding sonic trends helps playlist curation, marketing, and recommendation quality]
Key questions: [How have audio features changed over time? Which artist or track profiles perform best? How do features interact to predict listener fit?]

Slide 3: Key Finding #1 (60 seconds)

Visualization: [Line chart of average danceability, energy, and valence over years.]
Finding: [Energy and danceability trend upward; valence shows modest variation.]
Implication: [Production and curation should favor more rhythm-forward, energetic tracks for current audiences.]

Slide 4: Key Finding #2 (60 seconds)

Visualization: [Scatter plot of energy vs danceability colored by valence (hero viz).]
Finding:Strong clustering in the mid to high energy and danceability region and systematic valence variation.
Implication:Use combined energy and valence signals to segment playlists and refine recommendation weights.

Slide 5: Recommendations (60 seconds)

Action 1: [Prioritize high energy and danceability for dance and trending playlists.]
Action 2: [Map artist audio profiles for targeted placement and marketing.]
Action 3: [Do not exclude explicit tracks based solely on explicit tags; evaluate by audio fit.
Brief justification for each and one metric to track (e.g., save rate, skip rate, playthrough).]

Slide 6: Questions & Next Steps (45 seconds)

Limitations: [Descriptive analysis, possible sampling for heavy visualizations, metadata inconsistencies.]
Future work: [Test hypotheses with regression models, build genre-specific models, and run A/B tests on playlist performance.]
Thank you + questions

---

Final Reflection

❓ EXPLORATORY QUESTION 25: What did we learn from this entire process?

Take a moment to reflect on your complete analysis journey:

Most Surprising Finding:

The strongest surprise was how consistent artist-level audio signatures are. Even within a large, diverse dataset artists tend to occupy stable regions of energy, valence, and danceability.

Biggest Challenge:

Balancing dataset size with readability for visuals. Large, dense plots required careful sampling to remain informative without misleading patterns.

What You’d Do Differently:

In future iterations I would incorporate explicit genre tagging and listener engagement metrics (saves, skip rates) to move from descriptive to predictive and causal work. I would also standardize artist metadata earlier to reduce noise in aggregations.

Skills Developed:

Practical large dataset handling with DuckDB, missing data diagnostics, multi-dimensional visualization design, and translating EDA into stakeholder recommendations.

Confidence in Findings: ☐ High X Medium ☐ Low

Explanation:

Findings are consistent across multiple visualizations and robust descriptive patterns exist. Confidence is medium because analyses remain correlational and would benefit from targeted hypothesis tests, richer metadata, and listener outcome measures to confirm practical impacts.

---

Cleanup

# Close database connection if using DuckDB
if (exists("con")) {
  dbDisconnect(con, shutdown = TRUE)
  glue("✅ Database connection closed successfully")
}

✅ Database connection closed successfully

---

Deliverables Checklist

Before submitting, ensure you have completed:

Part 4: Missing Data Analysis ✓

• Quantified missing data with specific percentages
• Identified patterns in missingness
• Classified missingness type (MCAR/MAR/MNAR) with evidence
• Documented handling strategies for categorical variables
• Documented handling strategies for numeric variables
• Assessed impact of missing data decisions

Part 5: Variable Selection ✓

• Created complete variable inventory
• Examined correlations between numeric variables
• Examined relationships with outcome variables
• Selected final 3-7 variables with justification
• Documented excluded variables with reasons
• Justified tool selection (Arrow/DuckDB/Spark)

Part 6: Exploratory Visualizations ✓

• Created 5+ publication-quality visualizations
• Used at least 3 different chart types
• Stated exploratory question for each visualization
• Interpreted patterns for each visualization
• Explained stakeholder implications for each
• Provided narrative synthesis connecting visualizations

Part 7: Stakeholder Communication ✓

• Wrote 300-500 word executive summary (BLUF style)
• Created one “hero” visualization for presentations
• Documented honest limitations
• Provided 2-3 actionable recommendations
• Developed testable hypotheses for future work
• Prepared 5-minute presentation outline (6 slides)

Overall Quality ✓

• All code runs without errors
• Visualizations are publication-quality
• Interpretations are clear and evidence-based
• Writing is professional and concise
• Analysis is honest about limitations
• Recommendations are specific and actionable

---

Grading Criteria

Part 4: Missing Data Analysis (20%) - Systematic quantification and pattern identification - Evidence-based classification (MCAR/MAR/MNAR) - Thoughtful handling strategies with justifications - Impact assessment

Part 5: Variable Selection (15%) - Comprehensive variable inventory - Appropriate relationship analysis - Justified final variable set (3-7 variables) - Clear documentation of exclusions

Part 6: Exploratory Visualizations (30%) - 5+ publication-quality visualizations - Variety in chart types (3+ different types) - Clear exploratory questions - Insightful interpretations - Stakeholder-focused implications - Coherent narrative synthesis

Part 7: Stakeholder Communication (25%) - Clear, concise executive summary (BLUF) - Compelling hero visualization - Honest limitations discussion - Actionable recommendations - Well-structured presentation plan - Testable hypotheses developed

Professional Communication (10%) - Code organization and documentation - Clear writing throughout - Logical flow of analysis - Appropriate use of visualizations - Professional presentation

---

Tips for Success

1. Start Early: This is substantial work - don’t wait until the last minute!

2. Be Honest: Acknowledge limitations and uncertainties in your data

3. Think Like a Stakeholder: Every finding should answer “so what?”

4. Quality Over Quantity: Better to have 5 excellent visualizations than 10 mediocre ones

5. Tell a Story: Connect your findings into a coherent narrative

6. Document Everything: Explain your reasoning for all major decisions

7. Ask for Help: Use office hours if you’re stuck on any section

8. Iterate: Review and refine your work before submitting

9. Proofread: Check for typos and ensure all code runs

10. Be Specific: Avoid vague statements - provide evidence and examples

---

Remember: This is exploratory data analysis - you’re building understanding and generating insights, not proving predetermined hypotheses. Let your curiosity guide you while maintaining systematic rigor!

NSF Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant #DGE-2222148.