Listening Summary

Overview of your Spotify listening patterns and trends

Total Days Tracked

857

Total Songs Listened

24,278

Avg Songs / Day

28.3

Date Range

Aug 08, 2023 – Apr 02, 2026

Overall Repetition KPI

1.04

Songs Listened

Repetition KPI

Repetition KPI = Total Songs / Unique Songs — A value of 1 means every listen was a new track. Higher values mean more replaying of the same songs. Lower is more exploratory.

Artist Diversity

Shannon Diversity measures how evenly your listening is spread across artists. Higher = more varied mix (no single artist dominates). Concentration Top-5 shows what fraction of plays comes from your top-5 artists — lower means a more diverse mix.

Last updated on 2026-04-04

--- title: "Listening Summary" description: "Overview of your Spotify listening patterns and trends" --- ```{r setup} #| message: false #| warning: false library(here) source(here("src", "utils", "utils.R")) load_common_libraries() ``` ```{r load-data} #| message: false #| warning: false daily <- load_daily_processed() weekly <- load_weekly_processed() monthly <- load_monthly_processed() ``` ```{r value-boxes} total_days <- nrow(daily) total_songs <- sum(daily$total_songs) avg_songs <- round(mean(daily$total_songs), 1) date_range <- paste(format(min(daily$date), "%b %d, %Y"), "–", format(max(daily$date), "%b %d, %Y")) total_unique <- sum(daily$unique_songs) overall_rep <- round(total_songs / n_distinct(daily$date), 1) layout_column_wrap( width = 1/3, value_box( title = "Total Days Tracked", value = scales::comma(total_days), showcase = bsicons::bs_icon("calendar-check"), theme = "success" ), value_box( title = "Total Songs Listened", value = scales::comma(total_songs), showcase = bsicons::bs_icon("music-note-list"), theme = "primary" ), value_box( title = "Avg Songs / Day", value = avg_songs, showcase = bsicons::bs_icon("bar-chart"), theme = "secondary" ) ) ``` ```{r value-boxes-2} layout_column_wrap( width = 1/2, value_box( title = "Date Range", value = date_range, showcase = bsicons::bs_icon("calendar-range"), theme = "dark" ), value_box( title = "Overall Repetition KPI", value = round(total_songs / sum(daily$unique_songs), 2), showcase = bsicons::bs_icon("arrow-repeat"), theme = "warning" ) ) ``` ## Songs Listened ::: {.panel-tabset} ### Daily ```{r daily-songs} #| fig-width: 12 #| fig-height: 5 daily_with_week <- daily %>% mutate(week = floor_date(date, "week")) %>% group_by(week) %>% mutate(weekly_avg = mean(total_songs)) %>% ungroup() p <- daily_with_week %>% ggplot(aes(x = date, y = total_songs)) + geom_line(color = "#1DB954", linewidth = 0.8, alpha = 0.6) + geom_line(aes(y = weekly_avg), color = "#4A90E2", linewidth = 1, alpha = 0.8) + geom_smooth(method = "loess", color = "#FF6B6B", linewidth = 1, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", format(date, "%b %d, %Y"), " ", "Songs: ", total_songs, " ", "Unique: ", unique_songs, " ", "Rep. KPI: ", repetition_kpi ), data_id = as.character(date) ), color = "#1DB954", size = 1.5, alpha = 0.5 ) + labs( title = "Daily Songs Listened", caption = "Green = daily · Blue = 7-day rolling avg · Red = LOESS trend", x = NULL, y = NULL ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "6 months") + scale_y_continuous(breaks = scales::pretty_breaks(n = 6)) make_girafe(p, height_svg = 5) ``` ### Weekly ```{r weekly-songs} #| fig-width: 12 #| fig-height: 5 p <- weekly %>% ggplot(aes(x = week_start, y = total_songs)) + geom_line(color = "#1DB954", linewidth = 1) + geom_smooth(method = "loess", color = "#FF6B6B", linewidth = 1, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", year_week, " ", "Week of ", format(week_start, "%b %d, %Y"), " ", "Total songs: ", scales::comma(total_songs), " ", "Avg/day: ", avg_songs_per_day, " ", "Unique: ", scales::comma(unique_songs) ), data_id = year_week ), color = "#1DB954", size = 2 ) + labs( title = "Weekly Songs Listened", caption = "Each point is one week · Red band = LOESS trend", x = NULL, y = "Total Songs" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "6 months") + scale_y_continuous(labels = scales::comma, breaks = scales::pretty_breaks(n = 6)) make_girafe(p, height_svg = 5) ``` ### Monthly ```{r monthly-songs} #| fig-width: 12 #| fig-height: 5 p <- monthly %>% ggplot(aes(x = year_month, y = total_songs)) + geom_line(color = "#1DB954", linewidth = 1.2) + geom_smooth(method = "loess", color = "#FF6B6B", linewidth = 1, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", format(year_month, "%B %Y"), " ", "Total songs: ", scales::comma(total_songs), " ", "Unique: ", scales::comma(unique_songs), " ", "Avg/day: ", avg_songs_per_day, " ", "Avg/week: ", avg_songs_per_week ), data_id = as.character(year_month) ), color = "#1DB954", size = 3 ) + labs( title = "Monthly Song Count Trend", caption = "Each point is one month · Red band = LOESS trend", x = NULL, y = "Total Songs" ) + scale_y_continuous(limits = c(0, NA), labels = scales::comma) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "3 months", date_minor_breaks = "1 month") make_girafe(p, height_svg = 5) ``` ::: ## Repetition KPI > **Repetition KPI = Total Songs / Unique Songs** — A value of 1 means every listen was a new track. Higher values mean more replaying of the same songs. Lower is more exploratory. ::: {.panel-tabset} ### Daily ```{r daily-repetition} #| fig-width: 12 #| fig-height: 5 p <- daily %>% ggplot(aes(x = date, y = repetition_kpi)) + geom_line(color = "#FF6B6B", linewidth = 0.6, alpha = 0.5) + geom_smooth(method = "loess", color = "#4A90E2", linewidth = 1.2, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", format(date, "%b %d, %Y"), " ", "Rep. KPI: ", repetition_kpi, " ", "Total: ", total_songs, " · Unique: ", unique_songs ), data_id = as.character(date) ), color = "#FF6B6B", size = 1.5, alpha = 0.5 ) + geom_hline(yintercept = 1, linetype = "dashed", color = "gray50", linewidth = 0.8) + labs( title = "Daily Repetition KPI", caption = "Dashed line at 1 = no repetition · Blue = LOESS trend", x = NULL, y = "Repetition KPI" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "6 months") make_girafe(p, height_svg = 5) ``` ### Weekly ```{r weekly-repetition} #| fig-width: 12 #| fig-height: 5 p <- weekly %>% ggplot(aes(x = week_start, y = repetition_kpi)) + geom_line(color = "#FF6B6B", linewidth = 1) + geom_smooth(method = "loess", color = "#4A90E2", linewidth = 1.2, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", year_week, " ", "Rep. KPI: ", repetition_kpi, " ", "Total: ", scales::comma(total_songs), " · Unique: ", scales::comma(unique_songs) ), data_id = year_week ), color = "#FF6B6B", size = 2.5 ) + geom_hline(yintercept = 1, linetype = "dashed", color = "gray50", linewidth = 0.8) + labs( title = "Weekly Repetition KPI", caption = "Dashed line at 1 = no repetition · Blue = LOESS trend", x = NULL, y = "Repetition KPI" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "6 months") make_girafe(p, height_svg = 5) ``` ### Monthly ```{r monthly-repetition} #| fig-width: 12 #| fig-height: 5 latest_month_rep <- monthly %>% slice_tail(n = 1) %>% pull(repetition_kpi) p <- monthly %>% ggplot(aes(x = year_month, y = repetition_kpi)) + geom_line(color = "#FF6B6B", linewidth = 1.2) + geom_smooth(method = "loess", color = "#4A90E2", linewidth = 1.2, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", format(year_month, "%B %Y"), " ", "Rep. KPI: ", repetition_kpi, " ", "Total: ", scales::comma(total_songs), " · Unique: ", scales::comma(unique_songs) ), data_id = as.character(year_month) ), color = "#FF6B6B", size = 3 ) + geom_hline(yintercept = 1, linetype = "dashed", color = "gray50", linewidth = 0.8) + labs( title = "Monthly Repetition KPI", caption = "Dashed line at 1 = no repetition · Blue = LOESS trend", x = NULL, y = "Repetition KPI" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "3 months") make_girafe(p, height_svg = 5) ``` ::: ## Artist Diversity > **Shannon Diversity** measures how evenly your listening is spread across artists. Higher = more varied mix (no single artist dominates). **Concentration Top-5** shows what fraction of plays comes from your top-5 artists — lower means a more diverse mix. ::: {.panel-tabset} ### Weekly ```{r weekly-diversity} #| fig-width: 12 #| fig-height: 5 p <- weekly %>% ggplot(aes(x = week_start, y = shannon_diversity)) + geom_line(color = "#4A90E2", linewidth = 1) + geom_smooth(method = "loess", color = "#FF6B6B", linewidth = 1.2, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", year_week, " ", "Shannon diversity: ", round(shannon_diversity, 2), " ", "Top-5 concentration: ", scales::percent(concentration_top5, accuracy = 1), " ", "Unique artists: ", unique_artists ), data_id = year_week ), color = "#4A90E2", size = 2 ) + labs( title = "Weekly Shannon Diversity Index", caption = "Higher = more evenly spread across artists · Red = LOESS trend", x = NULL, y = "Shannon Diversity" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "6 months") make_girafe(p, height_svg = 5) ``` ### Monthly ```{r monthly-diversity} #| fig-width: 12 #| fig-height: 5 p <- monthly %>% ggplot(aes(x = year_month, y = shannon_diversity)) + geom_line(color = "#4A90E2", linewidth = 1.2) + geom_smooth(method = "loess", color = "#FF6B6B", linewidth = 1.2, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", format(year_month, "%B %Y"), " ", "Shannon diversity: ", round(shannon_diversity, 2), " ", "Top-5 concentration: ", scales::percent(concentration_top5, accuracy = 1), " ", "Unique artists: ", unique_artists ), data_id = as.character(year_month) ), color = "#4A90E2", size = 3 ) + labs( title = "Monthly Shannon Diversity Index", caption = "Higher = more evenly spread across artists · Red = LOESS trend", x = NULL, y = "Shannon Diversity" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "3 months") make_girafe(p, height_svg = 5) ``` ### Top-5 Concentration ```{r monthly-concentration} #| fig-width: 12 #| fig-height: 5 p <- monthly %>% ggplot(aes(x = year_month, y = concentration_top5)) + geom_line(color = "#FFD700", linewidth = 1.2, alpha = 0.7) + geom_smooth(method = "loess", color = "#FF6B6B", linewidth = 1.2, se = TRUE, alpha = 0.2) + geom_point_interactive( aes( tooltip = paste0( "", format(year_month, "%B %Y"), " ", "Top-5 concentration: ", scales::percent(concentration_top5, accuracy = 1), " ", "Shannon diversity: ", round(shannon_diversity, 2) ), data_id = as.character(year_month) ), color = "#FFD700", size = 3 ) + scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + labs( title = "Monthly Top-5 Artist Concentration", caption = "Lower = more diverse listening · Red = LOESS trend", x = NULL, y = "Share of plays by top 5 artists" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank() ) + scale_x_date(date_labels = "%b %Y", date_breaks = "3 months") make_girafe(p, height_svg = 5) ``` ::: --- *Last updated on `r Sys.Date()`*