April 2026
37
March 2026
819
Month-over-Month
-95.5%
Year-over-Year
-94.6%
Last updated on 2026-04-04
---
title: "Monthly Statistics"
description: "Detailed monthly listening patterns and year-over-year comparisons"
---
```{r setup}
#| message: false
#| warning: false
library(here)
source(here("src", "utils", "utils.R"))
load_common_libraries()
library(viridis)
```
```{r load-data}
#| message: false
#| warning: false
monthly_summary <- load_monthly_processed() %>%
mutate(
year = year(year_month),
month = month(year_month),
month_name = month(year_month, label = TRUE, abbr = FALSE)
)
current_month <- floor_date(Sys.Date(), "month")
previous_month <- current_month %m-% months(1)
```
## Current Month Overview
```{r value-boxes}
current_month_stats <- monthly_summary %>% filter(year_month == current_month)
previous_month_stats <- monthly_summary %>% filter(year_month == previous_month)
same_month_last_year <- monthly_summary %>%
filter(year == year(current_month) - 1, month == month(current_month))
current_total <- if (nrow(current_month_stats) > 0) current_month_stats$total_songs else 0
previous_total <- if (nrow(previous_month_stats) > 0) previous_month_stats$total_songs else 0
last_year_total <- if (nrow(same_month_last_year) > 0) same_month_last_year$total_songs else NA
mom_change <- if (previous_total > 0) round((current_total - previous_total) / previous_total * 100, 1) else NA
yoy_change <- if (!is.na(last_year_total) && last_year_total > 0) round((current_total - last_year_total) / last_year_total * 100, 1) else NA
current_rep <- if (nrow(current_month_stats) > 0) round(current_month_stats$repetition_kpi, 2) else NA
previous_rep <- if (nrow(previous_month_stats) > 0) round(previous_month_stats$repetition_kpi, 2) else NA
layout_column_wrap(
width = 1/4,
value_box(
title = format(current_month, "%B %Y"),
value = scales::comma(current_total),
showcase = bsicons::bs_icon("music-note-list"),
theme = "primary"
),
value_box(
title = format(previous_month, "%B %Y"),
value = scales::comma(previous_total),
showcase = bsicons::bs_icon("calendar3"),
theme = "secondary"
),
value_box(
title = "Month-over-Month",
value = if (!is.na(mom_change)) paste0(ifelse(mom_change >= 0, "+", ""), mom_change, "%") else "N/A",
showcase = bsicons::bs_icon(if (!is.na(mom_change) && mom_change >= 0) "graph-up-arrow" else "graph-down-arrow"),
theme = if (!is.na(mom_change) && mom_change >= 0) "success" else "danger"
),
value_box(
title = "Year-over-Year",
value = if (!is.na(yoy_change)) paste0(ifelse(yoy_change >= 0, "+", ""), yoy_change, "%") else "N/A",
showcase = bsicons::bs_icon(if (!is.na(yoy_change) && yoy_change >= 0) "graph-up-arrow" else "graph-down-arrow"),
theme = if (!is.na(yoy_change) && yoy_change >= 0) "success" else "danger"
)
)
```
## Monthly Trend
```{r monthly-trend}
#| fig-width: 14
#| fig-height: 6
p <- monthly_summary %>%
ggplot(aes(x = year_month, y = total_songs)) +
geom_smooth(method = "loess", color = "#FF6B6B", linewidth = 1, se = TRUE, alpha = 0.2) +
geom_line(color = "#1DB954", linewidth = 1.2) +
geom_point_interactive(
aes(
tooltip = paste0(
"<b>", format(year_month, "%B %Y"), "</b><br>",
"Songs: ", scales::comma(total_songs), "<br>",
"Unique: ", scales::comma(unique_songs), "<br>",
"Avg/day: ", avg_songs_per_day
),
data_id = as.character(year_month)
),
color = "#1DB954", size = 2.5
) +
labs(
title = "Monthly Song Count Trend",
subtitle = paste("From", format(min(monthly_summary$year_month), "%B %Y"),
"to", format(max(monthly_summary$year_month), "%B %Y")),
x = NULL,
y = "Total Songs",
caption = "Hover for details · Red band shows LOESS trend"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12, color = "gray60"),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
scale_x_date(date_labels = "%b %Y", date_breaks = "3 months") +
scale_y_continuous(labels = scales::comma)
make_girafe(p, height_svg = 5)
```
## Year-over-Year Comparison
```{r yoy-comparison}
#| fig-width: 14
#| fig-height: 6
yoy_data <- monthly_summary %>%
mutate(year = factor(year)) %>%
group_by(year, month, month_name) %>%
summarise(total_songs = sum(total_songs), .groups = "drop")
p <- yoy_data %>%
ggplot(aes(x = month, y = total_songs, color = year, group = year)) +
geom_line(linewidth = 1.2, alpha = 0.8) +
geom_point_interactive(
aes(
tooltip = paste0("<b>", month_name, " ", year, "</b><br>Songs: ", scales::comma(total_songs)),
data_id = paste(year, month, sep = "-")
),
size = 2.5
) +
scale_color_viridis_d(option = "plasma", name = "Year") +
scale_x_continuous(breaks = 1:12, labels = month.abb) +
labs(
title = "Year-over-Year Monthly Comparison",
subtitle = "Compare listening patterns across years",
x = "Month",
y = "Total Songs"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12, color = "gray60"),
legend.position = "bottom"
) +
scale_y_continuous(labels = scales::comma)
make_girafe(p, height_svg = 5)
```
## Monthly Heatmap
```{r monthly-heatmap}
#| fig-width: 14
#| fig-height: 8
heatmap_data <- monthly_summary %>%
mutate(
year = factor(year),
month_name = factor(month_name, levels = month.name)
)
p <- heatmap_data %>%
ggplot(aes(x = month_name, y = year, fill = total_songs)) +
geom_tile_interactive(
aes(
tooltip = paste0(
"<b>", month_name, " ", year, "</b><br>",
"Songs: ", scales::comma(total_songs), "<br>",
"Avg/day: ", avg_songs_per_day
),
data_id = paste(year, month_name, sep = "-")
),
color = "white", linewidth = 0.5
) +
geom_text(aes(label = scales::comma(total_songs)), color = "white", size = 3.5, fontface = "bold") +
scale_fill_gradient2(
low = "#2E4057",
mid = "#1DB954",
high = "#FFD700",
midpoint = median(heatmap_data$total_songs),
name = "Songs"
) +
labs(
title = "Monthly Listening Heatmap",
subtitle = "Total songs listened by year and month",
x = "Month",
y = "Year"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12, color = "gray60"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid = element_blank()
)
make_girafe(p, height_svg = 6)
```
## Monthly Performance Ranking
```{r monthly-ranking}
#| fig-width: 14
#| fig-height: 8
monthly_ranking <- monthly_summary %>%
arrange(desc(total_songs)) %>%
head(15) %>%
mutate(
label = format(year_month, "%b %Y"),
label = factor(label, levels = rev(label))
)
p <- monthly_ranking %>%
ggplot(aes(x = label, y = total_songs, fill = total_songs)) +
geom_col_interactive(
aes(
tooltip = paste0(
"<b>", label, "</b><br>",
"Songs: ", scales::comma(total_songs), "<br>",
"Unique: ", scales::comma(unique_songs), "<br>",
"Repetition KPI: ", repetition_kpi
),
data_id = as.character(label)
),
alpha = 0.9
) +
geom_text(aes(label = scales::comma(total_songs)), hjust = -0.1, size = 3.5) +
coord_flip() +
scale_fill_gradient(low = "#4A90E2", high = "#1DB954", guide = "none") +
labs(
title = "Top 15 Months by Total Songs",
subtitle = "Highest listening months in your history",
x = NULL,
y = "Total Songs"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12, color = "gray60")
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.15)), labels = scales::comma)
make_girafe(p, height_svg = 6)
```
## Seasonal Patterns
```{r seasonal-patterns}
#| fig-width: 14
#| fig-height: 6
seasonal_data <- monthly_summary %>%
group_by(month, month_name) %>%
summarise(
avg_songs = mean(total_songs),
median_songs = median(total_songs),
min_songs = min(total_songs),
max_songs = max(total_songs),
n_years = n(),
.groups = "drop"
) %>%
mutate(month_name = factor(month_name, levels = month.name))
p <- seasonal_data %>%
ggplot(aes(x = month_name)) +
geom_ribbon(aes(ymin = min_songs, ymax = max_songs, group = 1), fill = "#1DB954", alpha = 0.2) +
geom_line(aes(y = avg_songs, group = 1), color = "#1DB954", linewidth = 1.5) +
geom_point_interactive(
aes(
y = avg_songs,
tooltip = paste0("<b>", month_name, "</b><br>Avg: ", round(avg_songs), "<br>Range: ", round(min_songs), "–", round(max_songs)),
data_id = paste0("avg-", month_name)
),
color = "#1DB954", size = 3
) +
geom_line(aes(y = median_songs, group = 1), color = "#FF6B6B", linewidth = 1.2, linetype = "dashed") +
geom_point_interactive(
aes(
y = median_songs,
tooltip = paste0("<b>", month_name, "</b><br>Median: ", round(median_songs)),
data_id = paste0("med-", month_name)
),
color = "#FF6B6B", size = 2.5
) +
labs(
title = "Seasonal Listening Patterns",
subtitle = "Average (green) and median (red dashed) songs by month · Shaded area shows min-max range",
x = "Month",
y = "Number of Songs"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12, color = "gray60"),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
scale_y_continuous(labels = scales::comma)
make_girafe(p, height_svg = 5)
```
## Monthly Statistics Table
```{r monthly-table}
#| message: false
monthly_summary %>%
arrange(desc(year_month)) %>%
mutate(year_month_str = format(year_month, "%Y-%m")) %>%
select(
year_month_str,
total_songs,
unique_songs,
unique_artists,
days_in_data,
avg_songs_per_day,
avg_songs_per_week,
repetition_kpi
) %>%
gt() %>%
tab_header(
title = "Monthly Listening Statistics",
subtitle = "Detailed breakdown of listening activity by month"
) %>%
cols_label(
year_month_str = "Month",
total_songs = "Total Songs",
unique_songs = "Unique Songs",
unique_artists = "Unique Artists",
days_in_data = "Days",
avg_songs_per_day = "Avg/Day",
avg_songs_per_week = "Avg/Week",
repetition_kpi = "Repetition KPI"
) %>%
fmt_number(columns = c(total_songs, unique_songs, unique_artists), decimals = 0) %>%
fmt_number(columns = c(avg_songs_per_day, avg_songs_per_week, repetition_kpi), decimals = 1) %>%
data_color(
columns = total_songs,
colors = scales::col_numeric(palette = c("#FFE5E5", "#1DB954"), domain = NULL)
) %>%
data_color(
columns = repetition_kpi,
colors = scales::col_numeric(palette = c("#E3F2FD", "#FF6B6B"), domain = NULL)
) %>%
opt_interactive(use_search = TRUE, use_filters = TRUE, page_size_default = 12)
```
## Month-over-Month Changes
```{r mom-changes}
#| fig-width: 14
#| fig-height: 6
mom_data <- monthly_summary %>%
arrange(year_month) %>%
mutate(
prev_total = lag(total_songs),
mom_change = (total_songs - prev_total) / prev_total * 100,
change_direction = ifelse(mom_change >= 0, "Increase", "Decrease")
) %>%
filter(!is.na(mom_change))
p <- mom_data %>%
ggplot(aes(x = year_month, y = mom_change, fill = change_direction)) +
geom_col_interactive(
aes(
tooltip = paste0(
"<b>", format(year_month, "%b %Y"), "</b><br>",
"Change: ", round(mom_change, 1), "%<br>",
"This month: ", scales::comma(total_songs), "<br>",
"Prev month: ", scales::comma(prev_total)
),
data_id = as.character(year_month)
),
alpha = 0.8
) +
geom_hline(yintercept = 0, color = "gray50", linewidth = 0.5) +
scale_fill_manual(values = c("Increase" = "#1DB954", "Decrease" = "#FF6B6B"), name = NULL) +
labs(
title = "Month-over-Month Change",
subtitle = "Percentage change compared to previous month",
x = "Month",
y = "Change (%)"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 12, color = "gray60"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom"
) +
scale_x_date(date_labels = "%b %Y", date_breaks = "3 months")
make_girafe(p, height_svg = 5)
```
---
*Last updated on `r Sys.Date()`*