Peak Listening Hour
4:00–5:00
Avg Sessions / Day
3.6
Avg Songs / Session
8
Peak Session Start
14:00
Average songs played per hour slot, aggregated across all days. Brighter = more listening.
Last updated on 2026-04-04
---
title: "Intraday Patterns"
description: "When do you listen? Hour-of-day patterns and listening session analysis"
---
```{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
intraday <- load_intraday_hourly()
sessions <- load_sessions()
# All-time average plays by hour × weekday
hourly_weekday <- intraday %>%
mutate(weekday = wday(date, label = TRUE, abbr = TRUE, week_start = 1)) %>%
group_by(weekday, hour) %>%
summarise(avg_plays = round(mean(total_plays), 2), .groups = "drop") %>%
mutate(weekday = factor(weekday, levels = c("Mon","Tue","Wed","Thu","Fri","Sat","Sun")))
# Aggregate by hour across all days
hourly_overall <- intraday %>%
group_by(hour) %>%
summarise(
avg_plays = round(mean(total_plays), 2),
total_plays = sum(total_plays),
.groups = "drop"
)
peak_hour <- hourly_overall$hour[which.max(hourly_overall$avg_plays)]
peak_label <- paste0(peak_hour, ":00–", peak_hour + 1, ":00")
avg_sessions <- round(nrow(sessions) / n_distinct(sessions$date), 1)
avg_session_songs <- round(mean(sessions$session_songs), 1)
peak_session_hour <- sessions %>%
count(session_start_hour) %>%
slice_max(n, n = 1) %>%
pull(session_start_hour)
```
## Listening Overview
```{r listening-value-boxes}
layout_column_wrap(
width = 1/4,
value_box(
title = "Peak Listening Hour",
value = peak_label,
showcase = bsicons::bs_icon("clock"),
theme = "primary"
),
value_box(
title = "Avg Sessions / Day",
value = avg_sessions,
showcase = bsicons::bs_icon("play-circle"),
theme = "success"
),
value_box(
title = "Avg Songs / Session",
value = avg_session_songs,
showcase = bsicons::bs_icon("music-note-list"),
theme = "secondary"
),
value_box(
title = "Peak Session Start",
value = paste0(peak_session_hour, ":00"),
showcase = bsicons::bs_icon("lightning"),
theme = "info"
)
)
```
## Hour × Weekday Heatmap
> Average songs played per hour slot, aggregated across all days. Brighter = more listening.
```{r heatmap}
#| fig-width: 14
#| fig-height: 5
p <- hourly_weekday %>%
ggplot(aes(x = hour, y = weekday, fill = avg_plays)) +
geom_tile_interactive(
aes(
tooltip = paste0(
"<b>", weekday, " at ", hour, ":00</b><br>",
"Avg plays: ", avg_plays
),
data_id = paste(weekday, hour, sep = "-")
),
color = "white", linewidth = 0.4
) +
scale_fill_viridis_c(option = "plasma", name = "Avg Plays") +
scale_x_continuous(
breaks = c(0, 6, 9, 12, 15, 18, 21),
labels = c("00:00", "06:00", "09:00", "12:00", "15:00", "18:00", "21:00")
) +
labs(
title = "Listening Heatmap — Hour of Day × Day of Week",
subtitle = "All-time average songs played per hour slot",
x = "Hour of Day",
y = NULL
) +
theme_minimal() +
theme(
plot.title = element_text(size = 15, face = "bold"),
plot.subtitle = element_text(size = 11, color = "gray60"),
panel.grid = element_blank()
)
make_girafe(p, height_svg = 4)
```
## Average Plays by Hour of Day
```{r hourly-line}
#| fig-width: 12
#| fig-height: 5
p <- hourly_overall %>%
ggplot(aes(x = hour, y = avg_plays)) +
geom_area(fill = "#1DB954", alpha = 0.2) +
geom_line(color = "#1DB954", linewidth = 1.3) +
geom_point_interactive(
aes(
tooltip = paste0("<b>", hour, ":00</b><br>Avg plays: ", avg_plays),
data_id = as.character(hour)
),
color = "#1DB954", size = 3
) +
geom_vline(xintercept = peak_hour, linetype = "dashed", color = "#FF6B6B", linewidth = 0.8) +
annotate("text", x = peak_hour + 0.4, y = max(hourly_overall$avg_plays) * 0.95,
label = paste0("Peak: ", peak_label), color = "#FF6B6B", size = 3.5, hjust = 0) +
scale_x_continuous(breaks = seq(0, 23, 3), labels = paste0(seq(0, 23, 3), ":00")) +
labs(
title = "Average Songs by Hour of Day",
caption = "All-time average across all days",
x = "Hour",
y = "Avg Songs"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
panel.grid.minor = element_blank()
)
make_girafe(p, height_svg = 5)
```
## Listening Sessions
### Session Length Distribution
```{r session-distribution}
#| fig-width: 12
#| fig-height: 5
session_bins <- sessions %>%
mutate(
length_group = case_when(
session_songs == 1 ~ "1 song",
session_songs <= 3 ~ "2–3 songs",
session_songs <= 7 ~ "4–7 songs",
session_songs <= 15 ~ "8–15 songs",
session_songs <= 30 ~ "16–30 songs",
TRUE ~ "30+ songs"
),
length_group = factor(length_group,
levels = c("1 song", "2–3 songs", "4–7 songs", "8–15 songs", "16–30 songs", "30+ songs"))
) %>%
count(length_group)
p <- session_bins %>%
ggplot(aes(x = length_group, y = n, fill = length_group)) +
geom_col_interactive(
aes(
tooltip = paste0("<b>", length_group, "</b><br>Sessions: ", scales::comma(n),
"<br>", round(n / sum(n) * 100, 1), "% of total"),
data_id = as.character(length_group)
),
alpha = 0.85
) +
geom_text(aes(label = scales::comma(n)), vjust = -0.4, size = 3.5) +
scale_fill_viridis_d(option = "plasma", guide = "none") +
scale_y_continuous(expand = expansion(mult = c(0, 0.12))) +
labs(
title = "Session Length Distribution",
subtitle = paste0("Total sessions: ", scales::comma(nrow(sessions)), " · Avg: ", avg_session_songs, " songs"),
x = "Session Length",
y = "Number of Sessions"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 11, color = "gray60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank()
)
make_girafe(p, height_svg = 5)
```
### When Sessions Start
```{r session-start-hours}
#| fig-width: 12
#| fig-height: 5
session_start_dist <- sessions %>%
count(session_start_hour, name = "sessions")
p <- session_start_dist %>%
ggplot(aes(x = session_start_hour, y = sessions)) +
geom_col_interactive(
aes(
tooltip = paste0("<b>", session_start_hour, ":00</b><br>Sessions started: ", sessions),
data_id = as.character(session_start_hour),
fill = sessions
),
alpha = 0.9
) +
scale_fill_gradient(low = "#4A90E2", high = "#1DB954", guide = "none") +
scale_x_continuous(breaks = seq(0, 23, 3), labels = paste0(seq(0, 23, 3), ":00")) +
labs(
title = "Session Start Times",
subtitle = "How many sessions begin at each hour of the day",
x = "Hour of Day",
y = "Number of Sessions"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 11, color = "gray60"),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank()
)
make_girafe(p, height_svg = 5)
```
### Monthly Session Trends
```{r monthly-sessions}
#| fig-width: 12
#| fig-height: 5
monthly_sessions <- sessions %>%
mutate(year_month = floor_date(date, "month")) %>%
group_by(year_month) %>%
summarise(
total_sessions = n(),
avg_session_songs = round(mean(session_songs), 1),
.groups = "drop"
)
p <- monthly_sessions %>%
ggplot(aes(x = year_month, y = total_sessions)) +
geom_line(color = "#4A90E2", linewidth = 1.2) +
geom_point_interactive(
aes(
tooltip = paste0(
"<b>", format(year_month, "%B %Y"), "</b><br>",
"Sessions: ", total_sessions, "<br>",
"Avg session songs: ", avg_session_songs
),
data_id = as.character(year_month)
),
color = "#4A90E2", size = 2.5
) +
scale_x_date(date_labels = "%b %Y", date_breaks = "3 months") +
labs(
title = "Monthly Session Count",
x = NULL,
y = "Sessions"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank()
)
make_girafe(p, height_svg = 5)
```
---
*Last updated on `r Sys.Date()`*