Code
<- relay_episodes |>
lastyear filter(year == year(now()) - 1) |>
nrow()
<- relay_episodes |>
thisyear filter(year == year(now())) |>
nrow()
<- round(thisyear / lastyear * 100, 1) progress
Lukas Burk
December 1, 6:22 (UTC)
After doing The Incomparable, I had to go see what I could do with Relay FM’s data as well, naturally.
So this is the beginning of that. The source code is included in this repo, and, in case anyone asks:
Yes, you can use these graphs for whatever. Maybe link back to me, that would be cool.
Please note that there’s probably still stuff coming.
Relay FM spans 48 shows and a combined runtime of over 364 days spread across 7956 episodes since 2014.
In 2023, there were 649 episodes published, and we’re currently about 78.9% of the way there at 512 episodes this year.
relay_episodes |>
#filter(year >= 2014) |>
group_by(show) |>
mutate(
d_min = min(date),
d_max = max(date)
) |>
ggplot(aes(y = reorder(show, d_min), color = show_status)) +
geom_errorbar(aes(xmin = d_min, xmax = d_max),
width = .5, linewidth = 2, alpha = .75
) +
geom_vline(xintercept = as.Date("2014-01-01"), linetype = "dashed") +
scale_x_date(
date_breaks = "2 years", date_labels = "%Y",
date_minor_breaks = "1 year"
) +
scale_y_discrete(position = "right") +
scale_color_brewer(palette = "Dark2") +
labs(
y = NULL, x = "First to Last Episode Date",
title = "Relay FM: Show Timelines",
#subtitle = "Since 2014",
caption = caption, color = "Status"
) +
theme(legend.position = "top")
relay_episodes |>
filter(year >= 2014) |>
group_by(show, year) |>
tally() |>
ggplot(aes(x = as.factor(year), weight = n, fill = show)) +
geom_bar(show.legend = FALSE) +
scale_y_continuous(
breaks = seq(0, 1e6, 200),
minor_breaks = seq(0, 1e6, 100)
) +
scale_fill_viridis_d(guide = guide_legend(ncol = 2, keyheight = 1.5)) +
labs(
x = "Year", y = "# of Episodes", fill = "Show",
title = "Relay FM: Episodes per Year",
subtitle = "One color per show, but too many shows for a legible legend, sorry.",
caption = caption
)
relay_episodes |>
mutate(
week = week(date),
year_num = year(date)
) |>
filter(year_num >= 2014) |>
group_by(year, week) |>
tally() |>
ggplot(aes(x = week, y = n)) +
geom_point() +
geom_smooth(method = lm, formula = y ~ x, se = FALSE, color = "red") +
scale_y_continuous(
breaks = seq(0, 100, 10),
minor_breaks = seq(0, 100, 5)
) +
scale_x_continuous(breaks = seq(0, 55, 16)) +
facet_wrap(~year) +
labs(
title = "Relay FM: Weekly Episode Output",
x = "Week of Year", y = "Episodes Published", caption = caption
)
relay_episodes |>
mutate(
week = isoweek(date),
year_num = year(date)
) |>
filter(year_num == current_year) |>
group_by(show, year, week) |>
tally() |>
ggplot(aes(x = week, y = n)) +
geom_col() +
# scale_y_continuous(breaks = seq(0, 10, 1)) +
# scale_x_continuous(breaks = seq(0, 60, 10)) +
facet_wrap(~show, ncol = 3, scales = "free") +
labs(
title = "Relay FM", subtitle = "Episodes per Week in the current year",
x = "Week of Year", y = "# fo Episodes", caption = caption
) +
theme(axis.text.y = element_text(size = rel(.5)))
relay_episodes |>
mutate(
month = month(date),
year_num = year(date)
) |>
filter(year_num >= 2014) |>
group_by(year, month) |>
tally() |>
ggplot(aes(x = month, y = n)) +
geom_point() +
geom_smooth(method = lm, formula = y ~ x, se = FALSE, color = "red") +
scale_y_continuous(
breaks = seq(0, 1e6, 10),
minor_breaks = seq(0, 1e6, 5)
) +
scale_x_continuous(
breaks = seq(0, 13, 2),
minor_breaks = seq(0, 13, 1)
) +
facet_grid(. ~ year, space = "free_x", scales = "free_x") +
labs(
title = "Relay FM: Monthly Episode Output",
x = "Month of Year", y = "Episodes Published", caption = caption
) +
theme(axis.text.x = element_text(size = rel(.9)))
relay_episodes |>
filter(year >= 2014, duration <= hms(hours = 4)) |>
ggplot(aes(x = date, y = duration, color = show)) +
geom_point(alpha = .5, show.legend = FALSE) +
scale_x_date(
date_breaks = "1 years",
date_labels = "%Y",
minor_breaks = NULL
) +
scale_y_time(
breaks = hms(hours = seq(0, 1e6, 1)),
limits = c(0, NA)
) +
scale_color_viridis_d(guide = guide_legend(ncol = 1, keyheight = .75, )) +
labs(
title = "Relay FM: Episode Durations of Every Episode Since 2014",
subtitle = "No legend, but one color per show. Only for episodes < 4 hours",
x = "Date Published", y = "Duration (HH:MM:SS)", color = "Show",
caption = caption
)
relay_episodes |>
filter(show != "B-Sides", year >= 2014) |>
ggplot(aes(x = date, y = duration)) +
geom_point() +
expand_limits(y = 0) +
scale_x_date(
breaks = date_breaks("12 months"),
minor_breaks = date_breaks("6 month"),
date_labels = "%Y"
) +
scale_y_time(
breaks = hms(minutes = seq(0, 1e6, 60))
) +
facet_wrap(~show, ncol = 5, scales = "free") +
labs(
title = "Relay FM", subtitle = "Episode Durations",
x = "Date Published", y = "Duration (HH:MM:SS)", caption = caption
) +
theme(axis.text.x = element_text(angle = 90, vjust = .5))
relay_episodes |>
filter(duration <= hms(hours = 3)) |>
ggplot(aes(x = duration)) +
geom_histogram(binwidth = 120, fill = "gray", color = "white") +
scale_x_time(
breaks = hms(minutes = seq(0, 1e6, 30))
) +
scale_y_continuous(breaks = seq(0, 600, 50)) +
labs(
title = "Relay FM: Episode Durations",
subtitle = "Limited to episodes under 3 hours",
y = "Episode Count", x = "Duration (HH:MM:SS)", caption = caption
)
relay_episodes |>
group_by(show) |>
ggplot(aes(x = forcats::fct_reorder(show, duration, .fun = mean), y = duration)) +
geom_boxplot() +
stat_summary(
fun = "mean", geom = "point", size = 2,
color = "black", fill = "red", shape = 21
) +
coord_flip() +
scale_y_time(
breaks = hms(minutes = seq(0, 1e6, 60))
) +
labs(
title = "Relay FM",
subtitle = "Episode Duration — Boxplot + Mean (red)",
x = "", y = "Duration (HH:MM:SS)", caption = caption
)
Sadly I don’t have a lot of data about people besides the hosts of each show. I could try to pry out the guests of shows out of each show’s RSS feed, but sadly this information is not neatly presented in the feed, but rather would require an amount of regexing I am not prepared, and therefore not able to do.
Be that as it may, here’s a bit about the hosts.
Sooo okay! Filtered by episodes airing in 2014 or later though.
relay_episodes |>
filter(year >= 2014) |>
gather_people() |>
group_by(person) |>
summarize(
n = n(),
duration = sum(duration, na.rm = TRUE),
first_show = min(date, na.rm = TRUE),
last_show = max(date), na.rm = TRUE,
weeks_active = first_show %--% last_show / dweeks(1),
duration_per_week = as_hms(duration / weeks_active),
.groups = "drop"
) ->
people_activity
ggplot(people_activity, aes(x = reorder(person, duration_per_week), y = duration_per_week)) +
geom_col() +
coord_flip() +
scale_y_time(
breaks = hms(minutes = seq(0, 1e6, 60)),
labels = \(x) str_extract(as.character(x), "\\d{2}:\\d{2}")
) +
labs(
title = "Relay FM",
subtitle = "Total duration per host divided by number of weeks active\n(First to latest appearance)",
x = "", y = "Average Duration (HH:MM:SS)",
caption = caption
)
ggplot(people_activity) +
geom_segment(aes(
x = forcats::fct_reorder(person, first_show, .desc = TRUE), xend = person,
y = first_show, yend = last_show
),
linewidth = 2, alpha = .75
) +
coord_flip() +
labs(
title = "Relay FM",
subtitle = "Period of hosting activity (first to most recent show)\nOrdered by first appearance",
x = "", y = "Date of show",
caption = caption
)