library(tidyverse)
library(scales)
source(here::here("src", "R", "x_theme.R"))
# ------------------------------------------------------------
# DATA REQUIREMENTS
# ------------------------------------------------------------
# Your data should be a dataframe with:
# - set: numeric, Standard Effective Temperature [°C]
# - month_num: integer, month number (1–12)
# - occupied: integer, 0/1 occupancy indicator
# ------------------------------------------------------------
# SAMPLE DATA (replace with your own hourly timeseries)
set.seed(42)
ep_synth <- expand.grid(doy = 1:365, hour_day = 0:23) %>%
as_tibble() %>%
mutate(
month_num = as.integer(format(as.Date(doy - 1, origin = "2025-01-01"), "%m")),
annual = -cos(2 * pi * doy / 365),
diurnal = sin(pi * hour_day / 12 - pi / 6),
set = 23 + 2.5 * annual + 1.2 * diurnal + rnorm(n(), 0, 0.5),
occupied = as.integer(hour_day >= 7 & hour_day <= 22)
)
# Monthly SET breakdown (ASHRAE 55 comfort range: 22.2–25.6 °C)
monthly_comfort <- ep_synth %>%
filter(occupied > 0) %>%
mutate(
comfort_cat = factor(case_when(
set < 22.2 ~ "Cool discomfort",
set > 25.6 ~ "Warm discomfort",
TRUE ~ "Comfortable"
), levels = names(comfort_colours))
) %>%
count(month_num, comfort_cat) %>%
group_by(month_num) %>%
mutate(pct = n / sum(n) * 100) %>%
ungroup() %>%
mutate(month = factor(month.abb[month_num], levels = month.abb))
# ------------------------------------------------------------
# PLOT
# ------------------------------------------------------------
ggplot(monthly_comfort, aes(x = month, y = pct, fill = comfort_cat)) +
geom_col(width = 0.7) +
scale_fill_manual(values = comfort_colours, name = NULL) +
scale_y_continuous(labels = label_percent(scale = 1)) +
labs(
title = "Monthly SET comfort breakdown — Living zone",
subtitle = "% of occupied hours in each SET comfort category | ASHRAE 55 range: 22.2–25.6 °C",
x = NULL,
y = "% occupied hours"
) +
theme_ep()
