library(tidyverse)
library(scales)
source(here::here("src", "R", "x_theme.R"))
# ------------------------------------------------------------
# DATA REQUIREMENTS
# ------------------------------------------------------------
# Your data should be a dataframe with:
# - doy: integer, day of year (1–365)
# - hour_day: integer, hour of day (0–23)
# - living_rh: numeric, zone indoor relative humidity [%]
# ------------------------------------------------------------
# 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(
annual = -cos(2 * pi * doy / 365),
diurnal = sin(pi * hour_day / 12 - pi / 6),
# RH peaks in the cool/wet season; summer heating/drying lowers it
living_rh = 55 - 15 * annual - 3 * diurnal + rnorm(n(), 0, 2)
# range ≈ 25–80 %, triggering both the <40 % and >70 % thresholds
)
# ------------------------------------------------------------
# PLOT
# ------------------------------------------------------------
ep_synth %>%
ggplot(aes(x = doy, y = hour_day, fill = living_rh)) +
geom_tile() +
scale_fill_gradientn(
colours = c("#FFFFFF", "#C6DBEF", "#6BAED6", "#2171B5", "#084594"),
limits = c(15, 85),
oob = squish,
name = "RH (%)"
) +
geom_tile(
data = . %>% filter(living_rh < 40),
fill = NA, colour = "#4BE07D", linewidth = 0.1, alpha = 0.6
) +
geom_tile(
data = . %>% filter(living_rh > 70),
fill = NA, colour = "#F39704", linewidth = 0.1, alpha = 0.6
) +
scale_x_continuous(
breaks = c(0, cumsum(c(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31))),
labels = c(month.abb, ""),
expand = c(0, 0)
) +
scale_y_continuous(breaks = seq(0, 23, 6), expand = c(0, 0)) +
labs(
title = "Annual relative humidity heatmap — Living zone",
subtitle = "Orange-outlined cells exceed 70 % RH (mold risk) · Green-outlined cells below 40 % RH (dryness risk)",
x = "Month",
y = "Hour of day"
) +
theme_ep() +
theme(
panel.grid = element_blank(),
legend.position = "right",
legend.key.height = unit(1.5, "cm")
)
