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_oat: numeric, zone operative temperature [°C]
# ------------------------------------------------------------
# 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),
living_oat = 21 + 5.5 * annual + 2.5 * diurnal + rnorm(n(), 0, 0.6)
)
# ------------------------------------------------------------
# PLOT
# ------------------------------------------------------------
ep_synth %>%
ggplot(aes(x = doy, y = hour_day, fill = living_oat)) +
geom_tile() +
scale_fill_gradientn(
colours = c("#2166AC", "#74ADD1", "#FEE090", "#F46D43", "#A50026"),
name = "OT",
limits = c(14, 32),
oob = squish,
breaks = seq(14, 32, by = 3),
labels = function(x) paste0(x, "°C / ", round(x * 9 / 5 + 32), "°F")
) +
geom_tile(
data = . %>% filter(living_oat > 28),
fill = NA, colour = "#F5F3F3", linewidth = 0.1, alpha = 0.6
) +
geom_tile(
data = . %>% filter(living_oat < 18),
fill = NA, colour = "#5C5C5F", 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 operative temperature (OT) heatmap — Living zone",
subtitle = "White-outlined cells exceed 28 °C (overheating risk) · Grey-outlined cells below 18 °C (underheating risk)",
x = "Month",
y = "Hour of day"
) +
theme_ep() +
theme(
panel.grid = element_blank(),
legend.position = "right",
legend.key.height = unit(1.5, "cm")
)
