processed_data |>
group_by(date, peak) |>
summarise(total = sum(consumption, na.rm = TRUE)) |>
filter(date > "2024-08-31") |>
ggplot(aes(x = date, y = total, colour = peak)) +
geom_point() +
geom_smooth(method = "gam", se = FALSE, aes(fill = peak), alpha = 0.2) +
labs(
title = "Electricity Consumption",
x = "Date",
y = "Consumption (kWh)",
fill = "Peak"
) +
scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "1 month") +
theme(axis.text.x= element_text(angle = 90, hjust = 1, size = 10),
title = element_text(size = 14, face = "bold"),
legend.position = "bottom",
plot.title.position = "plot") +
geom_vline(xintercept = as.Date(c("2025-05-18", "2025-04-29")), linetype = "dashed", color = "black") +
facet_wrap(~ peak, scales = "free")
#> `summarise()` has grouped output by 'date'. You can override using the
#> `.groups` argument.
#> `geom_smooth()` using formula = 'y ~ s(x, bs = "cs")'
off_peak <- processed_data |>
filter(date > "2024-08-31") |>
group_by(year = year(date), month = month(date), peak) |>
reframe(mon_cons = sum(consumption)) |>
pivot_wider(names_from = peak, values_from = mon_cons) |>
mutate(summer = mean(peak[month %in% c(4:9)]),
winter = mean(peak[month %in% c(10:12, 1:3)]),
summer1 = mean(off_peak[month %in% c(4:9)]),
winter1 = mean(off_peak[month %in% c(10:12, 1:3)]),
period = lubridate::my(paste0(month,"-", year))) |>
ggplot(aes(x = period, y = off_peak)) +
geom_point() +
geom_smooth(method = "gam", se = FALSE, alpha = 0.2) +
geom_line(aes(y = summer1), colour = "red") +
geom_line(aes(y = winter1), colour = "blue") +
labs(
title = "Electricity Consumption",
x = "Date",
y = "Consumption (kWh)",
fill = "Peak"
) +
scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "1 month") +
theme(axis.text.x= element_text(angle = 90, hjust = 1, size = 10),
title = element_text(size = 14, face = "bold"),
legend.position = "bottom",
plot.title.position = "plot") +
geom_vline(xintercept = as.Date(c("2025-05-18", "2025-04-29")), linetype = "dashed", color = "black")
peak <- processed_data |>
filter(date > "2024-08-31") |>
group_by(year = year(date), month = month(date), peak) |>
reframe(mon_cons = sum(consumption)) |>
pivot_wider(names_from = peak, values_from = mon_cons) |>
mutate(summer = mean(peak[month %in% c(4:9)]),
winter = mean(peak[month %in% c(10:12, 1:3)]),
summer1 = mean(off_peak[month %in% c(4:9)]),
winter1 = mean(off_peak[month %in% c(10:12, 1:3)]),
period = lubridate::my(paste0(month,"-", year))) |>
ggplot(aes(x = period, y = peak)) +
geom_point() +
geom_smooth(method = "gam", se = FALSE, aes(fill = peak), alpha = 0.2) +
geom_line(aes(y = summer), colour = "red") +
geom_line(aes(y = winter), colour = "blue") +
labs(
title = "Electricity Consumption",
x = "Date",
y = "Consumption (kWh)",
fill = "Peak"
) +
scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "1 month") +
theme(axis.text.x= element_text(angle = 90, hjust = 1, size = 10),
title = element_text(size = 14, face = "bold"),
legend.position = "bottom",
plot.title.position = "plot") +
geom_vline(xintercept = as.Date(c("2025-05-18", "2025-04-29")), linetype = "dashed", color = "black")
library(patchwork)
off_peak + peak + plot_layout(ncol = 1)
#> `geom_smooth()` using formula = 'y ~ s(x, bs = "cs")'
#> `geom_smooth()` using formula = 'y ~ s(x, bs = "cs")'
#> Warning: The following aesthetics were dropped during statistical transformation: fill.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
