## -----------------------------------------------------------------------------
#| label: setup
library(metrosp)
library(dplyr)


## -----------------------------------------------------------------------------
library(sf)

rail_lines


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metro_colors


## -----------------------------------------------------------------------------
glimpse(line_entries_monthly)


## -----------------------------------------------------------------------------
#| eval: false
# # Latest published data
# entrance <- read_metro_demand("line_entries_monthly")
# 
# # The month's published batch, named in the analysis that used it
# entrance_sep <- read_metro_demand("line_entries_monthly", vintage = "2026-09")


## -----------------------------------------------------------------------------
#| code-fold: true
library(ggplot2)

theme_series <- theme_minimal(base_family = "Avenir", base_size = 10) +
  theme(
    panel.background = element_rect(fill = "#f5f5f5"),
    plot.background = element_rect(fill = "#f5f5f5"),
    plot.margin = margin(20, 10, 20, 10),
    plot.title = element_text(family = "Lora", size = 14),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.25),
    axis.title.x = element_blank(),
    axis.line.x = element_line(color = "gray10", linewidth = 0.5),
    axis.ticks.x = element_line(color = "gray10", linewidth = 0.5),
    strip.background = element_rect(fill = "#0D1B2A"),
    strip.text = element_text(color = "#ffffff"),
    legend.position = "bottom"
  )


## -----------------------------------------------------------------------------
glimpse(line_entries_monthly)


## -----------------------------------------------------------------------------
total_entrance <- line_entries_monthly |>
  filter(metric == "total")


## -----------------------------------------------------------------------------
#| code-fold: true
ggplot(total_entrance, aes(x = date, y = value, color = line_name)) +
  geom_line(lwd = 0.8) +
  facet_wrap(vars(line_name), scales = "free_y") +
  scale_color_manual(values = metro_colors) +
  guides(color = "none") +
  labs(
    title = "Total Entrance by Line",
    subtitle = "Total monthly passenger entrances by metro line",
    x = NULL,
    y = "Total Entrance"
  ) +
  theme_series


## -----------------------------------------------------------------------------
glimpse(line_transported_monthly)


## -----------------------------------------------------------------------------
daily_avg <- line_transported_monthly |>
  filter(metric == "mdu")


## -----------------------------------------------------------------------------
#| code-fold: true
ggplot(daily_avg, aes(x = date, y = value, color = line_name)) +
  geom_line(lwd = 0.8) +
  facet_wrap(vars(line_name), scales = "free_y") +
  scale_color_manual(values = metro_colors) +
  labs(
    title = "Daily Average Passenger Transported by Line",
    subtitle = "Monthly averages across business days",
    x = NULL,
    y = "Daily Average"
  ) +
  guides(color = "none") +
  theme_series


## -----------------------------------------------------------------------------
glimpse(station_transported_monthly)


## -----------------------------------------------------------------------------
#| code-fold: true
line4st <- station_transported_monthly |>
  filter(line_number == 4)

ggplot(line4st, aes(x = date, y = value)) +
  geom_line(lwd = 0.8, color = metro_colors["Yellow"]) +
  facet_wrap(vars(station_name), scales = "free_y") +
  labs(
    x = NULL,
    y = "Average Passengers",
    title = "Passengers per Station (line 4)"
  ) +
  theme_series


## -----------------------------------------------------------------------------
glimpse(station_entries_daily)


## -----------------------------------------------------------------------------
#| code-fold: true
#| fig-width: 10
#| fig-height: 8
line4st_daily <- station_entries_daily |>
  filter(line_number == 4, year == 2023)

ggplot(line4st_daily, aes(x = date, y = value)) +
  geom_smooth(
    lwd = 0.8,
    color = metro_colors["Yellow"],
    method = "loess",
    span = 0.65
  ) +
  facet_wrap(vars(station_name), scales = "free_y", ncol = 3) +
  scale_x_date(date_breaks = "1 month", date_labels = "%b") +
  labs(
    title = "Passengers per Station (line 4, 2023)",
    subtitle = "LOESS smoothed trend",
    x = NULL,
    y = "Average Passengers"
  ) +
  theme_series