dft_traffic_counts_aadf_by_direction.R

library(readr)
library(dplyr)
library(sf)
library(mapgl)

# download data here: https://roadtraffic.dft.gov.uk/downloads

dft <- read_csv("dft_traffic_counts_aadf_by_direction.csv",
                col_types = cols(
                  count_point_id = col_double(),
                  year = col_double(),
                  region_id = col_character(),
                  region_name = col_character(),
                  region_ons_code = col_character(),
                  local_authority_id = col_character(),
                  local_authority_name = col_character(),
                  local_authority_code = col_character(),
                  road_name = col_character(),
                  road_category = col_character(),
                  road_type = col_character(),
                  start_junction_road_name = col_character(),
                  end_junction_road_name = col_character(),
                  easting = col_double(),
                  northing = col_double(),
                  latitude = col_double(),
                  longitude = col_double(),
                  link_length_km = col_double(),
                  link_length_miles = col_double(),
                  estimation_method = col_character(),
                  estimation_method_detailed = col_character(),
                  direction_of_travel = col_character(),
                  pedal_cycles = col_double(),
                  two_wheeled_motor_vehicles = col_double(),
                  cars_and_taxis = col_double(),
                  buses_and_coaches = col_double(),
                  LGVs = col_double(),
                  HGVs_2_rigid_axle = col_double(),
                  HGVs_3_rigid_axle = col_double(),
                  HGVs_4_or_more_rigid_axle = col_double(),
                  HGVs_3_or_4_articulated_axle = col_double(),
                  HGVs_5_articulated_axle = col_double(),
                  HGVs_6_articulated_axle = col_double(),
                  all_HGVs = col_double(),
                  all_motor_vehicles = col_double()
                ))

spec(dft)  # provides col() as above which makes it easier to check and correct

dft1 <- dft |> filter(local_authority_name == "West Sussex")
rm(dft)
# peel off latest year for each count site
dft1 <- dft1 |> group_by(count_point_id) |> filter(year == max(year))|> ungroup()

dft1 <- dft1 |> st_as_sf(coords = c("easting", "northing"), crs = 27700)

dft1$direction_of_travel |> unique() 
# [1] "E" "W" "N" "S" "C"  -- C == combined, ie unknown

dft1 <- dft1 |> mutate(dot = case_when(
  direction_of_travel == "N" ~ 0,
  direction_of_travel == "E" ~ 90,
  direction_of_travel == "S" ~ 180,
  direction_of_travel == "W" ~ 270,
  .default = 0
  )
)



maplibre() |>
  fit_bounds(dft1) |>
  add_image(
    id = "direction_sprite",
    url = "https://maprva.org/img/surveillance-direction.png"
  ) |>
  add_circle_layer(
    id = "site",
    source = dft1,
    circle_color = "red",
    # popup = get_column("all_motor_vehicles")  # NOTE: TODO combine totals to get actual total
  ) |>
  add_symbol_layer(
    id = "direction",
    source = dft1,
    icon_image = "direction_sprite",
    icon_allow_overlap = TRUE,
    icon_rotate = list(
      "to-number",
      get_column("dot")
    ),
    text_field = get_column("all_motor_vehicles"),
    # text_offset = c(0,1),
    text_offset = match_expr(
      column = "direction_of_travel",
      values = c("N", "E", "S", "W"),
      stops = list(
        list("literal", c(0, -2)),
        list("literal", c(2, 0)),
        list("literal", c(0, 2)),
        list("literal", c(-2, 0))
      ),
      default = list(list("literal", c(0, 0)))
    ),
    text_allow_overlap = TRUE,
    filter = list(
      "!=",
      get_column("direction_of_travel"),
      "C"
    )
  )