[This article was first published on r.iresmi.net, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t.
Day 20 & 21 of 30DayMapChallenge: « OpenStreetMap » and « Conflict » (previously).
Mapping the accidents between bicycles and cars in 2023 in France. We have had a few sad accidents recently showing a growing attention on cyclist security and the conflicts on the road.
We’ll use the Annual databases of road traffic injuries on an OSM background.
library(dplyr) library(tidyr) library(readr) library(janitor) library(sf) library(glue) library(leaflet)
The data guide is available (in french).
# vehicules-2023.csv
vehicles <- read_csv2(
"https://www.data.gouv.fr/fr/datasets/r/146a42f5-19f0-4b3e-a887-5cd8fbef057b",
name_repair = make_clean_names)
# caract-2023.csv
caract <- read_csv2(
"https://www.data.gouv.fr/fr/datasets/r/104dbb32-704f-4e99-a71e-43563cb604f2",
name_repair = make_clean_names)
# usagers-2023.csv
user <- read_csv2(
"https://www.data.gouv.fr/fr/datasets/r/68848e2a-28dd-4efc-9d5f-d512f7dbe66f",
name_repair = make_clean_names)
severity <- tribble(
~grav, ~severity,
1, "Unharmed",
2, "Killed",
3, "Injured hospitalized",
4, "Slightly injured") |>
mutate(severity = factor(
severity,
labels = c("Killed",
"Injured hospitalized",
"Slightly injured",
"Unharmed")))
# accidents where car and bikes are involved
bike_car_acc <- vehicles |>
filter(catv %in% c(1, 7)) |> # 1 bike ; 7 : car
count(num_acc, catv) |>
pivot_wider(names_from = catv, values_from = n, names_prefix = "catv_") |>
filter(catv_7 > 0 & catv_1 > 0) |>
pull(num_acc)
# bikers injuries
bikers <- vehicles |>
filter(num_acc %in% bike_car_acc,
catv == 1) |>
left_join(user, join_by(num_acc, id_vehicule)) |>
left_join(severity, join_by(grav)) |>
count(num_acc, severity)
bikers_display <- bikers |>
mutate(outcome = glue("{severity} ({n})")) |>
arrange(severity) |>
summarise(.by = num_acc,
outcome = glue_collapse(outcome, sep = "
"))
# accident locations
bike_accidents <- caract |>
filter(num_acc %in% bike_car_acc) |>
st_as_sf(coords = c("long", "lat"), crs = "EPSG:4326") |>
left_join(bikers_display, join_by(num_acc))
That’s 2858 accidents and 772 bikers killed.
bike_accidents |>
leaflet() |>
addTiles(attribution = r"(
r.iresmi.net.
data: Ministère de l'intérieur 2023;
map: OpenStreetMap)") |>
addCircleMarkers(popup = ~ glue("{an}-{mois}-{jour}
biker status:
{outcome}"),
clusterOptions = markerClusterOptions())
