The goal of crash-data-vis-demo is to demonstrate how to get and visualise data on road traffic casualties and road geometries.
# Install the remotes package if not yet installed:
if (!"remotes" %in% installed.packages()) {
install.packages("remotes")
}# Check you have the packages installed:
remotes::install_cran(c("tidyverse", "stats19"))
#> Skipping install of 'tidyverse' from a cran remote, the SHA1 (2.0.0) has not changed since last install.
#> Use `force = TRUE` to force installation
#> Skipping install of 'stats19' from a cran remote, the SHA1 (3.0.3) has not changed since last install.
#> Use `force = TRUE` to force installationlibrary(tidyverse) # for data manipulation and visualisation
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.5
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.4.4.9000 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(stats19) # for getting road casualty data
#> Data provided under OGL v3.0. Cite the source and link to:
#> www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
library(tmap)
#>
#> Attaching package: 'tmap'
#>
#> The following object is masked from 'package:datasets':
#>
#> rivers
tmap_mode("view")
#> tmap mode set to 'view'# Get road casualty data for 2022
cas = get_stats19(year = 2022, type = "cas")
#> Files identified: dft-road-casualty-statistics-casualty-2022.csv
#> https://data.dft.gov.uk/road-accidents-safety-data/dft-road-casualty-statistics-casualty-2022.csv
#> Data already exists in data_dir, not downloading
#> Rows: 135480 Columns: 19
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): accident_index, accident_reference, lsoa_of_casualty
#> dbl (16): accident_year, vehicle_reference, casualty_reference, casualty_cla...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
col = get_stats19(year = 2022, type = "col")
#> Files identified: dft-road-casualty-statistics-collision-2022.csv
#>
#> https://data.dft.gov.uk/road-accidents-safety-data/dft-road-casualty-statistics-collision-2022.csv
#> Data already exists in data_dir, not downloading
#> Reading in:
#> ~/data/stats19/dft-road-casualty-statistics-collision-2022.csv
#> Rows: 106004 Columns: 36── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (6): accident_index, accident_reference, date, local_authority_ons_dis...
#> dbl (29): accident_year, location_easting_osgr, location_northing_osgr, lon...
#> time (1): time
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.date and time columns present, creating formatted datetime columnIf you want to get more data, you can change 2022 to 1979, which downloads all data from 1979 to the last year for which data is available.
We’ll get the data for Edinburgh as follows:
names(col)
#> [1] "accident_index"
#> [2] "accident_year"
#> [3] "accident_reference"
#> [4] "location_easting_osgr"
#> [5] "location_northing_osgr"
#> [6] "longitude"
#> [7] "latitude"
#> [8] "police_force"
#> [9] "accident_severity"
#> [10] "number_of_vehicles"
#> [11] "number_of_casualties"
#> [12] "date"
#> [13] "day_of_week"
#> [14] "time"
#> [15] "local_authority_district"
#> [16] "local_authority_ons_district"
#> [17] "local_authority_highway"
#> [18] "first_road_class"
#> [19] "first_road_number"
#> [20] "road_type"
#> [21] "speed_limit"
#> [22] "junction_detail"
#> [23] "junction_control"
#> [24] "second_road_class"
#> [25] "second_road_number"
#> [26] "pedestrian_crossing_human_control"
#> [27] "pedestrian_crossing_physical_facilities"
#> [28] "light_conditions"
#> [29] "weather_conditions"
#> [30] "road_surface_conditions"
#> [31] "special_conditions_at_site"
#> [32] "carriageway_hazards"
#> [33] "urban_or_rural_area"
#> [34] "did_police_officer_attend_scene_of_accident"
#> [35] "trunk_road_flag"
#> [36] "lsoa_of_accident_location"
#> [37] "datetime"
table(col$police_force)
#>
#> Avon and Somerset Bedfordshire Cambridgeshire Cheshire
#> 2404 1285 1485 1685
#> City of London Cleveland Cumbria Derbyshire
#> 175 674 854 1974
#> Devon and Cornwall Dorset Durham Dyfed-Powys
#> 2752 1449 621 982
#> Essex Gloucestershire Greater Manchester Gwent
#> 2901 930 2804 587
#> Hampshire Hertfordshire Humberside Kent
#> 3485 1780 1913 3983
#> Lancashire Leicestershire Lincolnshire Merseyside
#> 2765 1076 1604 2267
#> Metropolitan Police Norfolk North Wales North Yorkshire
#> 23327 1537 802 1174
#> Northamptonshire Northumbria Nottinghamshire Police Scotland
#> 1229 1874 1889 4125
#> South Wales South Yorkshire Staffordshire Suffolk
#> 944 2038 507 1163
#> Surrey Sussex Thames Valley Warwickshire
#> 2783 3678 3256 865
#> West Mercia West Midlands West Yorkshire Wiltshire
#> 1658 4943 4397 1380col_scotland = col |>
filter(str_detect(police_force, "Scotland"))Let’s get a case study area:
edinburgh_boundary = zonebuilder::zb_zone("Edinburgh", n_circles = 2)We’ll convert the collisions to sf objects so we can do a spatial
join:
col_sf = format_sf(col_scotland, lonlat = TRUE)
#> 0 rows removed with no coordinatesNow we can do a spatial filter:
col_edinburgh = col_sf[edinburgh_boundary, ]We can now plot these on a map:
tm_shape(col_edinburgh) +
tm_dots(col = "accident_severity", size = 0.1) +
tm_layout(legend.position = c("left", "bottom"))