Title:	Work with Open Road Traffic Casualty Data from Great Britain
Version:	4.0.0
Description:	Work with and download road traffic casualty data from Great Britain. Enables access to the UK's official road safety statistics, 'STATS19'. Enables users to specify a download directory for the data, which can be set permanently by adding 'STATS19_DOWNLOAD_DIRECTORY=/path/to/a/dir' to your '.Renviron' file, which can be opened with 'usethis::edit_r_environ()'. The data is provided as a series of '.csv' files. This package downloads, reads-in and formats the data, making it suitable for analysis. See the stats19 vignette for details. Data available from 1979 to 2024. See the official data series at https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-accidents-safety-data. The package is described in a paper in the Journal of Open Source Software (Lovelace et al. 2019) <doi:10.21105/joss.01181>. See Gilardi et al. (2022) <doi:10.1111/rssa.12823>, Vidal-Tortosa et al. (2021) <doi:10.1016/j.jth.2021.101291>, Tait et al. (2023) <doi:10.1016/j.aap.2022.106895>, and León et al. (2025) <doi:10.18637/jss.v114.i09> for examples of how the data can be used for methodological and empirical research.
Depends:	R (≥ 4.1.0)
License:	GPL-3
URL:	https://github.com/ropensci/stats19, https://docs.ropensci.org/stats19/
BugReports:	https://github.com/ropensci/stats19/issues
Encoding:	UTF-8
LazyData:	true
Imports:	sf, curl (≥ 3.2), readr, tibble, dplyr, lubridate, jsonlite, glue
Suggests:	duckdb, DBI, ggplot2, knitr, rmarkdown, stringr, testthat (≥ 2.1.0), tidyr, tidyselect, pkgdown, kableExtra, leaflet, geojsonsf, htmltools, tmap, spatstat.geom, osmdata, covr, readODS, gt, clock, waldo
VignetteBuilder:	knitr
RoxygenNote:	7.3.3
Language:	en-US
X-schema.org-keywords:	stats19, road-safety, transport, car-crashes, ropensci, data
NeedsCompilation:	no
Packaged:	2026-03-18 14:07:19 UTC; robin
Author:	Robin Lovelace [aut, cre], Malcolm Morgan [aut], Layik Hama [aut], Mark Padgham [aut], David Ranzolin [rev], Adam Sparks [rev, ctb], Ivo Wengraf [ctb], RAC Foundation [fnd], Blaise Kelly [aut]
Maintainer:	Robin Lovelace <rob00x@gmail.com>
Repository:	CRAN
Date/Publication:	2026-03-18 15:20:02 UTC

Sample of stats19 data (2022 collisions)

Description

Sample of stats19 data (2022 collisions)

Format

A data frame

Note

These were generated using the script in the data-raw directory (misc.Rmd file).

Examples

nrow(accidents_sample_raw)
accidents_sample_raw

Sample of stats19 data (2022 casualties)

Description

Sample of stats19 data (2022 casualties)

Format

A data frame

Note

These were generated using the script in the data-raw directory (misc.Rmd file).

Examples

nrow(casualties_sample_raw)
casualties_sample_raw

Local helper to be reused.

Description

Local helper to be reused.

Usage

check_input_file(filename = NULL, type = NULL, data_dir = NULL, year = NULL)

Arguments

Clean vehicle make

Description

This function cleans the make of the vehicle.

Usage

clean_make(make, extract_make = TRUE)

Arguments

Examples

clean_make(c("VW", "Mercedez"))
clean_make(c("FORD FIESTA", "LAND ROVER DISCOVERY"), extract_make = TRUE)

Clean vehicle make and model

Description

This function returns a combined cleaned make and model string. It uses clean_make and clean_model to standardize both parts.

Usage

clean_make_model(generic_make_model)

Arguments

Examples

clean_make_model(c("FORD FIESTA", "BMW 3 SERIES"))

Clean vehicle model

Description

This function cleans the model of the vehicle. It extracts the make using extract_make_stats19 and removes it from the string, returning the remaining text as the model in title case.

Usage

clean_model(model)

Arguments

Examples

clean_model(c("FORD FIESTA", "BMW 3 SERIES"))

Download STATS19 data for a year

Description

Download STATS19 data for a year

Usage

dl_stats19(
  year = NULL,
  type = NULL,
  data_dir = get_data_directory(),
  file_name = NULL,
  ask = FALSE,
  silent = FALSE,
  timeout = 600
)

Arguments

Examples

if (curl::has_internet()) {
  # type by default is collisions table
  dl_stats19(year = 2022)
}

Extract vehicle make from generic make/model string

Description

This function extracts the make from a generic make/model string, handling multi-word makes.

Usage

extract_make_stats19(generic_make_model)

Arguments

Examples

extract_make_stats19(c("FORD FIESTA", "LAND ROVER DISCOVERY"))

stats19 file names for easy access

Description

URL decoded file names. Currently there are 52 file names released by the DfT (Department for Transport) and the details include how these were obtained and would be kept up to date.

Format

A named list

Note

These were generated using the script in the data-raw directory (misc.Rmd file).

Examples

head(file_names)

Find file names within stats19::file_names.

Description

Find file names within stats19::file_names.

Usage

find_file_name(years = NULL, type = NULL)

Arguments

Examples

find_file_name(2016)

Format STATS19 casualties

Description

Format STATS19 casualties

Usage

format_casualties(x)

Arguments

Details

This function formats raw STATS19 data

Examples

if(curl::has_internet()) {
dl_stats19(year = 2022, type = "casualty")
x = read_casualties(year = 2022)
casualties = format_casualties(x)
}

Format STATS19 'collisions' data

Description

Format STATS19 'collisions' data

Usage

format_collisions(x)

Arguments

Details

This is a helper function to format raw STATS19 data

Examples

  if(curl::has_internet()) {
    dl_stats19(year = 2022, type = "collision")
  }

Format column names of raw STATS19 data

Description

This function takes messy column names and returns clean ones that work well with R by default. Names that are all lower case with no R-unfriendly characters such as spaces and - are returned.

Usage

format_column_names(column_names)

Arguments

Value

Column names cleaned.

Examples

if(curl::has_internet()) {
crashes_raw = read_collisions(year = 2022)
column_names = names(crashes_raw)
column_names
format_column_names(column_names = column_names)
}

Convert STATS19 data into ppp (spatstat) format.

Description

This function is a wrapper around the spatstat.geom::ppp() function and it is used to transform STATS19 data into a ppp format.

Usage

format_ppp(data, window = NULL, ...)

Arguments

Value

A ppp object.

See Also

format_sf for an analogous function used to convert data into sf format and spatstat.geom::ppp() for the original function.

Examples

if (requireNamespace("spatstat.geom", quietly = TRUE)) {
  x_ppp = format_ppp(accidents_sample)
  x_ppp
}

Format convert STATS19 data into spatial (sf) object

Description

Format convert STATS19 data into spatial (sf) object

Usage

format_sf(x, lonlat = FALSE)

Arguments

Examples

x_sf = format_sf(accidents_sample)
sf:::plot.sf(x_sf)

Format STATS19 vehicles data

Description

Format STATS19 vehicles data

Usage

format_vehicles(x)

Arguments

Details

This function formats raw STATS19 data

Examples

if(curl::has_internet()) {
dl_stats19(year = 2022, type = "vehicle", ask = FALSE)
x = read_vehicles(year = 2022, format = FALSE)
vehicles = format_vehicles(x)
}

Download vehicle data from the DVSA MOT API using VRM.

Description

Download vehicle data from the DVSA MOT API using VRM.

Usage

get_MOT(vrm, apikey)

Arguments

Details

This function takes a a character vector of vehicle registrations (VRMs) and returns vehicle data from MOT records. It returns a data frame of those VRMs which were successfully used with the DVSA MOT API.

Information on the DVSA MOT API is available here: https://dvsa.github.io/mot-history-api-documentation/

The DVSA MOT API requires a registration. The function therefore requires the API key provided by the DVSA. Be aware that the API has usage limits. The function will therefore limit lists with more than 150,000 VRMs.

Examples

vrm = c("1RAC","P1RAC")
apikey = Sys.getenv("MOTKEY")
if(nchar(apikey) > 0) {
  get_MOT(vrm = vrm, apikey = apikey)
}

Download DVLA-based vehicle data from the TfL API using VRM.

Description

Download DVLA-based vehicle data from the TfL API using VRM.

Usage

get_ULEZ(vrm)

Arguments

Details

This function takes a character vector of vehicle registrations (VRMs) and returns DVLA-based vehicle data from TfL's API, included ULEZ eligibility. It returns a data frame of those VRMs which were successfully used with the TfL API. Vehicles are either compliant, non-compliant or exempt. ULEZ-exempt vehicles will not have all vehicle details returned - they will simply be marked "exempt".

Be aware that the API has usage limits. The function will therefore limit API calls to below 50 per minute - this is the maximum rate before an API key is required.

Examples

if(curl::has_internet()) {
vrm = c("1RAC","P1RAC")
get_ULEZ(vrm = vrm)
}

Get data download dir

Description

Get data download dir

Usage

get_data_directory()

Download, read and format STATS19 data in one function.

Description

Download, read and format STATS19 data in one function.

Usage

get_stats19(
  year = NULL,
  type = "collision",
  data_dir = get_data_directory(),
  file_name = NULL,
  format = TRUE,
  ask = FALSE,
  silent = FALSE,
  output_format = "tibble",
  engine = "readr",
  where = NULL,
  ...
)

Arguments

Details

This function gets STATS19 data. Behind the scenes it uses dl_stats19() and ⁠read_*⁠ functions, returning a tibble (default), data.frame, sf or ppp object, depending on the output_format parameter.

By default, stats19 downloads files to a temporary directory. You can change this behavior to save the files in a permanent directory. This is done by setting the STATS19_DOWNLOAD_DIRECTORY environment variable. A convenient way to do this is by adding ⁠STATS19_DOWNLOAD_DIRECTORY=/path/to/a/dir⁠ to your .Renviron file, which can be opened with usethis::edit_r_environ().

The function returns data for a specific year (e.g. year = 2022)

Note: for years before 2016 the function may return data from more years than are requested due to the nature of the files hosted at data.gov.uk.

As this function uses dl_stats19 function, it can download many MB of data, so ensure you have a sufficient disk space.

If output_format = "data.frame" or output_format = "sf" or output_format = "ppp" then the output data is transformed into a data.frame, sf or ppp object using the as.data.frame() or format_sf() or format_ppp() functions, as shown in the examples.

See Also

dl_stats19()

read_collisions()

Examples

if(curl::has_internet()) {
col = get_stats19(year = 2022, type = "collision")
cas = get_stats19(year = 2022, type = "casualty")
veh = get_stats19(year = 2022, type = "vehicle")
class(col)
# data.frame output
x = get_stats19(2022, silent = TRUE, output_format = "data.frame")
class(x)

# # Get 5-years worth of data (commented-out due to large response size):
# col_5 = get_stats19(year = 5, type = "collision")
# cas_5 = get_stats19(year = 5, type = "casualty")
# veh_5 = get_stats19(year = 5, type = "vehicle")


# Run tests only if endpoint is alive:
if(nrow(x) > 0) {

# use duckdb engine
col_duck = get_stats19(year = 2022, type = "collision", engine = "duckdb")

# use duckdb with where clause
col_where = get_stats19(year = 2022, type = "collision", engine = "duckdb",
                       where = "speed_limit = 30")

# sf output
x_sf = get_stats19(2022, silent = TRUE, output_format = "sf")

# sf output with lonlat coordinates
x_sf = get_stats19(2022, silent = TRUE, output_format = "sf", lonlat = TRUE)
sf::st_crs(x_sf)

if (requireNamespace("spatstat.geom", quietly = TRUE)) {
# ppp output
x_ppp = get_stats19(2022, silent = TRUE, output_format = "ppp")

# We can use the window parameter of format_ppp function to filter only the
# events occurred in a specific area. For example we can create a new bbox
# of 5km around the city center of Leeds

leeds_window = spatstat.geom::owin(
xrange = c(425046.1, 435046.1),
yrange = c(428577.2, 438577.2)
)

leeds_ppp = get_stats19(2022, silent = TRUE, output_format = "ppp", window = leeds_window)
spatstat.geom::plot.ppp(leeds_ppp, use.marks = FALSE, clipwin = leeds_window)
}
}
}

Download and read-in severity adjustment factors

Description

See the DfT's documentation on adjustment factors Annex: Update to severity adjustments methodology.

Usage

get_stats19_adjustments(
  data_dir = get_data_directory(),
  u = paste0("https://data.dft.gov.uk/road-accidents-safety-data/",
    "dft-road-casualty-statistics-casualty-adjustment-lookup_",
    "2004-latest-published-year.csv")
)

Arguments

Details

See Estimating and adjusting for changes in the method of severity reporting for road accidents and casualty data: final report for details.

Examples

## Not run: 
if(curl::has_internet()) {
adjustment = get_stats19_adjustments()
}

## End(Not run)

Convert file names to urls

Description

Convert file names to urls

Usage

get_url(
  file_name = "",
  domain = "https://data.dft.gov.uk",
  directory = "road-accidents-safety-data"
)

Arguments

Examples

# get_url(find_file_name(1985))

Locate a file on disk

Description

Locate a file on disk

Usage

locate_files(
  data_dir = get_data_directory(),
  type = NULL,
  years = NULL,
  quiet = FALSE
)

Arguments

Pin down a file on disk from parameters.

Description

Pin down a file on disk from parameters.

Usage

locate_one_file(
  filename = NULL,
  data_dir = get_data_directory(),
  year = NULL,
  type = NULL
)

Arguments

Examples

locate_one_file()

Match STATS19 collisions with TAG (RAS4001) cost estimates

Description

Downloads and processes the UK Department for Transport TAG Data Book table RAS4001, and joins estimated collision costs to STATS19 data.

Three matching modes are available:

• "severity" — cost varies only by collision severity

• "severity_road" — cost varies by severity and road type

• "severity_road_bua" — cost varies by severity & road type, where road type is determined using ONS Built-Up Area (BUA) polygons (2022)

BUA polygons are downloaded automatically from: https://open-geography-portalx-ons.hub.arcgis.com/api/download/v1/items/ad30b234308f4b02b4bb9b0f4766f7bb/geoPackage?layers=0

Optionally, total costs may be summarised by severity and/or road type.

Usage

match_tag(
  crashes,
  shapes_url =
    paste0("https://open-geography-portalx-ons.hub.arcgis.com/api/download/v1/",
    "items/ad30b234308f4b02b4bb9b0f4766f7bb/geoPackage?layers=0"),
  costs_url = paste0("https://assets.publishing.service.gov.uk/media/",
    "68d421cc275fc9339a248c8e/ras4001.ods"),
  match_with = "severity",
  include_motorway_bua = FALSE,
  summarise = FALSE
)

Arguments

Details

The function:

1. Downloads and parses RAS4001 cost tables

2. Computes road type using STATS19 fields or optional BUA polygons

3. Joins appropriate cost estimates

4. Optionally aggregates totals

When crashes is an sf object and summarise = TRUE, geometry is automatically dropped.

Value

A data frame (or sf object if input is sf) with added columns for estimated collision costs. If summarise = TRUE, a summary table of total costs (in millions) is returned.

Examples

## Not run: 
  # Simple severity-based matching
  match_tag(stats19_df, match_with = "severity")

  # Severity + road type
  match_tag(stats19_df, match_with = "severity_road")

  # Using ONS Built-Up Areas, with motorway override
  match_tag(
    stats19_df,
    match_with = "severity_road_bua",
    include_motorway_bua = TRUE
  )

  # Summarised totals
  match_tag(stats19_df, match_with = "severity", summarise = TRUE)

## End(Not run)

Generate a phrase for data download purposes

Description

Generate a phrase for data download purposes

Usage

phrase()

Police force boundaries in England (2016)

Description

This dataset represents the 43 police forces in England and Wales. These are described on the Wikipedia page. on UK police forces.

Format

An sf data frame

Details

The geographic boundary data were taken from the UK government's official geographic data portal. See http://geoportal.statistics.gov.uk/

Note

These were generated using the script in the data-raw directory (misc.Rmd file) in the package's GitHub repo: github.com/ITSLeeds/stats19.

Examples

nrow(police_boundaries)
police_boundaries[police_boundaries$pfa16nm == "West Yorkshire", ]
sf:::plot.sf(police_boundaries)

Read in STATS19 road safety data from .csv files downloaded.

Description

Read in STATS19 road safety data from .csv files downloaded.

Usage

read_casualties(
  year = NULL,
  filename = "",
  data_dir = get_data_directory(),
  format = TRUE
)

Arguments

Read in STATS19 road safety data from .csv files downloaded.

Description

Read in STATS19 road safety data from .csv files downloaded.

Usage

read_collisions(
  year = NULL,
  filename = "",
  data_dir = get_data_directory(),
  format = TRUE,
  silent = FALSE
)

Arguments

Details

This is a wrapper function to access and load stats 19 data in a user-friendly way. The function returns a data frame, in which each record is a reported incident in the STATS19 data.

Examples

if(curl::has_internet()) {
dl_stats19(year = 2024, type = "collision")
ac = read_collisions(year = 2024)
}

Read in stats19 road safety data from .csv files downloaded.

Description

Read in stats19 road safety data from .csv files downloaded.

Usage

read_vehicles(
  year = NULL,
  filename = "",
  data_dir = get_data_directory(),
  format = TRUE
)

Arguments

Schema for stats19 data (UKDS)

Description

Schema for stats19 data (UKDS)

Format

A data frame

Interactively select from options

Description

Interactively select from options

Usage

select_file(fnames)

Arguments

Set data download dir

Description

Set data download dir

Usage

set_data_directory(data_path)

Arguments

Stats19 schema and variables

Description

stats19_schema and stats19_variables contain metadata on stats19 data. stats19_schema is a look-up table matching codes provided in the raw stats19 dataset with character strings.

Note

The schema data can be (re-)generated using the script in the data-raw directory.

Sample of stats19 data (2022 vehicles)

Description

Sample of stats19 data (2022 vehicles)

Format

A data frame

Note

These were generated using the script in the data-raw directory (misc.Rmd file).

Examples

nrow(vehicles_sample_raw)
vehicles_sample_raw