stats19 provides functions for downloading and formatting road crash data. Specifically, it enables access to the UK’s official road traffic casualty database, STATS19. (The name comes from the form used by the police to record car crashes and other incidents resulting in casualties on the roads.)
A full overview of STATS19 variables be found in a document provided by the UK’s Department for Transport (DfT).
The raw data is provided as a series of
.csv files that contain integers and which are stored in dozens of
.zip files. Finding, reading-in and formatting the data for research can be a time consuming process subject to human error. stats19 speeds up these vital but boring and error-prone stages of the research process with a single function:
get_stats19(). By allowing public access to properly labelled road crash data, stats19 aims to make road safety research more reproducible and accessible.
For transparency and modularity, each stage can be undertaken separately, as documented in the stats19 vignette.
Install and load the latest version with:
You can install the released version of stats19 from CRAN with:
type parameters, mirroring the provision of STATS19 data files, which are categorised by year (from 1979 onward) and type (with separate tables for crashes, casualties and vehicles, as outlined below). The following command, for example, gets crash data from 2017 (note: we follow the “crash not accident” campaign of RoadPeace in naming crashes, although the DfT refers to the relevant tables as ‘accidents’ data):
crashes = get_stats19(year = 2017, type = "accident", ask = FALSE) #> Files identified: dftRoadSafetyData_Accidents_2017.zip #> http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Accidents_2017.zip #> Attempt downloading from: #> Data saved at /tmp/RtmpAW1I3U/dftRoadSafetyData_Accidents_2017/Acc.csv #> Reading in: #> /tmp/RtmpAW1I3U/dftRoadSafetyData_Accidents_2017/Acc.csv
What just happened? For the
year 2017 we read-in crash-level (
type = "accident") data on all road crashes recorded by the police across Great Britain. The dataset contains 32 columns (variables) for 129,982 crashes. We were not asked to download the file (by default you are asked to confirm the file that will be downloaded). The contents of this dataset, and other datasets provided by stats19, are outlined below and described in more detail in the stats19 vignette.
We will see below how the function also works to get the corresponding casualty and vehicle datasets for 2017. The package also allows STATS19 files to be downloaded and read-in separately, allowing more control over what you download, and subsequently read-in, with
read_vehicles(), as described in the vignette.
Data files can be downloaded without reading them in using the function
dl_stats19(). If there are multiple matches, you will be asked to choose from a range of options. Providing just the year, for example, will result in the following options:
STATS19 data consists of 3 main tables:
The contents of each is outlined below.
Crash data was downloaded and read-in using the function
get_stats19(), as described above.
Some of the key variables in this dataset include:
crashes[c(7, 18, 23, 25)] #> # A tibble: 129,982 x 4 #> accident_severity speed_limit pedestrian_crossing_hu… light_conditions #> <chr> <int> <chr> <chr> #> 1 Fatal 30 None within 50 metres Darkness - lights… #> 2 Slight 30 None within 50 metres Darkness - lights… #> 3 Slight 30 None within 50 metres Darkness - lights… #> 4 Slight 30 None within 50 metres Darkness - lights… #> 5 Serious 20 None within 50 metres Darkness - lights… #> 6 Slight 30 None within 50 metres Darkness - lights… #> 7 Slight 40 None within 50 metres Darkness - lights… #> 8 Slight 30 Control by other autho… Darkness - lights… #> 9 Serious 50 None within 50 metres Darkness - lights… #> 10 Serious 30 None within 50 metres Darkness - lights… #> # … with 129,972 more rows
crashes, casualty data for 2017 can be downloaded, read-in and formatted as follows:
casualties = get_stats19(year = 2017, type = "casualties", ask = FALSE) #> Files identified: dftRoadSafetyData_Casualties_2017.zip #> http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Casualties_2017.zip #> Attempt downloading from: #> Data saved at /tmp/RtmpAW1I3U/dftRoadSafetyData_Casualties_2017/Cas.csv nrow(casualties) #>  170993 ncol(casualties) #>  16
The results show that there were 170,993 casualties reported by the police in the STATS19 dataset in 2017, and 16 columns (variables). Values for a sample of these columns are shown below:
casualties[c(4, 5, 6, 14)] #> # A tibble: 170,993 x 4 #> casualty_class sex_of_casualty age_of_casualty casualty_type #> <chr> <chr> <int> <chr> #> 1 Passenger Female 18 Car occupant #> 2 Driver or rider Male 19 Motorcycle 50cc and und… #> 3 Passenger Male 18 Motorcycle 50cc and und… #> 4 Passenger Female 33 Car occupant #> 5 Driver or rider Female 31 Car occupant #> 6 Passenger Male 3 Car occupant #> 7 Pedestrian Male 45 Pedestrian #> 8 Driver or rider Male 14 Motorcycle 125cc and un… #> 9 Driver or rider Female 58 Car occupant #> 10 Driver or rider Male 27 Car occupant #> # … with 170,983 more rows
The full list of column names in the
casualties dataset is:
names(casualties) #>  "accident_index" #>  "vehicle_reference" #>  "casualty_reference" #>  "casualty_class" #>  "sex_of_casualty" #>  "age_of_casualty" #>  "age_band_of_casualty" #>  "casualty_severity" #>  "pedestrian_location" #>  "pedestrian_movement" #>  "car_passenger" #>  "bus_or_coach_passenger" #>  "pedestrian_road_maintenance_worker" #>  "casualty_type" #>  "casualty_home_area_type" #>  "casualty_imd_decile"
Data for vehicles involved in crashes in 2017 can be downloaded, read-in and formatted as follows:
vehicles = get_stats19(year = 2017, type = "vehicles", ask = FALSE) #> Files identified: dftRoadSafetyData_Vehicles_2017.zip #> http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Vehicles_2017.zip #> Attempt downloading from: #> Data saved at /tmp/RtmpAW1I3U/dftRoadSafetyData_Vehicles_2017/Veh.csv nrow(vehicles) #>  238926 ncol(vehicles) #>  23
The results show that there were 238,926 vehicles involved in crashes reported by the police in the STATS19 dataset in 2017, with 23 columns (variables). Values for a sample of these columns are shown below:
vehicles[c(3, 14:16)] #> # A tibble: 238,926 x 4 #> vehicle_type journey_purpose_of_dr… sex_of_driver age_of_driver #> <chr> <chr> <chr> <int> #> 1 Car Not known Male 24 #> 2 Motorcycle 50cc and … Not known Male 19 #> 3 Car Not known Male 33 #> 4 Car Not known Male 40 #> 5 Car Not known Not known -1 #> 6 Car Not known Male 35 #> 7 Car Not known Female 31 #> 8 Car Not known Female 37 #> 9 Car Not known Female 29 #> 10 Car Not known Male 78 #> # … with 238,916 more rows
The full list of column names in the
vehicles dataset is:
names(vehicles) #>  "accident_index" "vehicle_reference" #>  "vehicle_type" "towing_and_articulation" #>  "vehicle_manoeuvre" "vehicle_location_restricted_lane" #>  "junction_location" "skidding_and_overturning" #>  "hit_object_in_carriageway" "vehicle_leaving_carriageway" #>  "hit_object_off_carriageway" "first_point_of_impact" #>  "was_vehicle_left_hand_drive" "journey_purpose_of_driver" #>  "sex_of_driver" "age_of_driver" #>  "age_band_of_driver" "engine_capacity_cc" #>  "propulsion_code" "age_of_vehicle" #>  "driver_imd_decile" "driver_home_area_type" #>  "vehicle_imd_decile"
An important feature of STATS19 data is that the “accidents” table contains geographic coordinates. These are provided at ~10m resolution in the UK’s official coordinate reference system (the Ordnance Survey National Grid, EPSG code 27700). stats19 converts the non-geographic tables created by
format_accidents() into the geographic data form of the
sf package with the function
format_sf() as follows:
The note arises because
NA values are not permitted in
sf coordinates, and so rows containing no coordinates are automatically removed. Having the data in a standard geographic form allows various geographic operations to be performed on it. The following code chunk, for example, returns all crashes within the boundary of West Yorkshire (which is contained in the object
sf data frame containing all police jurisdictions in England and Wales).
library(sf) #> Linking to GEOS 3.7.0, GDAL 2.3.2, PROJ 5.2.0 library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union wy = filter(police_boundaries, pfa16nm == "West Yorkshire") crashes_wy = crashes_sf[wy, ] nrow(crashes_sf) #>  129963 nrow(crashes_wy) #>  4371
This subsetting has selected the 4,371 crashes which occurred within West Yorkshire in 2017.
The three main tables we have just read-in can be joined by shared key variables. This is demonstrated in the code chunk below, which subsets all casualties that took place in Leeds, and counts the number of casualties by severity for each crash:
sel = casualties$accident_index %in% crashes_wy$accident_index casualties_wy = casualties[sel, ] cas_types = casualties_wy %>% select(accident_index, casualty_type) %>% mutate(n = 1) %>% group_by(accident_index, casualty_type) %>% summarise(n = sum(n)) %>% tidyr::spread(casualty_type, n, fill = 0) cas_types$Total = rowSums(cas_types[-1]) cj = left_join(crashes_wy, cas_types, by = "accident_index")
What just happened? We found the subset of casualties that took place in West Yorkshire with reference to the
accident_index variable. Then we used functions from the tidyverse package dplyr (and
spread() from tidyr) to create a dataset with a column for each casualty type. We then joined the updated casualty data onto the
crashes_wy dataset. The result is a spatial (
sf) data frame of crashes in Leeds, with columns counting how many road users of different types were hurt. The original and joined data look like this:
crashes_wy[1:2, c(1, 5)] %>% st_drop_geometry() #> # A tibble: 2 x 2 #> accident_index accident_severity #> * <chr> <chr> #> 1 2017120009776 Slight #> 2 2017120010412 Slight cas_types[1:2, c("accident_index", "Cyclist")] #> # A tibble: 2 x 2 #> # Groups: accident_index  #> accident_index Cyclist #> <chr> <dbl> #> 1 2017120009776 0 #> 2 2017120010412 1 cj[1:2, c(1, 5, 34)] %>% st_drop_geometry() #> # A tibble: 2 x 3 #> accident_index accident_severity Cyclist #> * <chr> <chr> <dbl> #> 1 2017120009776 Slight 0 #> 2 2017120010412 Slight 1
The join operation added a geometry column to the casualty data, enabling it to be mapped (for more advanced maps, see the vignette):
The spatial distribution of crashes in West Yorkshire clearly relates to the region’s geography. Crashes tend to happen on busy Motorway roads (with a high speed limit, of 70 miles per hour, as shown in the map above) and city centres, of Leeds and Bradford in particular. The severity and number of people hurt (proportional to circle width in the map above) in crashes is related to the speed limit. This can be seen by comparing the previous map with an overview of the area, from an academic paper on the social, spatial and temporal distribution of bike crashes in West Yorkshire (Lovelace, Roberts, and Kellar 2016):
We can also explore seasonal trends in crashes by aggregating crashes by day of the year:
library(ggplot2) crashes_dates = cj %>% st_set_geometry(NULL) %>% group_by(date) %>% summarise( walking = sum(Pedestrian), cycling = sum(Cyclist), passenger = sum(`Car occupant`) ) %>% tidyr::gather(mode, casualties, -date) ggplot(crashes_dates, aes(date, casualties)) + geom_smooth(aes(colour = mode), method = "loess") + ylab("Casualties per day")
Different types of crashes also tend to happen at different times of day. This is illustrated in the plot below, which shows the times of day when people who were travelling by different modes were most commonly injured.
library(stringr) crash_times = cj %>% st_set_geometry(NULL) %>% group_by(hour = as.numeric(str_sub(time, 1, 2))) %>% summarise( walking = sum(Pedestrian), cycling = sum(Cyclist), passenger = sum(`Car occupant`) ) %>% tidyr::gather(mode, casualties, -hour) ggplot(crash_times, aes(hour, casualties)) + geom_line(aes(colour = mode))
Note that cycling manifests distinct morning and afternoon peaks (see Lovelace, Roberts, and Kellar 2016 for more on this).
There is much important research that needs to be done to help make the transport systems in many cities safer. Even if you’re not working with UK data, we hope that the data provided by stats19 data can help safety researchers develop new methods to better understand the reasons why people are needlessly hurt and killed on the roads.
The next step is to gain a deeper understanding of stats19 and the data it provides. Then it’s time to pose interesting research questions, some of which could provide an evidence-base in support policies that save lives (e.g. Sarkar, Webster, and Kumari 2018). For more on these next steps, see the package’s introductory vignette.
The stats19 package builds on previous work, including: