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# stats19 <a href='https://docs.ropensci.org/stats19/'><img src='https://raw.githubusercontent.com/ropensci/stats19/master/man/figures/logo.png' align="right" height=215/></a>

**stats19** provides functions for downloading and formatting road crash
data. Specifically, it enables access to the UK’s official road traffic
casualty database,
[STATS19](https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-accidents-safety-data).
(The name comes from the form used by the police to record car crashes
and other incidents resulting in casualties on the roads.)

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 raw data is provided as a series of `.csv` 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](https://itsleeds.github.io/stats19/articles/stats19.html).

The package has now been peer reviewed and is stable, and has been
published in the Journal of Open Source Software (Lovelace et al. 2019).
Please tell people about the package, link to it and cite it if you use
it in your work.

## Installation

Install and load the latest version with:

``` r
remotes::install_github("ropensci/stats19")
```

``` r
library(stats19)
#> Data provided under OGL v3.0. Cite the source and link to:
#> www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
```

You can install the released version of stats19 from
[CRAN](https://cran.r-project.org/package=stats19) with:

``` r
install.packages("stats19")
```

Load the development version of the package from this repository with:

``` r
devtools::load_all()
```

## get_stats19()

`get_stats19()` requires `year` and `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 2023 (**note**: we follow the “crash not accident”
campaign of
[RoadPeace](https://www.roadpeace.org/working-for-change/crash-not-accident/)
in naming crashes, and also adopted by the DfT since 2025):

``` r
crashes = get_stats19(year = 2023, type = "collision")
#> Files identified: dft-road-casualty-statistics-collision-2023.csv
#> Data saved at <tempdir>/dft-road-casualty-statistics-collision-2023.csv
#> Reading in: <tempdir>/dft-road-casualty-statistics-collision-2023.csv
#> date and time columns present, creating formatted datetime column
```

What just happened? For the `year` 2023 we read-in crash-level
(`type = "collision"`) data on all road crashes recorded by the police
across Great Britain. The dataset contains 42 columns (variables) for
104,258 crashes. We were not asked to download the file (by default you
are not 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](https://itsleeds.github.io/stats19/articles/stats19.html).

We will see below how the function also works to get the corresponding
casualty and vehicle datasets for 2023. 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_collisions()`, `read_casualties()` and `read_vehicles()`, as
described in the vignette.

## Data download

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:

``` r
dl_stats19(year = 2023, data_dir = tempdir())
```

    Multiple matches. Which do you want to download?

    1: dft-road-casualty-statistics-casualty-2023.csv
    2: dft-road-casualty-statistics-vehicle-2023.csv
    3: dft-road-casualty-statistics-collision-2023.csv

    Selection: 
    Enter an item from the menu, or 0 to exit

To download other years it is worth being aware of the file structure of
[the files, hosted by the
DfT](https://www.gov.uk/government/statistical-data-sets/road-safety-open-data),
this package imports. The last 5 years of data (usually released
September for the previous year) are duplicated 3 times and available as
individual years, or 1 file covering all 5 years. The data goes all the
way back to 1979 (one of (if not the) the longest running collision
statistics databases in the world). This data is stored in one very
large file (~1GB) and also covers the last 5 years.

To request years using get_stats19 the following call structure can be
used:

``` r

# one year (only available for years within last 5 years of data)
cas_2024 = get_stats19(year = 2024,type = "casualty")
```

This will return a dataframe of just 2024.

``` r
# all of the last 5 years
cas_last_5_years = get_stats19(year = 5, type = "casualty")
```

This will return a dataframe with all of the last 5 years.

To request specific year ranges use start_year:end_year:

``` r

# a year or so longer than last 5 years
cas_last_6_years = get_stats19(year = 2019:2024,type = "casualty")
cas_since_day_one = get_stats19(year = 1979:2024,type = "casualty")
```

But be aware, even though the two calls above are very different ranges,
because they are both earlier than the last 5 years, the full raw file
will be downloaded to your temporary directory, which can take sometime.
Future calls should use this same file, but get_stats19 also formats
this local file, which can still take some time. So think carefully
about how you intend to work with the data.

## Using the data

STATS19 data consists of 3 main tables:

- Collisions, the main table which contains information on the crash
  time, location and other variables (37 columns in total)
- Casualties, containing data on people hurt or killed in each crash (21
  columns in total)
- Vehicles, containing data on vehicles involved in or causing each
  crash (34 columns in total)

The contents of each is outlined below.

### Crash data

Crash data was downloaded and read-in using the function
`get_stats19()`, as described above.

``` r
nrow(crashes)
#> [1] 104258
ncol(crashes)
#> [1] 42
```

Some of the key variables in this dataset include:

``` r
key_column_names = grepl(pattern = "severity|speed|pedestrian|light_conditions", x = names(crashes))
crashes[key_column_names]
#> # A tibble: 104,258 × 8
#>    collision_severity speed_limit pedestrian_crossing_phys…¹ pedestrian_crossing
#>    <chr>              <chr>       <chr>                      <chr>              
#>  1 Slight             60          No physical crossing faci… No physical crossi…
#>  2 Slight             30          No physical crossing faci… No physical crossi…
#>  3 Serious            30          No physical crossing faci… No physical crossi…
#>  4 Slight             30          No physical crossing faci… No physical crossi…
#>  5 Slight             30          Pelican, puffin, toucan o… Pedestrian light c…
#>  6 Slight             20          No physical crossing faci… No physical crossi…
#>  7 Slight             20          No physical crossing faci… No physical crossi…
#>  8 Slight             30          No physical crossing faci… No physical crossi…
#>  9 Serious            20          No physical crossing faci… No physical crossi…
#> 10 Slight             30          Zebra                      Human crossing con…
#> # ℹ 104,248 more rows
#> # ℹ abbreviated name: ¹​pedestrian_crossing_physical_facilities_historic
#> # ℹ 4 more variables: light_conditions <chr>,
#> #   enhanced_severity_collision <dbl>,
#> #   collision_adjusted_severity_serious <dbl>,
#> #   collision_adjusted_severity_slight <dbl>
```

For the full list of columns, run `names(crashes)` or see the
[vignette](https://github.com/ropensci/stats19/blob/master/vignettes/stats19.Rmd).

<!-- This means `crashes` is much more usable than `crashes_raw`, as shown below, which shows three records and some key variables in the messy and clean datasets: -->

### Casualties data

As with `crashes`, casualty data for 2023 can be downloaded, read-in and
formatted as follows:

``` r
casualties = get_stats19(year = 2023, type = "casualty", ask = FALSE, format = TRUE)
#> Files identified: dft-road-casualty-statistics-casualty-2023.csv
#> Data saved at C:\Users\xxx\AppData\Local\Temp\RtmpisbAkG/dft-road-casualty-statistics-casualty-2023.csv
#> Reading in: C:\Users\xxx\AppData\Local\Temp\RtmpisbAkG/dft-road-casualty-statistics-casualty-2023.csv
nrow(casualties)
#> [1] 132977
ncol(casualties)
#> [1] 23
```

The results show that there were 132,977 casualties reported by the
police in the STATS19 dataset in 2023, and 23 columns (variables).
Values for a sample of these columns are shown below:

``` r
casualties[c(4, 5, 6, 14)]
#> # A tibble: 132,977 × 4
#>    vehicle_reference casualty_reference casualty_class  bus_or_coach_passenger  
#>    <chr>             <chr>              <chr>           <chr>                   
#>  1 1                 1                  Pedestrian      Not a bus or coach pass…
#>  2 2                 1                  Driver or rider Not a bus or coach pass…
#>  3 1                 1                  Driver or rider Not a bus or coach pass…
#>  4 1                 1                  Driver or rider Not a bus or coach pass…
#>  5 2                 2                  Passenger       Not a bus or coach pass…
#>  6 1                 1                  Driver or rider Not a bus or coach pass…
#>  7 1                 1                  Driver or rider Not a bus or coach pass…
#>  8 1                 1                  Pedestrian      Not a bus or coach pass…
#>  9 2                 1                  Driver or rider Not a bus or coach pass…
#> 10 1                 1                  Driver or rider Not a bus or coach pass…
#> # ℹ 132,967 more rows
```

The full list of column names in the `casualties` dataset is:

``` r
names(casualties)
#>  [1] "collision_index"                    "collision_year"                    
#>  [3] "collision_ref_no"                   "vehicle_reference"                 
#>  [5] "casualty_reference"                 "casualty_class"                    
#>  [7] "sex_of_casualty"                    "age_of_casualty"                   
#>  [9] "age_band_of_casualty"               "casualty_severity"                 
#> [11] "pedestrian_location"                "pedestrian_movement"               
#> [13] "car_passenger"                      "bus_or_coach_passenger"            
#> [15] "pedestrian_road_maintenance_worker" "casualty_type"                     
#> [17] "casualty_imd_decile"                "lsoa_of_casualty"                  
#> [19] "enhanced_casualty_severity"         "casualty_injury_based"             
#> [21] "casualty_adjusted_severity_serious" "casualty_adjusted_severity_slight" 
#> [23] "casualty_distance_banding"
```

### Vehicles data

Data for vehicles involved in crashes in 2023 can be downloaded, read-in
and formatted as follows:

``` r
vehicles = get_stats19(year = 2023, type = "vehicle", ask = FALSE, format = TRUE)
#> Files identified: dft-road-casualty-statistics-vehicle-2023.csv
#> Data saved at C:\Users\xxx\AppData\Local\Temp\RtmpisbAkG/dft-road-casualty-statistics-vehicle-2023.csv
#> Reading in: C:\Users\xxx\AppData\Local\Temp\RtmpisbAkG/dft-road-casualty-statistics-vehicle-2023.csv
nrow(vehicles)
#> [1] 189815
ncol(vehicles)
#> [1] 29
```

The results show that there were 189,815 vehicles involved in crashes
reported by the police in the STATS19 dataset in 2023, with 29 columns
(variables). Values for a sample of these columns are shown below:

``` r
vehicles[c(3, 14:16)]
#> # A tibble: 189,815 × 4
#>    collision_ref_no vehicle_leaving_carriageway hit_object_off_carriageway
#>    <chr>            <chr>                       <chr>                     
#>  1 481356437        Did not leave carriageway   None                      
#>  2 440059930        Did not leave carriageway   None                      
#>  3 401356150        Did not leave carriageway   None                      
#>  4 131392428        Nearside                    Tree                      
#>  5 041363996        Did not leave carriageway   None                      
#>  6 231388706        Did not leave carriageway   None                      
#>  7 010427100        Did not leave carriageway   None                      
#>  8 461304428        Did not leave carriageway   None                      
#>  9 471348135        Did not leave carriageway   None                      
#> 10 041288045        Nearside                    Lamp post                 
#> # ℹ 189,805 more rows
#> # ℹ 1 more variable: first_point_of_impact <chr>
```

The full list of column names in the `vehicles` dataset is:

``` r
names(vehicles)
#>  [1] "collision_index"                  "collision_year"                  
#>  [3] "collision_ref_no"                 "vehicle_reference"               
#>  [5] "vehicle_type"                     "towing_and_articulation"         
#>  [7] "vehicle_manoeuvre"                "vehicle_direction_from"          
#>  [9] "vehicle_direction_to"             "vehicle_location_restricted_lane"
#> [11] "junction_location"                "skidding_and_overturning"        
#> [13] "hit_object_in_carriageway"        "vehicle_leaving_carriageway"     
#> [15] "hit_object_off_carriageway"       "first_point_of_impact"           
#> [17] "vehicle_left_hand_drive"          "journey_purpose_of_driver"       
#> [19] "sex_of_driver"                    "age_of_driver"                   
#> [21] "age_band_of_driver"               "engine_capacity_cc"              
#> [23] "propulsion_code"                  "age_of_vehicle"                  
#> [25] "generic_make_model"               "driver_imd_decile"               
#> [27] "lsoa_of_driver"                   "escooter_flag"                   
#> [29] "driver_distance_banding"
```

## Creating geographic crash data

An important feature of STATS19 data is that the collision 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_collisions()` into the geographic data form of
the [`sf` package](https://cran.r-project.org/package=sf) with the
function `format_sf()` as follows:

``` r
crashes_sf = format_sf(crashes)
#> 12 rows removed with no coordinates
```

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
[`police_boundaries`](https://itsleeds.github.io/stats19/reference/police_boundaries.html),
an `sf` data frame containing all police jurisdictions in England and
Wales).

``` r
library(sf)
#> Linking to GEOS 3.13.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUE
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")
#> old-style crs object detected; please recreate object with a recent sf::st_crs()
crashes_wy = crashes_sf[wy, ]
nrow(crashes_sf)
#> [1] 104246
nrow(crashes_wy)
#> [1] 4249
```

This subsetting has selected the 4,249 crashes which occurred within
West Yorkshire in 2023.

## Joining tables

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:

``` r
sel = casualties$collision_index %in% crashes_wy$collision_index
casualties_wy = casualties[sel, ]
names(casualties_wy)
#>  [1] "collision_index"                    "collision_year"                    
#>  [3] "collision_ref_no"                   "vehicle_reference"                 
#>  [5] "casualty_reference"                 "casualty_class"                    
#>  [7] "sex_of_casualty"                    "age_of_casualty"                   
#>  [9] "age_band_of_casualty"               "casualty_severity"                 
#> [11] "pedestrian_location"                "pedestrian_movement"               
#> [13] "car_passenger"                      "bus_or_coach_passenger"            
#> [15] "pedestrian_road_maintenance_worker" "casualty_type"                     
#> [17] "casualty_imd_decile"                "lsoa_of_casualty"                  
#> [19] "enhanced_casualty_severity"         "casualty_injury_based"             
#> [21] "casualty_adjusted_severity_serious" "casualty_adjusted_severity_slight" 
#> [23] "casualty_distance_banding"
cas_types = casualties_wy %>%
  select(collision_index, casualty_type) %>%
  mutate(n = 1) %>%
  group_by(collision_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 = "collision_index")
```

What just happened? We found the subset of casualties that took place in
West Yorkshire with reference to the `collision_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:

``` r
crashes_wy %>%
  select(collision_index, collision_severity) %>% 
  st_drop_geometry()
#> # A tibble: 4,249 × 2
#>    collision_index collision_severity
#>  * <chr>           <chr>             
#>  1 2023131365503   Serious           
#>  2 2023131379168   Serious           
#>  3 2023131371604   Slight            
#>  4 2023131265999   Serious           
#>  5 2023131289452   Slight            
#>  6 2023131309750   Slight            
#>  7 2023131317107   Slight            
#>  8 2023131360647   Slight            
#>  9 2023131261256   Serious           
#> 10 2023131299611   Serious           
#> # ℹ 4,239 more rows
cas_types[1:2, c("collision_index", "Cyclist")]
#> # A tibble: 2 × 2
#> # Groups:   collision_index [2]
#>   collision_index Cyclist
#>   <chr>             <dbl>
#> 1 2023121345088         1
#> 2 2023122300320         1
cj[1:2, c(1, 5, 34)] %>% st_drop_geometry()
#> # A tibble: 2 × 3
#>   collision_index latitude trunk_road_flag
#> * <chr>              <dbl> <chr>          
#> 1 2023131365503       53.8 Non-trunk      
#> 2 2023131379168       53.8 Non-trunk
```

## Mapping crashes

The join operation added a geometry column to the casualty data,
enabling it to be mapped (for more advanced maps, see the
[vignette](https://itsleeds.github.io/stats19/articles/stats19.html)):

``` r
cex = cj$Total / 3
plot(cj["speed_limit"], cex = cex)
```

<img src="man/figures/README-unnamed-chunk-4-1.png" alt="" width="100%" />

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.

STATS19 data can be used as the basis of road safety research. The map
below, for example, shows the results of an academic paper on the
social, spatial and temporal distribution of bike crashes in West
Yorkshire, which estimated the number of crashes per billion km cycled
based on commuter cycling as a proxy for cycling levels overall (more
sophisticated measures of cycling levels are now possible thanks to new
data sources) (Lovelace, Roberts, and Kellar 2016):

<img src="https://ars.els-cdn.com/content/image/1-s2.0-S136984781500039X-gr9.jpg" alt="" width="100%" />

## Time series analysis

We can also explore seasonal trends in crashes by aggregating crashes by
day of the year:

``` r
library(ggplot2)
head(cj$date)
#> [1] "2023-10-21" "2023-11-01" "2023-11-06" "2023-01-20" "2023-03-22"
#> [6] "2023-05-21"
class(cj$date)
#> [1] "Date"
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")
#> `geom_smooth()` using formula = 'y ~ x'
```

<img src="man/figures/README-crash-date-plot-1.png" alt="" width="100%" />

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.

``` r
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))
```

<img src="man/figures/README-crash-time-plot-1.png" alt="" width="100%" />

Note that cycling manifests distinct morning and afternoon peaks (see
Lovelace, Roberts, and Kellar 2016 for more on this).

## Usage in research and policy contexts

Examples of how the package can been used for policy making include:

- Use of the package in a web app created by the library service of the
  UK Parliament. See commonslibrary.parliament.uk[^1] screenshots of
  which from December 2019 are shown below, for details.

![](https://user-images.githubusercontent.com/1825120/70164249-bf730080-16b8-11ea-96d8-ec92c0b5cc69.png)

- Use of methods taught in the
  [stats19-training](https://docs.ropensci.org/stats19/articles/stats19-training.html)
  vignette by road safety analysts at Essex Highways and the Safer Essex
  Roads Partnership ([SERP](https://saferessexroads.org/)) to inform the
  deployment of proactive front-line police enforcement in the region
  (credit: Will Cubbin).

- Mention of road crash data analysis based on the package in an
  [article](https://www.theguardian.com/cities/2019/oct/07/a-deadly-problem-should-we-ban-suvs-from-our-cities)
  on urban SUVs. The question of how vehicle size and type relates to
  road safety is an important area of future research. A starting point
  for researching this topic can be found in the
  [`stats19-vehicles`](https://docs.ropensci.org/stats19/articles/stats19-vehicles.html)
  vignette, representing a possible next step in terms of how the data
  can be used.

## Next steps

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. For more on these next steps, see the package’s introductory
[vignette](https://itsleeds.github.io/stats19/articles/stats19.html).

## Further information

The **stats19** package builds on previous work, including:

- code in the [bikeR](https://github.com/Robinlovelace/bikeR) repo
  underlying an academic paper on collisions involving cyclists
- functions in [**stplanr**](https://docs.ropensci.org/stplanr/) for
  downloading Stats19 data
- updated functions related to the
  [CyIPT](https://github.com/cyipt/stats19) project

[![ropensci_footer](https://ropensci.org/public_images/ropensci_footer.png)](https://ropensci.org)

## References

<div id="refs" class="references csl-bib-body hanging-indent"
entry-spacing="0">

<div id="ref-lovelace_stats19_2019" class="csl-entry">

Lovelace, Robin, Malcolm Morgan, Layik Hama, Mark Padgham, and M
Padgham. 2019. “Stats19 A Package for Working with Open Road Crash
Data.” *Journal of Open Source Software* 4 (33): 1181.
<https://doi.org/10.21105/joss.01181>.

</div>

<div id="ref-lovelace_who_2016" class="csl-entry">

Lovelace, Robin, Hannah Roberts, and Ian Kellar. 2016. “Who, Where,
When: The Demographic and Geographic Distribution of Bicycle Crashes in
West Yorkshire.” *Transportation Research Part F: Traffic Psychology and
Behaviour*, Bicycling and bicycle safety, 41, Part B.
<https://doi.org/10.1016/j.trf.2015.02.010>.

</div>

</div>

[^1]:  Got to the following URL:
    commonslibrary.parliament.uk/constituency-data-traffic-accidents
    online
