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",
  ...
)

Arguments

year: A year matching file names on the STATS19 data release page e.g. 2020
type: One of 'collision', 'casualty', 'Vehicle'; defaults to 'collision'. This text string is used to match the file names released by the DfT.
data_dir: Parent directory for all downloaded files. Defaults to tempdir().
file_name: The file name (DfT named) to download.
format: Switch to return raw read from file, default is TRUE.
ask: Should you be asked whether or not to download the files? TRUE by default.
silent: Boolean. If FALSE (default value), display useful progress messages on the screen.
output_format: A string that specifies the desired output format. The default value is "tibble". Other possible values are "data.frame", "sf" and "ppp", that, respectively, returns objects of class data.frame, sf::sf and spatstat.geom::ppp. Any other string is ignored and a tibble output is returned. See details and examples.
...: Other arguments be passed to format_sf() or format_ppp() functions. Read and run the examples.

Details

This function uses 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. 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.

Examples

# \donttest{
if(curl::has_internet()) {
x = get_stats19(2022, silent = TRUE, format = TRUE)
class(x)
# data.frame output
x = get_stats19(2022, silent = TRUE, output_format = "data.frame")
class(x)

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

# 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)

# or even more fancy examples where we subset all the events occurred in a
# pre-defined polygon area

# The following example requires osmdata package
# greater_london_sf_polygon = osmdata::getbb(
# "Greater London, UK",
# format_out = "sf_polygon"
# )
# spatstat works only with planar coordinates
# greater_london_sf_polygon = sf::st_transform(greater_london_sf_polygon, 27700)
# then we extract the coordinates and create the window object.
# greater_london_polygon = sf::st_coordinates(greater_london_sf_polygon)[, c(1, 2)]
# greater_london_window = spatstat.geom::owin(poly = greater_london_polygon)

# greater_london_ppp = get_stats19(2022, output_format = "ppp", window = greater_london_window)
# spatstat.geom::plot.ppp(greater_london_ppp, use.marks = FALSE, clipwin = greater_london_window)
}
}
}
#> 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 column
#> 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 column
#> 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 column
#> 22 rows removed with no coordinates
#> 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 column
#> 22 rows removed with no coordinates
#> 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 column
#> 22 rows removed with no coordinates
#> Warning: some mark values are NA in the point pattern x
#> 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 column
#> 22 rows removed with no coordinates
#> Warning: 105096 points were rejected as lying outside the specified window
#> Warning: some mark values are NA in the point pattern x

# }

Download, read and format STATS19 data in one function.

Usage

Arguments

Details

See also

Examples

About

Community

Resources