Download, read and format STATS19 data in one function.

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

Arguments

year

Single year for which file is to be downloaded.

type

One of 'Accidents', 'Casualties', 'Vehicles'; defaults to 'Accidents'. Or any variation of to search the file names with such as "acc" or "accid".

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::ppp. Any other string is ignored and a tibble output is returned. See details and examples.

...

Other arguments that should be passed to format_sf() or format_ppp() functions. Read and run the examples.

Details

This function utilizes dl_stats19 and read_* functions and returns a tibble (default), a data.frame, a sf object or a ppp object (according to the output_format parameter). The file downloaded would be for a specific year (e.g. 2017) or multiple years (e.g. c(2017, 2018)). See examples.

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, respectively. See examples.

See also

Examples

# \donttest{ # default tibble output x = get_stats19(2019)
#> Files identified: DfTRoadSafety_Accidents_2019.zip
#> http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/DfTRoadSafety_Accidents_2019.zip
#> Attempt downloading from:
#> Data saved at /tmp/RtmpPSYWX6/DfTRoadSafety_Accidents_2019/Road Safety Data - Accidents 2019.csv
#> Reading in:
#> /tmp/RtmpPSYWX6/DfTRoadSafety_Accidents_2019/Road Safety Data - Accidents 2019.csv
#> date and time columns present, creating formatted datetime column
class(x)
#> [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"
x = get_stats19(2017, silent = TRUE)
#> date and time columns present, creating formatted datetime column
# data.frame output x = get_stats19(2019, silent = TRUE, output_format = "data.frame")
#> date and time columns present, creating formatted datetime column
class(x)
#> [1] "data.frame"
# multiple years get_stats19(c(2017, 2018), silent = TRUE)
#> date and time columns present, creating formatted datetime column
#> date and time columns present, creating formatted datetime column
#> # A tibble: 252,617 x 33 #> accident_index location_eastin… location_northi… longitude latitude #> <chr> <int> <int> <dbl> <dbl> #> 1 2017010001708 532920 196330 -0.0801 51.7 #> 2 2017010009342 526790 181970 -0.174 51.5 #> 3 2017010009344 535200 181260 -0.0530 51.5 #> 4 2017010009348 534340 193560 -0.0607 51.6 #> 5 2017010009350 533680 187820 -0.0724 51.6 #> 6 2017010009351 514510 172370 -0.354 51.4 #> 7 2017010009353 508640 181870 -0.435 51.5 #> 8 2017010009354 527880 181950 -0.158 51.5 #> 9 2017010009357 520940 192820 -0.254 51.6 #> 10 2017010009358 531430 178450 -0.108 51.5 #> # … with 252,607 more rows, and 28 more variables: police_force <chr>, #> # accident_severity <chr>, number_of_vehicles <int>, #> # number_of_casualties <int>, date <date>, day_of_week <chr>, time <chr>, #> # local_authority_district <chr>, local_authority_highway <chr>, #> # first_road_class <chr>, first_road_number <int>, road_type <chr>, #> # speed_limit <int>, junction_detail <chr>, junction_control <chr>, #> # second_road_class <chr>, second_road_number <int>, #> # pedestrian_crossing_human_control <chr>, #> # pedestrian_crossing_physical_facilities <chr>, light_conditions <chr>, #> # weather_conditions <chr>, road_surface_conditions <chr>, #> # special_conditions_at_site <chr>, carriageway_hazards <chr>, #> # urban_or_rural_area <chr>, #> # did_police_officer_attend_scene_of_accident <int>, #> # lsoa_of_accident_location <chr>, datetime <dttm>
# sf output x_sf = get_stats19(2017, silent = TRUE, output_format = "sf")
#> date and time columns present, creating formatted datetime column
#> 19 rows removed with no coordinates
# sf output with lonlat coordinates x_sf = get_stats19(2017, silent = TRUE, output_format = "sf", lonlat = TRUE)
#> date and time columns present, creating formatted datetime column
#> 29 rows removed with no coordinates
sf::st_crs(x_sf)
#> Coordinate Reference System: #> User input: EPSG:4326 #> wkt: #> GEOGCRS["WGS 84", #> DATUM["World Geodetic System 1984", #> ELLIPSOID["WGS 84",6378137,298.257223563, #> LENGTHUNIT["metre",1]]], #> PRIMEM["Greenwich",0, #> ANGLEUNIT["degree",0.0174532925199433]], #> CS[ellipsoidal,2], #> AXIS["geodetic latitude (Lat)",north, #> ORDER[1], #> ANGLEUNIT["degree",0.0174532925199433]], #> AXIS["geodetic longitude (Lon)",east, #> ORDER[2], #> ANGLEUNIT["degree",0.0174532925199433]], #> USAGE[ #> SCOPE["unknown"], #> AREA["World"], #> BBOX[-90,-180,90,180]], #> ID["EPSG",4326]]
# multiple years get_stats19(c(2017, 2018), silent = TRUE, output_format = "sf")
#> date and time columns present, creating formatted datetime column
#> 19 rows removed with no coordinates
#> date and time columns present, creating formatted datetime column
#> 55 rows removed with no coordinates
#> Simple feature collection with 252543 features and 31 fields #> geometry type: POINT #> dimension: XY #> bbox: xmin: 73639 ymin: 10235 xmax: 655391 ymax: 1209512 #> projected CRS: OSGB 1936 / British National Grid #> # A tibble: 252,543 x 32 #> accident_index longitude latitude police_force accident_severi… #> * <chr> <dbl> <dbl> <chr> <chr> #> 1 2017010001708 -0.0801 51.7 Metropolita… Fatal #> 2 2017010009342 -0.174 51.5 Metropolita… Slight #> 3 2017010009344 -0.0530 51.5 Metropolita… Slight #> 4 2017010009348 -0.0607 51.6 Metropolita… Slight #> 5 2017010009350 -0.0724 51.6 Metropolita… Serious #> 6 2017010009351 -0.354 51.4 Metropolita… Slight #> 7 2017010009353 -0.435 51.5 Metropolita… Slight #> 8 2017010009354 -0.158 51.5 Metropolita… Slight #> 9 2017010009357 -0.254 51.6 Metropolita… Serious #> 10 2017010009358 -0.108 51.5 Metropolita… Serious #> # … with 252,533 more rows, and 27 more variables: number_of_vehicles <int>, #> # number_of_casualties <int>, date <date>, day_of_week <chr>, time <chr>, #> # local_authority_district <chr>, local_authority_highway <chr>, #> # first_road_class <chr>, first_road_number <int>, road_type <chr>, #> # speed_limit <int>, junction_detail <chr>, junction_control <chr>, #> # second_road_class <chr>, second_road_number <int>, #> # pedestrian_crossing_human_control <chr>, #> # pedestrian_crossing_physical_facilities <chr>, light_conditions <chr>, #> # weather_conditions <chr>, road_surface_conditions <chr>, #> # special_conditions_at_site <chr>, carriageway_hazards <chr>, #> # urban_or_rural_area <chr>, #> # did_police_officer_attend_scene_of_accident <int>, #> # lsoa_of_accident_location <chr>, datetime <dttm>, geometry <POINT [m]>
if (requireNamespace("spatstat", quietly = TRUE)) { # ppp output x_ppp = get_stats19(2017, silent = TRUE, output_format = "ppp") spatstat::plot.ppp(x_ppp, use.marks = FALSE) # Multiple years get_stats19(c(2017, 2018), 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::owin( xrange = c(425046.1, 435046.1), yrange = c(428577.2, 438577.2) ) leeds_ppp = get_stats19(2017, silent = TRUE, output_format = "ppp", window = leeds_window) spatstat::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::owin(poly = greater_london_polygon) # greater_london_ppp = get_stats19(2017, output_format = "ppp", window = greater_london_window) # spatstat::plot.ppp(greater_london_ppp, use.marks = FALSE, clipwin = greater_london_window) }
#> date and time columns present, creating formatted datetime column
#> 19 rows removed with no coordinates
#> Warning: some mark values are NA in the point pattern x
#> date and time columns present, creating formatted datetime column
#> 19 rows removed with no coordinates
#> Warning: some mark values are NA in the point pattern x
#> date and time columns present, creating formatted datetime column
#> 55 rows removed with no coordinates
#> Warning: some mark values are NA in the point pattern x
#> Warning: some mark values are NA in the point pattern x
#> date and time columns present, creating formatted datetime column
#> 19 rows removed with no coordinates
#> Warning: 128936 points were rejected as lying outside the specified window
#> Warning: some mark values are NA in the point pattern x
# }