get_stats19.RdDownload, read and format STATS19 data in one function.
get_stats19(year = NULL, type = "accidents", data_dir = tempdir(), file_name = NULL, format = TRUE, ask = TRUE)
| 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 |
| file_name | The file name (DfT named) to download. |
| format | Switch to return raw read from file, default is |
| ask | Should you be asked whether or not to download the files? |
This function utilizes dl_stats19 and read_* functions
and retuns a df. The file downloaded would be for a specific year (e.g 2017).
As this function uses dl_stats19 function, it can download
many MB of data so ensure you have a sufficient disk space.
get_stats19(year = 2017)#>#>#>#>#>#>#> # A tibble: 129,982 x 32 #> 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 129,972 more rows, and 27 more variables: police_force <chr>, #> # accident_severity <chr>, number_of_vehicles <int>, #> # number_of_casualties <int>, date <dttm>, 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>get_stats19(year = 2009)#>#>#>#>#>#>#> # A tibble: 163,554 x 32 #> accident_index location_eastin… location_northi… longitude latitude #> <chr> <int> <int> <dbl> <dbl> #> 1 200901BS70001 524910 180800 -0.201 51.5 #> 2 200901BS70002 525050 181040 -0.199 51.5 #> 3 200901BS70003 526490 177990 -0.180 51.5 #> 4 200901BS70004 524800 180300 -0.203 51.5 #> 5 200901BS70005 526930 177490 -0.173 51.5 #> 6 200901BS70006 526060 178730 -0.186 51.5 #> 7 200901BS70007 526580 177270 -0.179 51.5 #> 8 200901BS70008 526550 178580 -0.179 51.5 #> 9 200901BS70009 527310 179100 -0.167 51.5 #> 10 200901BS70010 526250 177370 -0.183 51.5 #> # … with 163,544 more rows, and 27 more variables: police_force <chr>, #> # accident_severity <chr>, number_of_vehicles <int>, #> # number_of_casualties <int>, date <dttm>, 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>