Work with Open Road Traffic Casualty Data from Great Britain


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Documentation for package ‘stats19’ version 3.0.3

Help Pages

accidents_sample Sample of stats19 data (2022 collisions)
accidents_sample_raw Sample of stats19 data (2022 collisions)
casualties_sample Sample of stats19 data (2022 casualties)
casualties_sample_raw Sample of stats19 data (2022 casualties)
check_input_file Local helper to be reused.
dl_stats19 Download STATS19 data for a year
file_names stats19 file names for easy access
file_names_old stats19 file names for easy access
find_file_name Find file names within stats19::file_names.
format_casualties Format STATS19 casualties
format_collisions Format STATS19 'collisions' data
format_column_names Format column names of raw STATS19 data
format_ppp Convert STATS19 data into ppp (spatstat) format.
format_sf Format convert STATS19 data into spatial (sf) object
format_vehicles Format STATS19 vehicles data
get_data_directory Get data download dir
get_MOT Download vehicle data from the DVSA MOT API using VRM.
get_stats19 Download, read and format STATS19 data in one function.
get_stats19_adjustments Download and read-in severity adjustment factors
get_ULEZ Download DVLA-based vehicle data from the TfL API using VRM.
get_url Convert file names to urls
locate_files Locate a file on disk
locate_one_file Pin down a file on disk from four parameters.
phrase Generate a phrase for data download purposes
police_boundaries Police force boundaries in England (2016)
read_casualties Read in STATS19 road safety data from .csv files downloaded.
read_collisions Read in STATS19 road safety data from .csv files downloaded.
read_vehicles Read in stats19 road safety data from .csv files downloaded.
schema_original Schema for stats19 data (UKDS)
select_file Interactively select from options
set_data_directory Set data download dir
stats19_schema Stats19 schema and variables
stats19_variables Stats19 schema and variables
vehicles_sample Sample of stats19 data (2022 vehicles)
vehicles_sample_raw Sample of stats19 data (2022 vehicles)