njtr1 is a package that makes it easy to download New Jersey car crash data that is released by the New Jersey Department of Transportation. This package makes it easy to access detailed information on a statewide or county-by-county process that can assist those interested in studying trends in vehicle crashes in New Jersey.
The main function of this package is get_njtr1(). This function allows you to specify the specific table of NJTR-1 data that you'd like to query for a given year.
There are 5 different tables available with NJTR-1 data and published by the NJDOT. We will take a look at each of them in the following examples.
get_njtr1(year = 2019, type = "Accidents")
## # A tibble: 283,210 x 50
## id county_name municipality_na~ crash_date crash_day_of_we~ crash_time
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 2019~ ATLANTIC ABSECON CITY 02/14/2019 TH 1005
## 2 2019~ ATLANTIC ABSECON CITY 01/28/2019 MO 0953
## 3 2019~ ATLANTIC ABSECON CITY 01/01/2019 TU 0853
## 4 2019~ ATLANTIC ABSECON CITY 01/02/2019 WE 2200
## 5 2019~ ATLANTIC ABSECON CITY 01/09/2019 WE 1253
## 6 2019~ ATLANTIC ABSECON CITY 01/11/2019 FR 0855
## 7 2019~ ATLANTIC ABSECON CITY 01/11/2019 FR 1110
## 8 2019~ ATLANTIC ABSECON CITY 01/13/2019 SU 1007
## 9 2019~ ATLANTIC ABSECON CITY 01/16/2019 WE 2000
## 10 2019~ ATLANTIC ABSECON CITY 01/18/2019 FR 1738
## # ... with 283,200 more rows, and 44 more variables: police_dept_code <chr>,
## # police_department <chr>, police_station <chr>, total_killed <dbl>,
## # total_injured <dbl>, pedestrians_killed <dbl>, pedestrians_injured <dbl>,
## # severity <chr>, intersection <chr>, alcohol_involved <chr>,
## # hazmat_involved <chr>, crash_type_code <chr>,
## # total_vehicles_involved <dbl>, crash_location <chr>,
## # location_direction <chr>, route <dbl>, route_suffix <dbl>,
## # sri_std_rte_identifier <chr>, milepost <dbl>, road_system <chr>,
## # road_character <lgl>, road_horizontal_alignment <chr>, road_grade <chr>,
## # road_surface_type <chr>, surface_condition <chr>, light_condition <chr>,
## # environmental_condition <chr>, road_divided_by <chr>,
## # temporary_traffic_control_zone <chr>, distance_to_cross_street <dbl>,
## # unit_of_measurement <chr>, directn_from_cross_street <chr>,
## # cross_street_name <chr>, is_ramp <lgl>, ramp_tofrom_route_name <chr>,
## # ramp_tofrom_route_direction <chr>, posted_speed <dbl>,
## # posted_speed_cross_street <dbl>, first_harmful_event <chr>, latitude <lgl>,
## # longitude <lgl>, cell_phone_in_use_flag <chr>, other_property_damage <chr>,
## # reporting_badge_no <chr>
get_njtr1(year = 2019, type = "Drivers")
## # A tibble: 538,367 x 22
## id vehicle_number driver_city driver_state driver_zip_code
## <chr> <dbl> <chr> <chr> <chr>
## 1 2019~ 1 PLEASANTVI~ NJ 08232
## 2 2019~ 1 ATLANTIC C~ NJ 08401
## 3 2019~ 2 ABSECON NJ 08201
## 4 2019~ 1 PLEASANTVI~ NJ 08232
## 5 2019~ 1 ABSECON NJ 08201
## 6 2019~ 2 ABSECON NJ 08201
## 7 2019~ 1 GALLOWAY NJ 08205
## 8 2019~ 2 EGG HARBOR~ NJ 08234
## 9 2019~ 1 ATLANTIC C~ NJ 08401
## 10 2019~ 2 OCEAN CITY NJ 08226
## # ... with 538,357 more rows, and 17 more variables:
## # driver_license_state <chr>, driver_dob <chr>, driver_sex <chr>,
## # alcohol_test_given <chr>, alcohol_test_type <chr>,
## # alcohol_test_results <dbl>, charge_1 <chr>, summons_1 <chr>,
## # charge_2 <chr>, summons_2 <chr>, charge_3 <chr>, summons_3 <chr>,
## # charge_4 <chr>, summons_4 <chr>, driver_physical_status_1 <lgl>,
## # driver_physical_status_2 <chr>, NA <lgl>
get_njtr1(year = 2019, type = "Vehicles")
## # A tibble: 538,353 x 40
## id vehicle_number insurance_compa~ owner_state make_of_vehicle
## <chr> <dbl> <chr> <chr> <chr>
## 1 2019~ 1 134 NJ HONDA
## 2 2019~ 1 411 NJ VOLKSWAGEN
## 3 2019~ 2 134 NJ JEEP
## 4 2019~ 1 <NA> NJ MERCURY
## 5 2019~ 1 135 NJ NISSAN
## 6 2019~ 2 963 NJ BUICK
## 7 2019~ 1 148 NJ FORD
## 8 2019~ 2 054 NJ LEXUS
## 9 2019~ 1 487 NJ CHEVROLET
## 10 2019~ 2 148 NJ TOYOTA
## # ... with 538,343 more rows, and 35 more variables: model_of_vehicle <chr>,
## # color_of_vehicle <chr>, year_of_vehicle <dbl>, license_plate_state <chr>,
## # vehicle_weight_rating <dbl>, towed <lgl>, removed_by <dbl>,
## # drivenleft_at_scenetowed <dbl>, initial_impact_location <chr>,
## # principal_damage_location <chr>, extent_of_damage <chr>,
## # traffic_controls_present <chr>, vehicle_type <chr>, vehicle_use <chr>,
## # special_function_vehicles <chr>, cargo_body_type <chr>,
## # contributing_circumstances_1 <chr>, contributing_circumstances_2 <chr>,
## # direction_of_travel <chr>, precrash_action <chr>,
## # first_sequence_of_events <chr>, second_sequence_of_events <chr>,
## # third_sequence_of_events <chr>, fourth_sequence_of_events <chr>,
## # most_harmful_event <chr>, oversizeoverweight_permit <chr>,
## # hazmat_status <lgl>, hazmat_class <lgl>, hazmat_placard <lgl>,
## # usdot_number <chr>, mcmx_number <dbl>, usdot__other_flag <lgl>,
## # usdot__other_number <lgl>, carrier_name <chr>, hit__run_driver_flag <chr>
Each of the vehicle records contain a field name insurance_company_code that identifies the name of the insurance company that covers the vehicle involved in an accident.
This code can be used to join the vehicle data with a dataset containing details for every insurance company that does business in New Jersey.
Access the insurance from the package like so:
data("insurance")
head(insurance)
## # A tibble: 6 x 9
## ID_NO NAME ADDRESS_1 ADDRESS_2 ADDRESS_3 CITY STATE ZIP NOTES
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 001 SAMSUNG FIR~ 25 CHALLENG~ <NA> <NA> RIDGE~ NJ 07660 COMM O~
## 2 002 BROTHERHOOD~ PO BOX 2227 <NA> <NA> FORT ~ IN 07661 COMM O~
## 3 003 MID-CENTURY~ 4680 WILSHI~ <NA> <NA> LOS A~ CA 07662 PRIV P~
## 4 004 ACE PROPERT~ 1601 CHESTN~ PO BOX 4~ <NA> PHILA~ PA 07663 COMM O~
## 5 005 FIRST NONPR~ 111 NORTH C~ SUITE 801 <NA> CHICA~ IL 07664 COMM O~
## 6 006 EVEREST PRE~ 477 MARTINS~ <NA> <NA> LIBER~ NJ 07665 COMM O~
The insurance dataset contains a field ID_NO which is the same as insurance_company_code in the Vehicles table. This common field can be used for performing join operations.
get_njtr1(year = 2019, type = "Pedestrians")
## # A tibble: 7,946 x 35
## id pedestrian_numb~ physical_condit~ address_city address_state
## <chr> <dbl> <chr> <chr> <chr>
## 1 2019~ 1 02 PLEASANTVIL~ NJ
## 2 2019~ 1 04 ABSECON NJ
## 3 2019~ 1 03 GALLOWAY NJ
## 4 2019~ 1 01 LINDENWOLD NJ
## 5 2019~ 31 <NA> TUCKERTON NJ
## 6 2019~ 1 03 ABSECON NJ
## 7 2019~ 1 02 EGG HARBOR ~ NJ
## 8 2019~ 1 02 EGG HARBOR ~ NJ
## 9 2019~ 1 03 ABSECON NJ
## 10 2019~ 1 02 ABSECON NJ
## # ... with 7,936 more rows, and 30 more variables: address_zip <chr>,
## # date_of_birth <chr>, age <chr>, sex <chr>, alcohol_test_given <chr>,
## # alcohol_test_type <chr>, alcohol_test_results <lgl>, charge_1 <chr>,
## # summons_1 <chr>, charge_2 <chr>, summons_2 <chr>, charge_3 <lgl>,
## # summons_3 <lgl>, charge_4 <chr>, summons_4 <chr>, multi_charge_flag <lgl>,
## # traffic_controls <chr>, contributing_circumstances_1 <chr>,
## # contributing_circumstances_2 <chr>, direction_of_travel <chr>,
## # precrash_action <chr>, location_of_most_severe_injury <chr>,
## # type_of_most_severe_phys_injury <chr>, refused_medical_attention <chr>,
## # safety_equipment_used <chr>, hospital_code <dbl>, physical_status_1 <chr>,
## # physical_status_2 <chr>, is_bycyclist <chr>, is_other <lgl>
get_njtr1(year = 2019, type = "Occupants")
## # A tibble: 651,507 x 15
## id vehicle_number occupant_number physical_condit~ position_in_on_~
## <chr> <dbl> <dbl> <chr> <chr>
## 1 2019~ 1 1 <NA> 01
## 2 2019~ 1 1 04 01
## 3 2019~ 1 2 <NA> 06
## 4 2019~ 2 3 04 01
## 5 2019~ 1 1 <NA> 01
## 6 2019~ 1 1 04 01
## 7 2019~ 1 2 04 03
## 8 2019~ 2 3 04 01
## 9 2019~ 2 4 04 03
## 10 2019~ 2 5 <NA> 06
## # ... with 651,497 more rows, and 10 more variables: ejection_code <chr>,
## # age <chr>, sex <chr>, location_of_most_severe_injury <chr>,
## # type_of_most_severe_phys_injury <chr>, refused_medical_attention <chr>,
## # safety_equipment_available <chr>, safety_equipment_used <chr>,
## # airbag_deployment <chr>, NA <dbl>