| data_elevators {modeldatatoo} | R Documentation |
elevators data set
Description
A data set containing information of a subset of the elevators in NYC. The data set has been filtered to contain active elevators with non-missing speed.
Usage
data_elevators(...)
Arguments
... |
Arguments passed to |
Details
- device_number
Unique identify number for the elevator
- bin
Building Identification Number
- borough
Regional subdivisions of NYC. One of "Manhattan", "Bronx", "Brooklyn", "Queens", or "Staten Island"
- tax_block
Id for tax block. Smaller than borough
- tax_lot
Id for tax block. Smaller than tax_block
- house_number
House number, very poorly parsed. Use with caution
- street_name
Street name, very poorly parsed. Use with caution
- zip_code
Zip code, formatted to 5 digits. 0 and 99999 are marked as NA
- device_type
Type of device. Most common type is "Passenger Elevator"
- lastper_insp_date
Date, refers to the last periodic inspection by the Department of Buildings. These dates will no longer be accurate, as they were collected by November 2015
- approval_date
Date of approval for elevator
- manufacturer
Name of manufacturer, poorly cleaned. Most assigned NA
- travel_distance
Distance travelled, not cleaned. Mixed formats
- speed_fpm
Speed in feet/minute
- capacity_lbs
Capacity in lbs
- car_buffer_type
Buffer type. A buffer is a device designed to stop a descending car or counterweight beyond its normal limit and to soften the force with which the elevator runs into the pit during an emergency. Takes values "Oil", "Spring", and NA
- governor_type
Governor type, An overspeed governor is an elevator device which acts as a stopping mechanism in case the elevator runs beyond its rated speed
- machine_type
Machine type, labels unknown.
- safety_type
Safety type, labels unknown.
- mode_operation
Operation mode, labels unknown.
- floor_from
Lowest floor, not cleaned. Mixed formats
- floor_to
Highest floor, not cleaned. Mixed formats
- latitude
Latitude of elevator
- longitude
Longitude of elevator
- elevators_per_building
number of elevators in building
...
Value
tibble
tibble print
data_elevators() #> # A tibble: 35,042 x 25 #> device_number bin tax_block tax_lot house_number street_name zip_code #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 1D10028 1024795 1021 26 1614 BROADWAY 10019 #> 2 1D10094 1041822 1392 25 53 E 77TH ST 10021 #> 3 1D10097 1038223 1323 1 201 E 49 ST 10017 #> 4 1D10146 1080443 1274 6 40 CENTRAL PARK S~ <NA> #> 5 1D10200 1085777 1074 24 651 TENTH AVENUE <NA> #> 6 1D10301 1002075 181 16 179 FRANKLIN STREET 10013 #> 7 1D10302 1010518 606 4 121 WEST 10 STREET 10011 #> 8 1D10303 1085955 1329 1 915 3 AVENUE 10022 #> 9 1D10304 1044058 1430 5 220 E. 76 ST 10021 #> 10 1D10305 1087468 1951 4 133 MORNINGSIDE AV~ <NA> #> # i 35,032 more rows #> # i 18 more variables: borough <fct>, device_type <chr>, #> # lastper_insp_date <date>, approval_date <date>, manufacturer <chr>, #> # travel_distance <chr>, speed_fpm <dbl>, capacity_lbs <dbl>, #> # car_buffer_type <chr>, governor_type <chr>, machine_type <chr>, #> # safety_type <chr>, mode_operation <chr>, floor_from <chr>, floor_to <chr>, #> # latitude <dbl>, longitude <dbl>, elevators_per_building <int>
glimpse()
tibble::glimpse(data_elevators()) #> Rows: 35,042 #> Columns: 25 #> $ device_number <chr> "1D10028", "1D10094", "1D10097", "1D10146", "1D~ #> $ bin <chr> "1024795", "1041822", "1038223", "1080443", "10~ #> $ tax_block <chr> "1021", "1392", "1323", "1274", "1074", "181", ~ #> $ tax_lot <chr> "26", "25", "1", "6", "24", "16", "4", "1", "5"~ #> $ house_number <chr> "1614", "53", "201", "40", "651", "179", "121",~ #> $ street_name <chr> "BROADWAY", "E 77TH ST", "E 49 ST", "CENTRAL PA~ #> $ zip_code <chr> "10019", "10021", "10017", NA, NA, "10013", "10~ #> $ borough <fct> Manhattan, Manhattan, Manhattan, Manhattan, Man~ #> $ device_type <chr> "Dumbwaiter", "Dumbwaiter", "Dumbwaiter", "Dumb~ #> $ lastper_insp_date <date> 2015-09-18, 2015-08-07, 2015-04-02, 2014-10-15~ #> $ approval_date <date> 2006-03-07, 2006-05-15, 1998-09-21, 2010-08-02~ #> $ manufacturer <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~ #> $ travel_distance <chr> "16'4\"", NA, "23", "8'", "24 FT", "9'0", "12'0~ #> $ speed_fpm <dbl> 50, 25, 50, 50, 50, 50, 50, 50, 50, 100, 100, 5~ #> $ capacity_lbs <dbl> 500, 500, 500, 500, NA, 500, 300, 500, 500, 500~ #> $ car_buffer_type <chr> "Spring", NA, NA, NA, NA, NA, "Spring", NA, NA,~ #> $ governor_type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~ #> $ machine_type <chr> NA, "OD", "BD", "BD", NA, "OD", "OD", "BD", "OG~ #> $ safety_type <chr> "I", NA, "I", NA, NA, "I", "I", NA, "I", NA, NA~ #> $ mode_operation <chr> "A", "P", "A", "A", NA, "A", "A", "A", "A", "P"~ #> $ floor_from <chr> "B", "SB", "B", "B", "C", "BAS", "B", "C", "BMT~ #> $ floor_to <chr> "1", "3", "2", "1", "G", "1", "1", "2", "4", "5~ #> $ latitude <dbl> 40.76088, 40.77502, 40.75518, 40.76500, 40.7622~ #> $ longitude <dbl> -73.98391, -73.96256, -73.97079, -73.97573, -73~ #> $ elevators_per_building <int> 11, 2, 1, 1, 2, 2, 1, 1, 1, 5, 5, 1, 2, 1, 1, 2~
Source
https://github.com/datanews/elevators
Examples
data_elevators()