A subset of the complaints received by the Department of Buildings (DOB) in New York City, USA.
Source
https://data.cityofnewyork.us/Housing-Development/DOB-Complaints-Received/eabe-havv/about_data
Arguments
- ...
Arguments passed to
pins::pin_read().
Details
A data frame with 4,234 rows and 11 columns:
- days_to_disposition
Days to disposition of the complaint
- status
Status of the complaint
- year_entered
Year the complaint was entered
- latitude, longitude
Geographic coordinates
- borough
Borough
- special_district
Special district
- unit
Unit dispositioning the complaint
- community_board
Community board. 3-digit identifier: Borough code = first position, last 2 = community board
- complaint_category
Complaint category
- complaint_priority
Complaint priority
tibble print
data_building_complaints()
#> # A tibble: 4,234 x 11
#> days_to_disposition status year_entered latitude longitude borough
#> <dbl> <chr> <fct> <dbl> <dbl> <fct>
#> 1 72 ACTIVE 2023 40.7 -74.0 Brooklyn
#> 2 1 ACTIVE 2023 40.6 -74.0 Brooklyn
#> 3 41 ACTIVE 2023 40.7 -73.9 Queens
#> 4 45 ACTIVE 2023 40.7 -73.8 Queens
#> 5 16 ACTIVE 2023 40.6 -74.0 Brooklyn
#> 6 62 ACTIVE 2023 40.7 -73.8 Queens
#> 7 56 ACTIVE 2023 40.7 -74.0 Brooklyn
#> 8 11 ACTIVE 2023 40.7 -74.0 Brooklyn
#> 9 35 ACTIVE 2023 40.7 -73.8 Queens
#> 10 38 ACTIVE 2023 40.7 -73.9 Queens
#> # i 4,224 more rows
#> # i 5 more variables: special_district <fct>, unit <fct>,
#> # community_board <fct>, complaint_category <fct>, complaint_priority <fct>glimpse()
tibble::glimpse(data_building_complaints())
#> Rows: 4,234
#> Columns: 11
#> $ days_to_disposition <dbl> 72, 1, 41, 45, 16, 62, 56, 11, 35, 38, 39, 106, 1,~
#> $ status <chr> "ACTIVE", "ACTIVE", "ACTIVE", "ACTIVE", "ACTIVE", ~
#> $ year_entered <fct> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 20~
#> $ latitude <dbl> 40.66173, 40.57668, 40.73242, 40.68245, 40.63156, ~
#> $ longitude <dbl> -73.98297, -74.00453, -73.87630, -73.79367, -73.99~
#> $ borough <fct> Brooklyn, Brooklyn, Queens, Queens, Brooklyn, Quee~
#> $ special_district <fct> None, None, None, None, None, None, None, None, No~
#> $ unit <fct> Q-L, Q-L, SPOPS, Q-L, BKLYN, Q-L, Q-L, SPOPS, Q-L,~
#> $ community_board <fct> 307, 313, 404, 412, 312, 406, 306, 306, 409, 404, ~
#> $ complaint_category <fct> 45, 45, 49, 45, 31, 45, 45, 49, 45, 45, 45, 4A, 31~
#> $ complaint_priority <fct> B, B, C, B, C, B, B, C, B, B, B, B, C, C, B, B, B,~Examples
# \donttest{
data_building_complaints()
#> # A tibble: 4,234 × 11
#> days_to_disposition status year_entered latitude longitude borough
#> <dbl> <chr> <fct> <dbl> <dbl> <fct>
#> 1 72 ACTIVE 2023 40.7 -74.0 Brooklyn
#> 2 1 ACTIVE 2023 40.6 -74.0 Brooklyn
#> 3 41 ACTIVE 2023 40.7 -73.9 Queens
#> 4 45 ACTIVE 2023 40.7 -73.8 Queens
#> 5 16 ACTIVE 2023 40.6 -74.0 Brooklyn
#> 6 62 ACTIVE 2023 40.7 -73.8 Queens
#> 7 56 ACTIVE 2023 40.7 -74.0 Brooklyn
#> 8 11 ACTIVE 2023 40.7 -74.0 Brooklyn
#> 9 35 ACTIVE 2023 40.7 -73.8 Queens
#> 10 38 ACTIVE 2023 40.7 -73.9 Queens
#> # ℹ 4,224 more rows
#> # ℹ 5 more variables: special_district <fct>, unit <fct>,
#> # community_board <fct>, complaint_category <fct>,
#> # complaint_priority <fct>
# }