README.md

In austensen/geoclient: Tools to work with NYC's _Geoclient_ REST API

geoclient

Tools to work with NYC's Geoclient REST API.

This packages uses NYC's Geoclient API but is neither endorsed nor supported by the the City of New York.

For information about the Geoclient API visit NYC's Developers Portal.

Installation

Install from Github with remotes:

# install.packages("remotes")
remotes::install_github("austensen/geoclient")

Set up Geoclient API key

You can acquire your Geoclient API Key by first registering with the NYC's API Portal, then adding a "subscription" to the Geoclient User API. Once completed, you will then be able to access your 'Primary key' at any point by visiting the NYC's API Portal and viewing your user profile.

To avoid having to provide the Key with each function call you can use geoclient_api_key() to add your Geoclient Key to your .Renviron file so they can be called securely without being stored in your code.

Basic Usage

There are 6 main location types that can be set with Geoclient: Address, BBL (Borough-Block-Lot), BIN (Building Identification Number), Blockface, Intersection, and Place ("well-known NYC place name"). All of these functions return the results of the Geoclient API call as a dataframe, with additional columns for the arguments provided to the function.

geo_address(
  house_number = "139", 
  street = "MacDougal St", 
  borough = "MN",
  zip = "10012"
)
#> # A tibble: 1 x 154
#>   input_house_num… input_street input_borough input_zip no_results
#>   <chr>            <chr>        <chr>         <chr>     <lgl>     
#> 1 139              MacDougal St manhattan     10012     FALSE     
#> # ... with 149 more variables: assemblyDistrict <chr>, bbl <chr>,
#> #   bblBoroughCode <chr>, bblTaxBlock <chr>, bblTaxLot <chr>, …

You can also pull out just a single column if that is all you need.

df <- tibble::tribble(
  ~num,  ~st,                ~boro,         ~zip,
  "139", "MacDougal St",     "manhattan",   "11231",
  "295", "Lafayette street", NA,            "10012-2722",
  "40",  "WASHINGTON SQ S",  "MN",          NA
)

dplyr::mutate(df, bbl = geo_address(num, st, boro, zip)[["bbl"]])
#> # A tibble: 3 x 5
#>   num   st               boro      zip        bbl       
#>   <chr> <chr>            <chr>     <chr>      <chr>     
#> 1 139   MacDougal St     manhattan 11231      1005430053
#> 2 295   Lafayette street <NA>      10012-2722 1005107502
#> 3 40    WASHINGTON SQ S  MN        <NA>       1005410001

For each of these location types there are two functions in this package that allow the arguments to be supplied either as individual vector, or with a dataframe and bare column names.

geo_address_data(df, num, st, boro, zip)
#> # A tibble: 3 x 241
#>   input_house_num… input_street input_borough input_zip no_results
#>   <chr>            <chr>        <chr>         <chr>     <lgl>     
#> 1 139              MacDougal St manhattan     11231     FALSE     
#> 2 295              Lafayette s… <NA>          10012-27… FALSE     
#> 3 40               WASHINGTON … manhattan     <NA>      FALSE     
#> # ... with 236 more variables: assemblyDistrict <chr>, bbl <chr>,
#> #   bblBoroughCode <chr>, bblTaxBlock <chr>, bblTaxLot <chr>, …

The return dataframe will always be the same length and in the same order, so you can easily add all the return columns to your existing dataframe.

dplyr::bind_cols(df, geo_address_data(df, num, st, boro, zip))
#> # A tibble: 3 x 245
#>   num   st    boro  zip   input_house_num… input_street input_borough
#>   <chr> <chr> <chr> <chr> <chr>            <chr>        <chr>        
#> 1 139   MacD… manh… 11231 139              MacDougal St manhattan    
#> 2 295   Lafa… <NA>  1001… 295              Lafayette s… <NA>         
#> 3 40    WASH… MN    <NA>  40               WASHINGTON … manhattan    
#> # ... with 238 more variables: input_zip <chr>, no_results <lgl>,
#> #   assemblyDistrict <chr>, bbl <chr>, bblBoroughCode <chr>, …

In addition to the 6 location types, Geoclient also provides a single-field search option, which will guess the location type. This can be particularly helpful when you have address data that is not easily separated for use with geo_address().

df <- tibble::tribble(
  ~address,
  "139 MacDougal St manhattan, 11231",
  "295 Lafayette street, 10012-2722",
  "40 WASHINGTON SQ S MN"
)

geo_search_data(df, address)
#> # A tibble: 3 x 233
#>   input_location no_results alleyCrossStree… assemblyDistrict bbl  
#>   <chr>          <lgl>      <chr>            <chr>            <chr>
#> 1 139 MacDougal… FALSE      X                55               3015…
#> 2 295 Lafayette… FALSE      X                66               1005…
#> 3 40 WASHINGTON… FALSE      <NA>             66               1005…
#> # ... with 228 more variables: bblBoroughCode <chr>, bblTaxBlock <chr>,
#> #   bblTaxLot <chr>, boardOfElectionsPreferredLgc <chr>,
#> #   boePreferredStreetName <chr>, …

austensen/geoclient documentation built on Nov. 20, 2021, 11:12 p.m.