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Clean and standardise address fields

Source: R/clean-address.R
clean_addresses.Rd

Normalises raw address, city and ZIP text into geocoder-friendly columns and builds a single-line full_address_clean. Column mappings are supplied with tidy-eval (bare column names). Original address pieces are preserved in *_raw columns. If the input already contains a full_address_clean column in any case style (for example Full_Address_Clean), locatr preserves that user-supplied value as full_address_raw so there is only one canonical full_address_clean column after cleaning.

Usage

clean_addresses(
  data,
  id = NULL,
  address,
  city,
  zip = NULL,
  name = NULL,
  state = "NJ"
)

Arguments

data

A data frame of records with addresses.

id

Optional bare column name holding a unique record identifier. When omitted, record_id is generated from the row number.

address, city

Bare column names for the raw address and city. Required.

zip

Optional bare column name for the raw ZIP/postal code. When omitted, zip_clean is NA.

name

Optional bare column name for the record name (kept as record_name for review/exports). Defaults to NULL.

state

Two-letter state used for all rows. Defaults to "NJ" for the first production workflow; pass another state abbreviation as needed.

Value

data with added columns: record_id, record_name, address_raw, city_raw, zip_raw, optional full_address_raw, address_clean, city_clean, state_clean, zip_clean, full_address_clean.

Details

Only address and city are required. When id is omitted, a surrogate record_id is generated from the row position. When zip is omitted (or empty), zip_clean is NA and full_address_clean is built without a trailing ZIP, so an address + city + state row is still geocodable. Supplying a ZIP improves Census structured-match precision but is no longer mandatory.

Examples

df <- tibble::tibble(
  LocationID = "NJ306100", Name = "Hackensack-UMC Mountainside",
  Address = "ONE BAY AVE", City = "Montclair", Zip = "7042"
)
clean_addresses(df, id = LocationID, address = Address,
                       city = City, zip = Zip, name = Name)
#> # A tibble: 1 × 15
#>   LocationID Name     Address City  Zip   record_id address_raw city_raw zip_raw
#>   <chr>      <chr>    <chr>   <chr> <chr> <chr>     <chr>       <chr>    <chr>  
#> 1 NJ306100   Hackens… ONE BA… Mont… 7042  NJ306100  ONE BAY AVE Montcla… 7042   
#> # ℹ 6 more variables: state_clean <chr>, address_clean <chr>, city_clean <chr>,
#> #   zip_clean <chr>, full_address_clean <chr>, record_name <chr>

# address + city only (surrogate id, no ZIP)
clean_addresses(tibble::tibble(Address = "100 Main St", City = "Trenton"),
                address = Address, city = City)
#> # A tibble: 1 × 12
#>   Address City  record_id address_raw city_raw zip_raw state_clean address_clean
#>   <chr>   <chr> <chr>     <chr>       <chr>    <chr>   <chr>       <chr>        
#> 1 100 Ma… Tren… 1         100 Main St Trenton  NA      NJ          100 MAIN STR…
#> # ℹ 4 more variables: city_clean <chr>, zip_clean <chr>,
#> #   full_address_clean <chr>, record_name <chr>