parallel.splchk_addr: Spell check a large data frame of NYC addresses.

In gmculp/rNYCclean: A package to clean NYC addresses

Description

The parallel.splchk_addr function is a more efficient way to perform a spell check on a large data frame of NYC addresses (+10,000 records) with a street name dictionary built from NYC Department of City Planning's (DCP) PAD (Property Address Directory) and SND (Street Name Dictionary) using parallel processing.

Usage

parallel.splchk_addr(in_clus, in_df, new_addr_col_name, addr_col_name,
  third_col_name, third_col_type)

Arguments

in_clus	the number of clusters available to the function as integer. Required.
in_df	a data frame containing NYC addresses. Required.
new_addr_col_name	the name of output addresses column as string. Required.
addr_col_name	the name of the input addresses column as string. Required.
third_col_name	the name of either the borough code or zip code column as string. Required.
third_col_type	either "boro_code" or "zip_code" as string. Required.

Value

A data frame containing the input data frame plus the spell checked address column.

Examples

# create a data frame of addresses
ADDR <- c(paste(1:5000,"BRODWAY"),paste(1:2400,"1 AVNUE"),
    paste(1:3400,"ATLANTAC AVE"),paste(1:3400,"FULTAIN ST"))
BORO_CODE <- ifelse(grepl("ATLANT|FULT",ADDR),3,1)
u_id <- 1:length(ADDR)
df = data.frame(u_id, ADDR, BORO_CODE)

#get version of DCP PAD used to build package data
rNYCclean::pad_version

#get number of records
nrow(df)

#spell check address column using borough code
df1 <- parallel.splchk_addr(in_clus = 10, in_df = df,   
    new_addr_col_name="ADDR.splchk", addr_col_name="ADDR", 
    third_col_name="BORO_CODE", third_col_type="boro_code")

#preview records
head(df1)

gmculp/rNYCclean documentation built on July 14, 2020, 5:07 a.m.