Description Usage Arguments Value Examples
View source: R/parallel.splchk_addr.R
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.
1 2 | parallel.splchk_addr(in_clus, in_df, new_addr_col_name, addr_col_name,
third_col_name, third_col_type)
|
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 |
A data frame containing the input data frame plus the spell checked address column.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 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)
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