Written by A name parser has been developed to assist with processing large administrative datasets that often have human names reported in a single unstructured text field.
The peopleparser package for R converts the raw text version of a name into a structured database. The program probabilistically determines name order based upon frequencies of the name appearing on US birth certificates.
Gender coding is similarly done probabilistically using the first name from the parsed string and the same Census data table.
The raw text version of the name is returned as a data table with the name parsed into the component parts:
- SALUTATION
- FIRST_NAME
- MIDDLE_NAME
- LAST_NAME
- SUFFIX
- GENDER
# the parse.name() function returns a string
x <- 'livingston III, Mr. MICHAEL JOHN9'
parse.name(x)
# "MR | MICHAEL | JOHN | LIVINGSTON |III | M | 99.6"
x <- "THOMAS H VON KAMECKE"
parse.name(x)
# [1] " | THOMAS | H | VON-KAMECKE | | M | 99.8"
# the parse.names() function (plural) returns a data frame
x <- get_example_names( n=10 )
parse.names(x)
name salutation first_name middle_name last_name suffix gender gender_confidence
1 Karen H Green KAREN H GREEN F 100
2 ED MARKS ED MARKS M 100
3 MATTHEW BERSHADKER MATTHEW BERSHADKER M 99.7
4 LINDA L SCHOELKOPF LINDA L SCHOELKOPF F 100
5 JAMES ROWAN JAMES ROWAN M 99.7
Use With Large Datasets
The parse.names() function utilizes parallelization to speed up large jobs.
To estimate time needed for a large number of names you can benchmark performance by examining runtimes:
x <- get_example_names( n=1000 )
start_time <- Sys.time()
pn <- parse.names( x )
end_time <- Sys.time()
end_time - start_time
# Time difference of 8.7648 secs
Installation
The R package can be installed as follows:
devtools::install_github( "Nonprofit-Open-Data-Collective/peopleparser" )
library( peopleparser )