Last updated: 2021-01-13

Checks: 7 0

Knit directory: fa_sim_cal/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20201104)

The command set.seed(20201104) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: d3deb84

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version d3deb84. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .tresorit/
    Ignored:    data/VR_20051125.txt.xz
    Ignored:    output/ent_raw.fst
    Ignored:    renv/library/
    Ignored:    renv/staging/

Untracked files:
    Untracked:  analysis/01_get_check_data.Rmd.txt
    Untracked:  analysis/standardise.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/01-5_check_name.Rmd) and HTML (docs/01-5_check_name.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	d3deb84	Ross Gayler	2021-01-13	Add 01-5 check name

# Set up the project environment, because each Rmd file knits in a new R session
# so doesn't get the project setup from .Rprofile

# Project setup
library(here)
source(here::here("code", "setup_project.R"))

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.0.4     ✓ dplyr   1.0.2
✓ tidyr   1.1.2     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.0

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

# Extra set up for the 01*.Rmd notebooks
source(here::here("code", "setup_01.R"))


Attaching package: 'glue'

The following object is masked from 'package:dplyr':

    collapse

# Extra set up for this notebook
# ???

# start the execution time clock
tictoc::tic("Computation time (excl. render)")

1 Introduction

The 01*.Rmd notebooks read the data, filter it to the subset to be used for modelling, characterise it to understand it, check for possible gotchas, clean it, and save it for the analyses proper.

This notebook (01-5_check_name) characterises the name variables in the saved subset of the data.

These variables will be used to construct the main predictors in the compatibility models.

We intend to use the one snapshot file as both the database to be queried and as the set of queries. Consequently, strictly speaking, we don’t need to standardise the name variables because the database and query records are guaranteed to be identical (they will literally be the same record). However, we will look at the name variables with an eye to standardisation because it is never a good idea to statistically model data without having an idea about the quality of the data. We will apply some basic standardisation to the name variables, if appropriate, because it parallels what would be necessary in practice.

Define the name variables.

vars_name <- c(
  "last_name", "first_name", "midl_name", "name_sufx_cd" 
)

Read the usable data. Remember that this consists of only the ACTIVE & VERIFIED records.

# Show the entity data file location
# This is set in code/file_paths.R
f_entity_fst

[1] "/home/ross/RG/projects/academic/entity_resolution/fa_sim_cal_TOP/fa_sim_cal/output/ent_raw.fst"

# get data for next section of analyses
d <- fst::read_fst(
  f_entity_fst, 
  columns = c(vars_name, "sex") # get sex as well for cross-checking
) %>% 
  tibble::as_tibble()
dim(d)

[1] 4099699       5

Take a quick look at the distributions.

d %>% skimr::skim()

Table 1.1: Data summary
Name	Piped data
Number of rows	4099699
Number of columns	5
_______________________
Column type frequency:
character	5
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	n_unique
last_name	0	1.00	1	21	191996
first_name	23	1.00	1	19	126589
midl_name	252695	0.94	1	20	175742
name_sufx_cd	3869063	0.06	1	3	101
sex	0	1.00	3	6	3

last_name 100% filled
first_name ~100% filled (23 missing)
midl_name 94% filled
name_sufx_cd 6% filled

2 Name length

Look at the distributions of name lengths first, before moving on to analyses more focused on standardisation.

Calculate the lengths of the name variables.

x <- d %>% 
  dplyr::mutate(
    len_last = stringr::str_length(last_name),
    len_first = stringr::str_length(first_name),
    len_midl = stringr::str_length(midl_name)
  )

2.1 last_name

last_name Voter last name

Look at the distributions of name lengths.

summary(x$len_last)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   5.000   6.000   6.345   7.000  21.000

table(x$len_last, useNA = "ifany")


      1       2       3       4       5       6       7       8       9      10 
     18    2046   53580  393363  864542 1094952  805773  514347  212379   96777 
     11      12      13      14      15      16      17      18      19      20 
  33039   12034    6844    4239    2679    1632     824     404     152      73 
     21 
      2

x %>% 
  ggplot() +
  geom_histogram(aes(x = len_last), binwidth = 1) +
  scale_y_sqrt()

Look at examples of short names.

# length == 1
x %>% 
  dplyr::filter(len_last == 1) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
A	CHUH	NA
A	THEK	NA
H	MOIH	NA
J	J	NA
K	HOA	HIEP
K	NGEO	NA
K	NIUH	NA
K	RICHARD	V
K	SANG	NA
M	COY	FAY
N	RENEE	VIVIAN
R	ANDREW	PERNELL
R	MARY	NA
S	PETER	THOMAS
U	RAYMOND	NA
X	MARCUS	NA
X	WILLIE	LARRY
Y	PRUM	NA

1-letter last names are very rare
1-letter last names are probably errors

# length == 2
x %>% 
  dplyr::filter(len_last == 2) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
AR	ORAWAN	P
DO	HANH THUAN	T
EA	YOUNG	HOW
EO	YONG	SUK
HA	YONG	S
KO	LINDA	KYONGSUK
LE	ANDREW	CHAU
LE	DANH	MINH
LE	DU	D
LE	NANCY	NICHOLS
LE	QUANG	TRAN
LU	IAN	MICHAEL
MA	ARNOLD	M
MA	JAMES	SUNG KAO
NG	AMY	L0CKAMY
VO	KHANH	HUU
WU	KUY	M
YI	JI SUK	NA
YU	JUN	HYUK
YU	XIAO	LI

Most 2-letter last names are probably valid.
ST is probably Saint from a multi-word last name

Look at examples of long names.

# length == 21
x %>% 
  dplyr::filter(len_last == 21) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
ALESSANDRETTI-STRAUSS	MARIA	E
BREWINGTON-SUTHERLAND	LISA	A

21-letter last names are hyphenated

# length >= 20
x %>% 
  dplyr::filter(len_last >= 20) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
ANASTASIOU-JOSEPHIDE	THEODORA	A
ARDESHIRPOUR-ZARTOSH	PARVIZ	NA
ARRIAGADA-VALENZUELA	GONZALO	ESTEBAN
BEDINGFIELD-DEMATTEO	HOLLIS	BEDINGFIELD
BEN MESSAOUDMESSAOUD	AHMED	BEN
FERRIOLA-BRUCKENSTEI	ZACHARY	NA
FRANKFORT-WINNINGHAM	SUSAN	R
GERKHARDT-GODZIEMSKI	ALICE	ELIZABETH
HUDSON-CHARLES-PIERR	MONIQUE	NA
KACZMAREK-HUFFSTETLE	KIM	NA
KLOCZKOWSKI-BERTRAND	DAWN	M
MCCUTCHEON-GUTKNECHT	LISA	ANN
MORRISON-WESTMORELAN	DAWN	IRVING
NOOHLANHLA GUGULETHE	ALAMILLA	NA
SCHIAPPACASSE-DEPUTY	STEPHANIE	E
SOTELO DE LOS SANTOS	MARCOS	ANTONIO
THEODORDES-GRINESTAF	APRIL	ARLETHA
THEODORIDES-GRINESTA	APRIL	ARLETHA
VALL-SPINOSA PERKINS	JESSIE	FAYE
WASHINGTON-HALFKENNY	DAVID	D

20+-letter last names appear to be multi-word and/or hyphenated

2.2 first_name

first_name Voter first name

Look at the distributions of name lengths.

summary(x$len_first)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   5.000   6.000   5.913   7.000  19.000      23

table(x$len_first, useNA = "ifany")


      1       2       3       4       5       6       7       8       9      10 
   8070    3799   99236  525505 1077727 1018768  884199  295743  135014   19359 
     11      12      13      14      15      16      17      18      19    <NA> 
  29314    1487     880     345     215       9       4       1       1      23

x %>% 
  ggplot() +
  geom_histogram(aes(x = len_first), binwidth = 1) +
  scale_y_sqrt()

Warning: Removed 23 rows containing non-finite values (stat_bin).

Look at the missing names.

x %>% 
  dplyr::filter(is.na(first_name)) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
ALEXANDER	NA	JASON
AMEN	NA	NA
BULLARD	NA	ALEXIS
BURGESS	NA	NA
CHESTER	NA	JAMES
ELSASS	NA	NA
FRISBY	NA	M
FRYE WILLIAM C	NA	NA
FUQUA	NA	MARY
FUQUA	NA	WILLIAM
GRAYWOLF	NA	NA
JUDITH	NA	NA
KAUCHICK	NA	PAULINE
MAGENTA	NA	NA
MALIK	NA	NA
MCKEEL	NA	LESTER
MOLET	NA	MICHAEL
MORRIS	NA	ALEXANDER
PATTERSON	NA	JOHN DEXTER
PHOENIX	NA	NA
SILVERMOON	NA	NA
WARREN	NA	NA
ZIMMER	NA	CLIFFORD

Some missing first names look like the middle name is actually the first name, e.g. ? JASON ALEXANDER
Some missing first names appear to have only a last name, e.g. ? ? AMEN
Some missing first names appear to have the entire name in the last name variable, e.g. ? ? FRYE WILLIAM C

Look at examples of short names.

# length == 1
x %>% 
  dplyr::filter(len_first == 1) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
ANDREWS	A	E
BARNETTE	C	V
BENFIELD	J	D
BOONE	A	C
BOSTWICK	H	KATHLEEN
CAMPBELL	W	THOMAS
HERMAN	L	E
HOOKER	S	A
MCDONALL	W	B MRS
MILLER	J	H
MILTON	E	D
OVERMAN	R	DALE
REIDENBACH	W	SCOTT
SMITH	J	C
STRAUB	C	WINIFRED
TOWNSEND	J	B
TUTTLE	M	GERTRUDE
WIGGINS	J	BELTON
WILLIAMS	A	BLANDENA
WILLIFORD	W	T

The 1-letter first names appear to be using an initial as the first name

# length == 2
x %>% 
  dplyr::filter(len_first == 2) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
BOONE	JO	GRAHAM
BROOKS	SU	TONYA CARYETTE
CALVILLO	AJ	NA
CLARK	JO	W
FAIR	JD	FAIR
FAULHABER	JO	ANN
FOWLER	LA	SONDA
HARVEY	TY	NA
HOFF	MI	YONG
JUDD	JO	D
MCKEE	JO	SHUMATE
MILLER	AL	NA
MIMS	JO	CHANDLER
MULLEN	JO	L
NGUYEN	HO	NGOC
NICHOLSON	DE	MELVIN
TANG	YU	NA
THOMASON	JO	CARPENTER
WHICHARD	AL	NA
WON	UN	T

2-letter first names appear to be:

Valid, e.g. JO W CLARK, HO NGOC NGUYEN
Part of a multi word name that has bee split across the first and middle name variables, e.g. LA SONDA FOWLER

Look at the long names.

# length >= 16
x %>% 
  dplyr::filter(len_first >= 16) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
ANDERSON	MICHAEL-CHEROKEE	DEMCK
DOUPE	KIMBERLY DANIELLE	WYATT
ENRIQUEZ	MARIA DEL CARMEN	NA
FIELDS	ADRIENNE`FELICIA	NA
LAPPAS-KOTARA	MICHELLE-ADRIENNE	NA
MIDDLESWORTH	ELIZABETH-LINDSAY	MCCOY
NAGARAJ	SANTHEBACHAHALLI	S
NATARAJA	HEGGADADEVANAKOTE	NA
NGUYEN	THI PHUONG KHAUH	NA
NUNEZ	MARIANA DE JESUS	N
ODEMS	MICHAEL-CHRISTOPHER	NA
PERRY	SHIRLEY ANN-PEPPER	NA
RODRIGUEZ	MARIA DEL CARMAN	NA
SUBRAMANIAM	LAKSHMINARAYANAN	NA
WINKLER	ELIZABETH PORTIS	G

Long first names appear to be:

Long non-anglo names, e.g. LAKSHMINARAYANAN
Multi-word and/or hyphenated, e.g. ELIZABETH-LINDSAY

2.3 midl_name

midl_name Voter middle name

These names will often be missing or initials only.

Look at the distributions of name lengths.

summary(x$len_midl)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1.00    3.00    5.00    4.73    6.00   20.00  252695

table(x$len_midl, useNA = "ifany")


     1      2      3      4      5      6      7      8      9     10     11 
826716  10491 289439 440549 651587 705383 508158 227267 114306  30604  20536 
    12     13     14     15     16     17     18     19     20   <NA> 
  9807   5186   3514   3379     50     21      8      2      1 252695

x %>% 
  ggplot() +
  geom_histogram(aes(x = len_midl), binwidth = 1) +
  scale_y_sqrt()

Warning: Removed 252695 rows containing non-finite values (stat_bin).

Many records are missing middle name
Spike of 1-letter names will be initials

Look at the long names.

# lentgh >= 16
x %>% 
  dplyr::filter(len_midl >= 16) %>% 
  dplyr::select(ends_with("_name")) %>% 
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name
ARTIST	SYLVIA	JOYCE WIILIAMSON
BILLOW	LESLEY	ELIZABETH CLAUSS
BOWDEN	CORA	FRANCES THOMPSON
BRINKHUIS	VANESSA	INGRID-PRISCILLA
CALL	LUNIA	ANNTONIA MCCRARY
DELLA	MEA	CAROLYN ROBINSON
EXUM	SHEILA	LANENA WHITEHEAD
GANAWAY	SUSAN	ANN WINTERHALTER
GULLEY	JOHN	MARCUS DELAFAYETTE
HARRIS	ANN	PULLER- MARCELINE-ZO
HARTSFIELD	NAOMI	RUTH SATTERWHITE
HICKS	NELLIE	BEATRICE-RICHARDSON
HOGGARD	ANN	DENISE HARRINGTON
MOORE	VIDA	GWENEVERE BARNER
RIVERA	RAFAEL	ANTONIO CARAMBOT
ROGERS	RUBYE	REBECCA/SUDDRETH
SWINSON	MARY	ELIZABETH FRANCIS
WHITENER	STEPHANIE	LYNNE WARREN PARKER
WOOD	T	BENBURY HAUGHTON
YOUNGER	ZEE	CAMILLE PREVETTE

Long middle names appear to be multiple names and/or hyphenated

# clean up
rm(x)
gc()

           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  1805215  96.5    2918587 155.9   2918587 155.9
Vcells 29961780 228.6   89587521 683.5 105198465 802.7

3 name_sufx_cd

name_sufx_cd Voter name suffix

This is intended for generation markers, e.g. Junior, Senior.

I am not going to use name suffix in entity resolution because age should be sufficient and is much better quality. I will look at what values turn up in the name suffix because the same values sometimes wrongly occur in the main name variables. Knowing what values occur may help us to remove those values from the main name variables.

d %>% dplyr::select(name_sufx_cd) %>% skimr::skim()

Table 3.1: Data summary
Name	Piped data
Number of rows	4099699
Number of columns	1
_______________________
Column type frequency:
character	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
name_sufx_cd	3869063	0.06	1	3	0	101	0

table(d$name_sufx_cd, useNA = "ifany") %>% sort() %>% rev()


   <NA>      JR     III      SR      II      IV     JR.     SR.       I       V 
3869063  153804   29605   27494   14043    3682    1060     226     218     190 
    111     MRS      11      VI       `     VII     MR.     MS.       J       E 
     67      50      28      27      13       9       7       5       5       4 
     MR       C       W     SCO       S     REV       R       N       M      JD 
      3       3       2       2       2       2       2       2       2       2 
    DR.       D     ANN       0     (JR       X     WAL     VIR     TOB     Sr. 
      2       2       2       2       2       1       1       1       1       1 
    SMI     SAM     REE     RAY       Q     PLA       P      ON      OD       O 
      1       1       1       1       1       1       1       1       1       1 
     MS     MOO     MMO      MD     MCQ     MAC     LOC     LLL      LL     LEW 
      1       1       1       1       1       1       1       1       1       1 
    LEE     LAR       L     KIT     KEN       K     JR,     JAC     ING     ILI 
      1       1       1       1       1       1       1       1       1       1 
    II.       H     GUY     GLE       G     FOR     FAU     F M      EY     EWA 
      1       1       1       1       1       1       1       1       1       1 
    ELS     DOR      DO     DIC     CUB     CHA       B     ALB     AJR       A 
      1       1       1       1       1       1       1       1       1       1 
    8TH       5     3RD      39     346       2      1V      15     134     070 
      1       1       1       1       1       1       1       1       1       1 
     \\     (II 
      1       1

# get a better look at the cleaned suffixes
d %>% 
  dplyr::mutate(
    sufx = name_sufx_cd %>% 
      stringr::str_to_upper() %>% 
      stringr::str_remove_all(pattern = "[^A-Z0-9]") %>% # remove non-alphanumeric
      dplyr::na_if("") 
  ) %>% 
  dplyr::count(sufx) %>% 
  dplyr::filter(n > 1) %>% 
  dplyr::arrange(desc(n), sufx) %>% 
  knitr::kable()

sufx	n
NA	3869077
JR	154867
III	29605
SR	27721
II	14045
IV	3682
I	218
V	190
111	67
MRS	50
11	28
VI	27
MR	10
VII	9
MS	6
J	5
E	4
C	3
0	2
ANN	2
D	2
DR	2
JD	2
M	2
N	2
R	2
REV	2
S	2
SCO	2
W	2

There are generation suffixes: JR, SR, I, II (11), III (111), IV, V, VI, VII
There are honorific titles: MRS, MR, MS, DR, REV

4 Standardisation

Look at issues that might be addressed by standardisation.

For each type of standardisation issue look at first middle and last names separately, because the issue may manifest differently in each of the name variables.

4.1 Lower-case letters.

d %>% dplyr::select(last_name) %>%
  dplyr::filter(stringr::str_detect(last_name, "[a-z]"))

# A tibble: 3 x 1
  last_name       
  <chr>           
1 MacQUEEN        
2 MacQUEEN        
3 BROWN-McCULLOUGH

d %>% dplyr::select(first_name) %>%
  dplyr::filter(stringr::str_detect(first_name, "[a-z]"))

# A tibble: 11 x 1
   first_name
   <chr>     
 1 JoANN     
 2 LaVERNE   
 3 JoANNE    
 4 JoANN     
 5 SiROBERT  
 6 McCKINES  
 7 DeNEAL    
 8 McHILDIA  
 9 JoANN     
10 LaSONYA   
11 JeROME

d %>% dplyr::select(midl_name) %>%
  dplyr::filter(stringr::str_detect(midl_name, "[a-z]"))

# A tibble: 76 x 1
   midl_name  
   <chr>      
 1 McBRIDE    
 2 McBRIDE    
 3 McKINNIE   
 4 McLAWHORN  
 5 McKEITHAN  
 6 McCULLEN   
 7 MacFRANKLIN
 8 McQUEEN    
 9 McPHAIL    
10 McCULLEN   
# … with 66 more rows

Lower case letters occur in last, first, and middle names
Associated with particles where there would optionally be a space, e.g. JoANN, McBride

4.2 Non-alphanumeric

Check for non-alphanumeric characters in names.

4.2.1 Hyphen

Check for hyphens.

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, "-"))

nrow(x)

[1] 20543

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
BAZAN-MANSON	ANDREA	NA	NA	FEMALE
BENITEZ-GRAHAM	ANA	NA	NA	FEMALE
BERRY-DANIEL	VAUGHN	NA	NA	FEMALE
BIBB-FREEMAN	TIFFANY	OCTAVIA	NA	FEMALE
COALE-KRUPA	MARY	KITTY	NA	FEMALE
EVERETT-GIGLIO	SUZANNE	MARIE	NA	FEMALE
HARRISON-LAMPTEY	JAMES	CHARLES	NA	MALE
JEAN-PIERRE	HERBERT	NA	NA	MALE
JENKINS-JAMES	TREVA	NA	NA	FEMALE
JOHNSON-DILLENBECK	LINDA	JEAN	NA	FEMALE
JOHNSON-FORBES	SHAQUILLA	NICOLE JOHNSON	NA	FEMALE
MACK-PURNELL	JOYCE	A	NA	FEMALE
MANNING-SHAUB	CHERYL	NA	NA	FEMALE
POLLARD-GREIF	RHIANNON	ELIZABETH	NA	FEMALE
RADFORD-BLACK	ANITA	NA	NA	FEMALE
RICHMOND-GRAVES	VANESSA	D	NA	FEMALE
SHERRELL-PATTERSON	KRYSTAL	ANN	NA	FEMALE
STONE-TANENBERG	KAREN	ANNE	NA	FEMALE
TILLEY-VARELA	MYRA	AMANDA	NA	FEMALE
WIMBISH-VANDERBECK	LAURA	NA	NA	FEMALE

~21k last names with hyphens
Look like legitimately hyphenated last names

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, "-"))

nrow(x)

[1] 3011

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(first_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
ROSAVAGE	ANN-MARIE	NA	NA	FEMALE
CRENSHAW	CALLIE-ANNE	DOANE	NA	FEMALE
GLENN	CHIH-TZU	L	NA	FEMALE
WOODARD	ESTHER-JOAN	SURRETT	NA	FEMALE
ROUSSEAUX	JEAN-CLAUDE	CHRISTIAN	NA	MALE
BERARD	JEAN-PAUL	NA	NA	MALE
ARTIS	JO-ANN	NA	NA	FEMALE
FURLONG	JO-ANN	ALICE	NA	FEMALE
DALE	JON-MARC	RYAN	NA	MALE
COOK	KAWIKA-JAMAL	SAMUEL	NA	MALE
MILLER	LEE-JAMIL	K	NA	MALE
BEAVER	RUTH-ANNE	GUST	NA	FEMALE
YEUNG	SHIN-YIING	NA	NA	FEMALE
JAN	SHYI-TAI	NA	NA	MALE
CHU	TE-HSIN	A	NA	FEMALE
SU	TSUNG-HU	NA	NA	FEMALE
MOJICA	WILLIAM-JOSEPH	KAILI	NA	MALE
TSAI	WON-WHEI	NA	NA	FEMALE
TAPP	YOUNG-SUK	O	NA	FEMALE
CHANG	YU-JHI(JULIE)	CHEN	NA	FEMALE

~3kL first names with hyphens
Look like legitimately hyphenated first names

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, "-"))

nrow(x)

[1] 3883

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
CABRIELE	DEBRA	ANN-MARIE	NA	FEMALE
CONRAD	HEATHER	CLEONA-JANE	NA	FEMALE
REEVES	ANDREW	DAVID-JOEL	NA	MALE
BROOKS	DARRICK	E-ALSTON	NA	MALE
EBRAHIM	EMAD	ELDIN-YASHAR	NA	MALE
WILSON	KAY	FRANCES-LAWS	NA	FEMALE
LANE	MICHELLE	GAYE-PRESTON	NA	FEMALE
YUAN	DEREK	HAW-LUEN	NA	MALE
PATE	BARBARA	JEAN-DALE	NA	FEMALE
CHAN	GODWIN	KWOK-YIN	NA	MALE
HUGHES	RACHEL	LYNN-INGRAM	NA	FEMALE
MORTON	QUIANA	MAISHA-ANN	NA	FEMALE
SAYE	ROBYN	MOO-YOUNG	NA	FEMALE
DIXON	STANLEY	RAY-HAMILTON	NA	MALE
PARKER	BRANDON	SHON-DAY	NA	MALE
CYRAN	JACLYN	SUZANNE-MARIE	NA	FEMALE
BOWMAN	HELEN	TAUSSIG-HAUPT	NA	FEMALE
BELL	ROSE	TIEH-CHIN	NA	FEMALE
COLDREN	RUTH	VIOLA-SHEATS	NA	FEMALE
KIM	LEGIA	YOUNG-SON	NA	FEMALE

~4k middle names with hyphens
Look like legitimately hyphenated middle names

4.2.2 Quote

Check for quotes.

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, "'"))

nrow(x)

[1] 4920

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
D’ARBEAU	STEPHEN	B	NA	MALE
I’ANSON-JACKSON	JENNIFER	NA	NA	FEMALE
O’BRIANT	MABEL	S	NA	FEMALE
O’BRIEN	MARK	S	NA	MALE
O’BRIEN	PATRICK	WAYNE	SR	MALE
O’BRIEN	WILLIAM	PATRICK	NA	MALE
O’CONNELL	TINA	DEE	NA	FEMALE
O’MALLEY-HELMS	COLLEEN	E	NA	FEMALE
O’MEARA	MORGAN	STUART	NA	MALE
O’NEAL	DORIS	TAYLOR	NA	FEMALE
O’NEAL	KELLY	NA	NA	FEMALE
O’NEAL	BETTY	MAGALENE D	NA	FEMALE
O’NEAL	TAKINA	L	NA	FEMALE
O’NEAL	LINDA	F	NA	FEMALE
O’NEAL	CHARLES	FRANKLIN	NA	MALE
O’NEIL	DONNA	LOUISE	NA	FEMALE
O’NEILL	LUCILLE	W	NA	FEMALE
O’QUINN	VICKIE	LEE	NA	FEMALE
O’ROURKE	JOHN	F	NA	MALE
O’ROURKE	JEFFERY	JAMES	NA	MALE

~5k last names with quotes
Look like legitimately quoted last names

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, "'"))

nrow(x)

[1] 1226

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(first_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
KNIGHT	A’NDREA	LANIER	NA	FEMALE
RICHARDSON	ANDRE’	STEVEN	NA	MALE
CORBETT	D’ANDREA	PERE	NA	FEMALE
PASOUR	D’ETTA	TAYLOR	NA	FEMALE
JONES	DEONTAE’	QUINN	NA	MALE
EDWARDS	DESIRE’	DENISE	NA	FEMALE
WILKINS	FAR’D	HAKEEM	NA	MALE
RICHARDSON	J’MAINE	NMN	NA	MALE
ALSTON	J’MIA	KAE	NA	FEMALE
DUNLAP	JA’TINA	R	NA	FEMALE
SUITT	L’TONYA	NA	NA	FEMALE
LITTLEJOHN	LA’KANYA	MICHELLE	NA	FEMALE
HALL	LA’KETTA	CHENTAL	NA	FEMALE
DOWELL	LA’TONYA	YVETTE	NA	FEMALE
FORD	O’DENA	NA	NA	FEMALE
JACKSON	O’NEIL	NA	NA	MALE
HILBURN	O’NEILL	NA	NA	MALE
LEBEAU	RENE’	DOMITIEN	NA	MALE
WEATHERBEE	SADE’	SHANNON	NA	FEMALE
MITCHELL	SHANTAE’	T	NA	FEMALE

~1k first names with quotes
Look like legitimately quoted first names

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, "'"))

nrow(x)

[1] 3152

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
GRILLO	LUIS	CHE’	NA	MALE
CRAWFORD	NICKOLAS	D’ANDRE	NA	MALE
LARSEN	HEATHER	D’ANN	NA	FEMALE
SINGHATEH	NICHELLE	DY’VONNE	NA	FEMALE
HARPER	DUNSEY	LA’TAZE	NA	MALE
BATTLE	IKEDA	LE’RECIA	NA	FEMALE
O’CONNELL	KAREN	O’BRIEN	NA	FEMALE
SPERRY	ANN	O’BRIEN	NA	FEMALE
GINYARD	DEEDRICK	O’BRIEN	NA	MALE
ARNEY	KATHLEEN	O’DWYER	NA	FEMALE
DOWNES	ANN	O’HARA	NA	FEMALE
VANHOOK	BRANDON	O’NEAL	NA	MALE
JONES	ROBIN	O’NEIL	NA	FEMALE
JOHNSON	DAESHAWAN	O’NEIL	NA	MALE
MANNS	RUSSELL	O’NEIL	JR	MALE
CALLOWAY	TOMIKA	RENEE’	NA	FEMALE
THOMAS	DENA	RENEE’	NA	FEMALE
CALLAHAN	PAMELA	RENEE’	NA	FEMALE
GRAHAM	QUINDERIA	SH’RON	NA	FEMALE
JAMES	BRITTANY	VONT’E	NA	FEMALE

~3k middle names with quotes
Look like legitimately quoted middle names

4.2.3 Period

Check for periods.

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, "\\."))

nrow(x)

[1] 11

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
BINGHAM JR.	AMES	EDMOND	NA	MALE
DAYE JR.	JAMES	NA	JR	MALE
RUSSELL, JR.	KERMITT	PATRICK	NA	MALE
ST. CLAIR	JACK	LEE	NA	MALE
ST. CYR	CANDICE	NICOLE	NA	FEMALE
ST. GEORGE	MARTHA	S	NA	FEMALE
ST. GERMAIN	AMY	NA	NA	FEMALE
ST. JOHN	JESSICA	JO	NA	FEMALE
ST. LAWRENCE	ELIZABETH	W	NA	FEMALE
ST.CLAIRE	KEVIN	WAYNE	NA	MALE
ST.JOHN	JOANN	DIMAGGIO	NA	FEMALE

11 last names with periods
Look like legitimate abbreviations

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, "\\."))

nrow(x)

[1] 120

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(first_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
NORRIS	A.T.	NA	NA	MALE
EVINS	BETTY L.	CURRIN	NA	FEMALE
BUIE	BEVERLY D.	COOKE	NA	FEMALE
DUNN	C. SHAY	HARRELSON	NA	FEMALE
SWOFFORD	D.	MILYNN	NA	FEMALE
ROSS	E.	TRAVIS	JR	MALE
INGOLFSSON	E. JUANITA	O’BRIEN	NA	FEMALE
LILLEY	G.	C.	NA	MALE
NAVARRE	J.	RICHARD	II	MALE
JARRETT	J.	REID	NA	MALE
AINSLEY	J. (JULIUS)	T.(THOMAS)	NA	MALE
RENDLEMAN	J.T.	NA	NA	MALE
GIBSON	M.	COLINE	NA	FEMALE
HICKS	MARY E.	PALMER	NA	FEMALE
UNDERWOOD	NORMA J.	PHILLIPS	NA	FEMALE
GARSKA	P.J.	JAN DE BEWR	NA	FEMALE
USSERY	PRISCILLA B.	SANDERS	NA	FEMALE
BOUCHER	T. RENEE	NA	NA	FEMALE
GABLE	THOMAS J.	WESTLEY	NA	MALE
MUSSON	W.	JAMES	NA	MALE

120 first names with periods
Look like initials

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, "\\."))

nrow(x)

[1] 2233

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
KYKER	JACOB	C.	NA	MALE
TOWNSEND	IRIS	D. MELENDEZ	NA	FEMALE
FLOYD	WALTER	E.	NA	MALE
WARMACK	WILLIAM	G.	NA	MALE
MASON	PEGGY	H.	NA	FEMALE
ENOCH	WILLIAM	H.	NA	MALE
MITCHELL	JOHN	J.	NA	MALE
HAMILTON	MILON	J.	NA	MALE
PARROTT	ULYSSES	J.	JR	MALE
NEWBLE	ANDRE	L.K.	NA	MALE
LENAHAN	LOIS	M.	NA	FEMALE
JONES	TANETTA	M.	NA	FEMALE
PHILLIPS	HELEN	M.	NA	FEMALE
MOORE	DOROTHY	M.	NA	FEMALE
EDWARDS	STANLEY	M.	NA	MALE
BROOKS	MARILYN	M.LEDFORD	NA	FEMALE
WAVERLY	TRACY	R.	NA	FEMALE
SCOTT	HENRY	R.	NA	MALE
GRAHAM	KENDRA	T.	NA	FEMALE
ESPERGREN	MARY	T.	NA	FEMALE

~2k middle names with periods
Look like initials

4.2.4 Comma

Check for commas.

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, ","))

nrow(x)

[1] 2

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
FILLINGHAM, II	ROBERT	E	NA	MALE
RUSSELL, JR.	KERMITT	PATRICK	NA	MALE

2 last names with commas
Punctuation for suffix field values added to last name

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, ","))

nrow(x)

[1] 4

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(first_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
PHILLIPS	FRANK,	NA	JR	MALE
HICKS	MARION,	NA	SR	MALE
CANIPE	NOAH,	NA	JR	MALE
MCADAMS	WILL,JR	NA	NA	MALE

4 first names with commas
Arbitrary added punctuation
Punctuation for suffix field value added to first name

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, ","))

nrow(x)

[1] 12

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
FAUCETTE	JESSE	EDWARD, J	NA	MALE
BRASWELL	ROBERT	ELLIS, J	NA	MALE
MARTIN	LLOYD	FRANKLIN, S	NA	MALE
GAY	ROBERT	HENRY, III.	NA	MALE
FERGUSON	STANTON	HYDE, J	NA	MALE
CLARK	COLEMAN	JACKSON, I	NA	MALE
BARNES	RUSSELL	JOSEPH, J	NA	MALE
PIERCE	RUTH	P,	NA	FEMALE
COVINGTON	EDNA(MRS	PERRY, JR)	NA	FEMALE
SCARBOROUGH	JOHN	R,	NA	MALE
SHEARIN	ANDREW	THOMAS, S	NA	MALE
WILLIAMS	ERVIN	W., SR.,	NA	MALE

12 middle names with periods
List separator
Punctuation to squeeze in extra field

4.2.5 Other non-alphanumeric

Check for other non-alphanumeric characters.

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, "[^ a-zA-Z0-9\\.,'-]"))

nrow(x)

[1] 31

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) # %>%

# A tibble: 20 x 5
   last_name           first_name midl_name name_sufx_cd sex   
   <chr>               <chr>      <chr>     <chr>        <chr> 
 1 "BOYLES`"           LINDA      BROWN     <NA>         FEMALE
 2 "BRYANT`"           WILLIAM    STEWART   <NA>         MALE  
 3 "COLLINS/SISK"      RHONDA     L         <NA>         FEMALE
 4 "D*AMICO"           PATRICIA   MARIE     <NA>         FEMALE
 5 "D*AMICO"           MEGAN      MARIE     <NA>         FEMALE
 6 "GALINSKY/MALAGUTI" DANA       ANNE      <NA>         FEMALE
 7 "GOSHEN\\"          DIXIE      M         <NA>         FEMALE
 8 "LA\"BEE"           DELACRUZ   <NA>      <NA>         FEMALE
 9 "MARTIN/HUFF"       ELLEN      MARIE     <NA>         FEMALE
10 "MORRISON`"         HAZEL      M         <NA>         FEMALE
11 "NICHOLS/BROWN"     MARY       SUE       <NA>         FEMALE
12 "O*BRIEN"           COLIN      JAMES     <NA>         MALE  
13 "O*TOOLE"           PETER      TERRENCE  <NA>         MALE  
14 "REAVIS/LONG"       SHAWN      MICHELLE  <NA>         FEMALE
15 "RHONEY/PETERS"     DONNA      <NA>      <NA>         FEMALE
16 "SCHERM%MARTIN"     WYATT      <NA>      <NA>         FEMALE
17 "STRTHEIT\\"        LOLA       C         <NA>         FEMALE
18 "TALBERT/GRAHAM"    BRENDA     <NA>      <NA>         FEMALE
19 "TUCKER/JACKSON"    LAVONDA    LYNN      <NA>         FEMALE
20 "WOODARD`"          JASON      WARREN    <NA>         MALE

  # knitr::kable() # some of the characters break the kable formatting

31 last names with other non-alphanumeric characters
Most look like substitutions for hyphen or quote
Some look like random cruft

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, "[^ a-zA-Z0-9\\.,'-]"))

nrow(x)

[1] 102

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(first_name, sex) # %>%

# A tibble: 20 x 5
   last_name first_name         midl_name name_sufx_cd sex   
   <chr>     <chr>              <chr>     <chr>        <chr> 
 1 POTEAT    "(KAY)"            ANNE CATH <NA>         FEMALE
 2 FIELDS    "ADRIENNE`FELICIA" <NA>      <NA>         FEMALE
 3 STEELE    "AR`KISHA"         FERNESE   <NA>         FEMALE
 4 JACKSON   "AR`MONIE"         <NA>      <NA>         FEMALE
 5 STUBBS    "BRITNE`"          ELIZABETH <NA>         FEMALE
 6 CLARK     "CANDERE`"         L         <NA>         FEMALE
 7 SELF      "CATHERINE`"       MARIE     <NA>         FEMALE
 8 FABIAN    "D`ARLINE"         D         <NA>         FEMALE
 9 INGRAM    "D`WON"            LAMONTE   <NA>         MALE  
10 NICHOLS   "DORIS ( MRS W"    <NA>      <NA>         FEMALE
11 STEWART   "JA`VONDA"         NICHOLE   <NA>         FEMALE
12 SPENCER   "JAMES (JIM)"      N         <NA>         MALE  
13 CARTER    "JOSE`"            PIERRE    <NA>         MALE  
14 HEMPHILL  "LA`CHERICA"       EVON      <NA>         FEMALE
15 CHESTNUT  "LA`WANDA"         F         <NA>         FEMALE
16 DUNN      "MARY (\"PETE\")"  BURNETTE  <NA>         FEMALE
17 JERNOVICS "MARY SUSAN/"      R         <NA>         FEMALE
18 KERN      "O  (BUDDY)"       R         <NA>         MALE  
19 FOSTER    "OTIS(NMN)"        JR        <NA>         MALE  
20 DAVIDOV   "ZVIYA`CRYSTAL"    <NA>      <NA>         FEMALE

  # knitr::kable() # some of the characters break the kable formatting

102 first names with other non-alphanumeric characters
Some look like substitutions for hyphen or quote
Some are parenthetical notes
Some look like random cruft

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, "[^ a-zA-Z0-9\\.,'-]"))

nrow(x)

[1] 1097

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
KEENE	JOSEPHINE	(MRS OTIS)	NA	FEMALE
PIERMARINI	JACLYN	(NMN)	NA	FEMALE
TORAIN	ROOSEVELT	(NMN)	JR	MALE
WILEY	ROOSEVELT	(NMN)	JR	MALE
HARVEY	NATHANIEL	(NMN)	NA	MALE
EULISS	MAX	(NMN)	NA	MALE
ASHE	WILLARD	(NMN)	NA	MALE
CAMERON	CHARLES	(NMN)	JR	MALE
CRUTCHFIELD	WAYNE	(NMN)	NA	MALE
ROBERTS	ROBIN	(NMN)	NA	MALE
COHEN	SETH	(NMN)	NA	MALE
DEMAS	DOLORIS	A/GEARHART	NA	MALE
MUSSELWHITE	ALLISON	ELAINE/HUMPH	NA	FEMALE
BASS	H	J (HUBERT)	NA	MALE
BRITT	ANGELA	KAY / ROGERS	NA	FEMALE
LOCKLEAR	MINNIE	LEE/JONES	NA	FEMALE
DREW	VIRGINIA	M/KELLEY	NA	FEMALE
BROOKS	WILLIAM	MACK (BILL)	NA	MALE
FRIEDRICH	DELLA	MAE /KEYS	NA	FEMALE
ALSTON	MEI	WAN/DAN	NA	FEMALE

~1k middle names with other non-alphanumeric characters
Some look like substitutions for hyphen
Many are parenthetical notes (NMN = no middle name)

4.3 Digits

Check for digits.

4.3.1 Zero

Check for zero

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, "0"))

nrow(x)

[1] 29

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
ALEM0N	NOE	A	NA	MALE
BOLAD0	PAULA	HUTCHENS	NA	FEMALE
CAPUT0	BARBARA	DAVIS	NA	FEMALE
CONR0Y	WILLIAM	COURTNEY	NA	MALE
D0WNS	MARIO	ENRICO	NA	MALE
EAT0N	VICKIE	TUGGLE	NA	FEMALE
ESC0BEDO	AUDREY	ANN	NA	FEMALE
FERNANDEZ-BRAV0	GIOVANNI	NA	NA	MALE
GUARDAD0	MANUEL	FELIX	NA	MALE
J0HNSON	LUCILLE	FRANCES	NA	FEMALE
JOHNS0N	MICHAEL	NA	NA	MALE
MCD0UGAL	BETTY	JEAN	NA	FEMALE
OCONN0R	GERALDINE	LOUISE	NA	FEMALE
PEREZ-NAVARR0	CAROLE	SHAY	NA	FEMALE
R0CCO	CHRISTOPHER	NA	NA	MALE
REYN0LDS	ADAM	DANIEL	NA	MALE
RUSS0	ANGEL	MARIE	NA	FEMALE
SIMPS0N	MARY	ANN	NA	FEMALE
WIT0SKY	MICHAEL	ADAM	NA	MALE
YATSK0	JEANETTE	MARIE	NA	FEMALE

29 last names with zero
Substitution for O

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, "0"))

nrow(x)

[1] 33

x %>%   
  dplyr::slice_sample(n = 20) %>%
  dplyr::arrange(first_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
PETTY	ALLIS0N	JEAN	NA	FEMALE
BROWN	C0LBY	TODD	NA	MALE
COOPER	C0RDELIA	P	NA	FEMALE
WHITTEMORE	D0LORES	H	NA	FEMALE
LOWE	D0NNA	G	NA	FEMALE
BRANDT	J0HN	C	NA	MALE
CRANFILL	J0HN	NA	NA	MALE
ADAMS	J0HN	WILLIAMS	NA	MALE
TANNAHILL	J0SEPH	ERIC	NA	MALE
WILLIAMS	M0NIKA	UDANA	NA	FEMALE
KEENAN	MARY-J0	NA	NA	FEMALE
SHEPHERD	OTH0	L	NA	MALE
THOMAS	P0LLY	BROWN	NA	FEMALE
RAMIREZ	REYNALD0	G	NA	MALE
BUIE	S0NTE	Y	NA	FEMALE
MITCHELL	SHANN0N	ARLINE	NA	FEMALE
JOHNSON	T0NYA	BETH	NA	FEMALE
MONK	T0NYA	SIVLEY	NA	FEMALE
RUFFIN	TIM0THY	ONEILL	NA	MALE
KENNEDY	V0NCIEAL	LEE	NA	FEMALE

33 first names with zero
Substitution for O

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, "0"))

nrow(x)

[1] 77

x %>%   
  dplyr::slice_sample(n = 20) %>%
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
PONGPAIROJ	AMANDA	0	NA	FEMALE
IVESTER	WILLIAM	0DELL	NA	MALE
MOORE	EVA	0MAE	NA	FEMALE
LANE	KATHLEEN	0VERTON	NA	FEMALE
SODAGAR	EASA	2205	NA	MALE
FRENCH	SHNETTA	ALEXANDER080572	NA	FEMALE
NEWSOME	MARK	ANTH0NY	NA	MALE
BRODIE	WILLIAM	C1010	NA	MALE
SMITH	BRODY	CO0PER	NA	MALE
OROPEZA	AMILCAR	COL0N	NA	MALE
LUCK	GENA	DON0HOO	NA	FEMALE
STOLLBRINK	KATHY	J0	NA	FEMALE
NAYLOR	ANGELA	LY0NS	NA	FEMALE
MCKOY	LILLY	M00RE	NA	FEMALE
JONES	RASHAWN	M0NIQUE	NA	FEMALE
MARSHALL	MONICA	NICH0LE	NA	FEMALE
DAVIS	LEANN	RUNY0N	NA	FEMALE
HOLTON	RANDY	SC0TT	NA	MALE
NATION	LAVONIA	V0SS	NA	FEMALE
HASH	MYRTLE	Y0UNG	NA	FEMALE

77 middle names with zero
Some are substitution for O
Some are in superfluous numbers

4.3.2 One

Check for one.

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, "1"))

nrow(x)

[1] 1

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
SATTERFIELD 111	CHARLES	MASON	NA	MALE

1 last name with one
Substitution for I in generation suffix (111 = III)

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, "1"))

nrow(x)

[1] 0

0 first names with one

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, "1"))

nrow(x)

[1] 39

x %>%   
  dplyr::slice_sample(n = 20) %>%
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
PATTERSON	CARL	11	NA	MALE
ADAMS	RALPH	11	NA	MALE
REED	CHARLES	11	NA	MALE
WILLIAMS	JOSEPH	11	NA	MALE
BEST	KENNETH	111	NA	MALE
LOPEZ	CARLOS	111	NA	MALE
QUERY	FRED	111	NA	MALE
FEATHERSTONE	GEORGE	111	NA	MALE
FREEZE	HOMER	111	NA	MALE
KLUTTZ	JOE	111	NA	MALE
MCGOVERN	WILLIAM	111	NA	MALE
WINECOFF	DAVID	111	NA	MALE
JOHNSON	ULUS	111	NA	MALE
COOKE	GEORGE	111	NA	MALE
HOWERIN	MICHAEL	DALE401	NA	MALE
HUNTER	MORDECAI	J1-TO	NA	MALE
FAICLOTH	TIMOTHY	LOUIS7100	NA	MALE
BREEN	TERRANCE	MICHAEL146	NA	MALE
PATTERSON	CARLA	NADINE DOUGLAS1	NA	FEMALE
PLESS	JOAN	WRIGHT2106	NA	FEMALE

39 middle names with one
Some are substitution for I in generation suffix
Some are in superfluous numbers

4.3.3 Other digits

Check for other digits.

x <- d %>% 
  dplyr::filter(stringr::str_detect(last_name, "[2-9]"))

nrow(x)

[1] 1

x %>%   
  dplyr::slice_sample(n = 20) %>% 
  dplyr::arrange(last_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
ALBER5TSON	BASIL	ERVIN	NA	MALE

1 last name with a 5
Substitution of 5 for S

x <- d %>% 
  dplyr::filter(stringr::str_detect(first_name, "[2-9]"))

nrow(x)

[1] 2

x %>%   
  dplyr::slice_sample(n = 20) %>%
  dplyr::arrange(first_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
SPIVEY	FR4ANK	THOMAS	SR	MALE
CHILTON	J8IMMIE	HERBERT	NA	MALE

2 first names with digits 2-9
Look like random insertions

x <- d %>% 
  dplyr::filter(stringr::str_detect(midl_name, "[2-9]"))

nrow(x)

[1] 24

x %>%   
  dplyr::slice_sample(n = 20) %>%
  dplyr::arrange(midl_name, sex) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex
SODAGAR	EASA	2205	NA	MALE
YOUNG	WANWYNE	4625	NA	FEMALE
CLARKE	MINERVA	4932	NA	FEMALE
PHAIR	IDELL	8017	NA	FEMALE
FRENCH	SHNETTA	ALEXANDER080572	NA	FEMALE
BEACHAM	HEATHER	ANDERSON9104576	NA	FEMALE
SHUMAKER	RUTH	ANN BURTON47	NA	FEMALE
KOERNER	JENNIFER	ANN155	NA	FEMALE
WARD	EVA	B2957	NA	FEMALE
HOWERIN	MICHAEL	DALE401	NA	MALE
GLOVER	DIONNE	LYNN1820	NA	FEMALE
GUIDO	DEANA	LYNN2513	NA	FEMALE
BECHTEL	TERESA	MARIE103062	NA	FEMALE
BREEN	TERRANCE	MICHAEL146	NA	MALE
HILL	ZEB	MITCHELL368	NA	MALE
BLAIR	ESSIE	MIZELLE25248249	NA	FEMALE
PERKINS	TERESA	ROSENBAUM3305	NA	FEMALE
TOOMES	BRIAN	SCOTT3450	NA	MALE
PYRTLE	PHILLIP	W5RAY	SR	MALE
PLESS	JOAN	WRIGHT2106	NA	FEMALE

24 middle names with digits 2-9
One random insertion
Most appear to be superfluous numbers (from the address?)

4.4 Special words

Look for special words that shouldn’t be in names.

Define word patterns to search for.

# honorifics
w_hons <- c(
  "MR", "MISTER", "MASTER", "MRS", "MS", "MISS", 
  "REV", "REVEREND", "SR", "SISTER", "BR", "BROTHER",
  "FATHER", "MOTHER", "PASTOR", "ELDER", "BISHOP",
  "DR", "DOCTOR", "MD", "PROF", "PROFESSOR"
)

# generation suffixes
w_gen <- c(
  "JR", "JNR", "JUNIOR", "SR", "SNR", "SENIOR",
  "1ST", "2ND", "3RD", "4TH", "5TH", "6TH", "7TH", "8TH",
  "FIRST", "SECOND", "THIRD", "FOURTH", "FIFTH", "SIXTH", "SEVENTH", "EIGHTH", "EIGHTTH",
  "1", "2", "3", "4", "5", "6", "7", "8",
  "I", "II", "III", "IIII", "IV", "V", "VI"
)

# special values
w_spec <- c(
  "NN", "NMN", "NAME",
  "UNK", "UNKNOWN", "AKA", "KNOWN AS", "ALSO KNOWN AS", "ALIAS",
  "BLIND"
)

# test
w_test <- c(
  "TEST", "TST", "DUMMY", "VOTER",  "([A-Z])\\1{2,}"
)

4.4.1 Last name

# regular expression to match words
w_regexp <- 
  c(w_hons, w_gen, w_spec, w_test) %>% # all special words
  unique() %>% # make it a set
  dplyr::setdiff( # remove words that appear to mostly be validly used
    c(
      "BISHOP",
      "BLIND",
      "BROTHER",
      "DOCTOR",
      "ELDER",
      "FIRST",
      "JUNIOR",
      "MASTER",
      "MISS",
      "MISTER",
      "PASTOR",
      "SENIOR",
      "TEST",
      "THIRD",
      "VOTER"
    )
  ) %>% 
  glue::glue(x = . , "\\b{x}\\b") %>%  # must be words
  glue::glue_collapse(sep = "|") # search for any

x <- d %>% 
  dplyr::mutate(
    match = 
      last_name %>% 
      stringr::str_to_upper() %>% 
      stringr::str_replace_all(pattern = "[^ A-Z]", replacement = " ") %>% 
      stringr::str_squish() %>% 
      stringr::str_extract(pattern = w_regexp)
  ) %>% 
  dplyr::filter(!is.na(match))

nrow(x)

[1] 124

x %>% 
  dplyr::arrange(match, sex, last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex	match
WILLIAMSON DR	IRVIN	D	NA	MALE	DR
I’ANSON-JACKSON	JENNIFER	NA	NA	FEMALE	I
BIREN II	WILLIAM	GEORGE	NA	MALE	II
CRITTENDON II	WILLIAM	BURRELL	NA	MALE	II
EVANS II	DONALD	M	NA	MALE	II
FILLINGHAM, II	ROBERT	E	NA	MALE	II
GOODWIN II	PAUL	J	NA	MALE	II
GREEN II	BILLY	HOWARD	NA	MALE	II
METTS II	CAREY	MONTGOMERY	NA	MALE	II
MILHORN II	JOSEPH	JAMES	NA	MALE	II
PERSON II	DARYEL	JAMES	NA	MALE	II
SEABOLD II	GERALD	W	NA	MALE	II
STANLEY II	WILLIAM	A	NA	MALE	II
TAYLOR II	ROBERT	D	NA	MALE	II
THOMBS II	DANIEL	EUGENE	NA	MALE	II
WATSON II	ROBERT	NATHANIEL	NA	MALE	II
WORD II	JOE	NAHAN	NA	MALE	II
AUSLEY III	PRESTON	ALEXANDER	NA	MALE	III
BEATTY III	CURTIS	M	NA	MALE	III
BLACKWELDER III	DWIGHT	MCNAIRY	NA	MALE	III
BOONE III	JAMES	HENRY	NA	MALE	III
BOSQUEZ III	RICHARD	NA	NA	MALE	III
CHAPPELL III	TRAVIS	NA	NA	MALE	III
COCKERHAM III	BOBBY	LEE	NA	MALE	III
CONNELL III	THOMAS	JOSEPH	NA	MALE	III
FAULKNER III	HOWARD	VERNON	NA	MALE	III
GOODWIN III	WARD	ALEXANDER	NA	MALE	III
GROUSE III	CHARLES	J	NA	MALE	III
HARRIS III	WILLIAM	T	NA	MALE	III
KNOX III	JOHN	J	NA	MALE	III
LANE III	WILLIAM	JAMES	NA	MALE	III
MCGUIRT III	JAMES	WILLIAM	NA	MALE	III
MILLER III	JOHNNIE	H	NA	MALE	III
MOORE III	JAMES	P	NA	MALE	III
NEWSOME III	THOMAS	LESLIE	NA	MALE	III
PEACOCK III	EDWARD	JACKSON	NA	MALE	III
PETERS III	MARION	HOWELL	NA	MALE	III
PRUDEN III	THOMAS	EUGENE	NA	MALE	III
REDFEARN III	WILBERT	NA	NA	MALE	III
SMITH III	GUY	R	NA	MALE	III
THOMPSON III	EMERY	NA	NA	MALE	III
BAKER IIII	WILLAIM	RAINEY	NA	MALE	IIII
BUXTON IV	SAMUEL	R	NA	MALE	IV
LONG IV	FLOYD	M	NA	MALE	IV
THOMPSON IV	HARRY	M	NA	MALE	IV
ANSELMENT JR	JOSEPH	LEONARD	NA	MALE	JR
BALL JR	SAMUEL	LEE	NA	MALE	JR
BARKLEY JR	CHARLES	W	NA	MALE	JR
BENDER JR	JOHN	JOHN P	NA	MALE	JR
BINGHAM JR.	AMES	EDMOND	NA	MALE	JR
BIRCHFIELD JR	MILBURN	JOEL	NA	MALE	JR
BLEDSOE JR	HOMER	BLAINE	NA	MALE	JR
BROWN JR	ROBERT	A	NA	MALE	JR
BUNDESMAN JR	BERNARD	B	NA	MALE	JR
BYRD JR	HERBERT	L	NA	MALE	JR
CAIL JR	MALCOLM	LEHOLMES	NA	MALE	JR
CARRIER JR	ROBERT	WILSON	NA	MALE	JR
CHAMBERS JR	KENNETH	RAY	NA	MALE	JR
CHARLES JR	WILLIE	J	NA	MALE	JR
CLAY JR	WILEY	WALTON	JR	MALE	JR
CLAYTON JR	JAMES	D	NA	MALE	JR
CULBRETH JR	WALTER	E	NA	MALE	JR
DAYE JR.	JAMES	NA	JR	MALE	JR
ENGLISH JR	WARREN	ROBERT	NA	MALE	JR
EVANS JR	RALPH	NA	II	MALE	JR
FAILLE JR	EDWARD	J	NA	MALE	JR
FARMER JR	BENJAMIN	STEVE	NA	MALE	JR
FRAZIER JR	JAMES	A	NA	MALE	JR
GARCIA JR	FRANK	NA	NA	MALE	JR
HALL JR	JAMES	B	NA	MALE	JR
HARDIN JR	CHARLES	ELMORE	NA	MALE	JR
HARGRAVES JR	JAMES	CALVIN	NA	MALE	JR
HARRIS JR	CHAMP	NA	NA	MALE	JR
HAWKINS JR	REED	GREGORY	NA	MALE	JR
HENSLEY JR	LAWRENCE	G	NA	MALE	JR
HERNDON JR	EVERETT	GEORGE	NA	MALE	JR
HILL JR	JAMES	C	NA	MALE	JR
HOYLE JR	GEORGE	A	NA	MALE	JR
HUMPHRIES JR	DONNIE	R	NA	MALE	JR
KENNEDY JR	THOMAS	E	NA	MALE	JR
KUBU JR	JERRY	JOHN	NA	MALE	JR
LANE JR	DAVID	C	NA	MALE	JR
LAWRENCE JR	HARRY	NA	NA	MALE	JR
MARBLE JR	ROBERT	STERLING	NA	MALE	JR
MCCLURE JR	DONALD	R	NA	MALE	JR
MCGUIRE JR	JOHN	M	NA	MALE	JR
MONGIOVI JR	ANTHONY	B	NA	MALE	JR
MOORE JR	HARRY	GRADY	NA	MALE	JR
MORRISON JR	WILLIAM	EMERSON	NA	MALE	JR
MOSES JR	MICHAEL	WILLIAM	NA	MALE	JR
NASIFE JR	SAMUEL	NICHOLAS	NA	MALE	JR
OUTLAND JR	HOWARD	BROWN	NA	MALE	JR
OVERTON JR	ROBERT	ALLEN	NA	MALE	JR
PARKS JR	JOEL	TIMOTHY	NA	MALE	JR
PULSIFER JR	HAROLD	WINFRED	NA	MALE	JR
REED JR	BRUCE	HAL	NA	MALE	JR
ROBERTS JR	GEORGE	MARION	NA	MALE	JR
RUSSELL, JR.	KERMITT	PATRICK	NA	MALE	JR
SHADE JR	EVERETTE	LEE	NA	MALE	JR
SHEALLY JR	WILLIAM	B	NA	MALE	JR
ST JEAN JR	JOSEPH	NA	NA	MALE	JR
STANSBERRY JR	DAVID	R	NA	MALE	JR
STREETER JR	THOMAS	EARL	NA	MALE	JR
VAN DOREN JR	EDWARD	FOSTER	NA	MALE	JR
WHITEHOUSE JR	JOHN	JOSEPH	NA	MALE	JR
WHITFIELD JR	RAYMOND	E	NA	MALE	JR
WIEGOLD JR	RICHARD	MARTIN	NA	MALE	JR
WILLIAMSON JR	SOLOMAN	J	NA	MALE	JR
YOAKUM JR	JC	NA	NA	MALE	JR
SMITH MD	PATRICIA	ANN	NA	FEMALE	MD
VAN NAME	MARY	A	NA	FEMALE	NAME
VAN NAME	NANCY	HIGGINS	NA	FEMALE	NAME
VAN NAME	CHRISTOPHER	PAUL	NA	MALE	NAME
VAN NAME	GARY	GEORGE	NA	MALE	NAME
VAN NAME	MARK	L	NA	MALE	NAME
BRAKE SR ESS	CAROLYN	G	NA	FEMALE	SR
DOSS SR	MICHAEL	RAY	NA	MALE	SR
HICKS SR	WILFORD	LYTLE	SR.	MALE	SR
STIMSON SR	RICHARD	BARRETT	NA	MALE	SR
VAUGHN SR	WALTER	S	NA	MALE	SR
WHITWORTH SR	RANDY	SEAN	NA	MALE	SR
V’SOSKE	ERIKA	DONNELL	NA	FEMALE	V
MOODY V	WILLIE	HOLMES	NA	MALE	V
TENNENT V	EDWARD	S	NA	MALE	V

I eyeballed the results and removed words which appeared to be mostly validly used.

Invalid words:

As whole field:
As first word:
As last word: DR, II, III, IIII, IV, JR, MD, SR
As internal word: SR

4.4.2 First name

# regular expression to match words
w_regexp <- 
  c(w_hons, w_gen, w_spec, w_test) %>% # all special words
  unique() %>% # make it a set
  dplyr::setdiff( # remove words that appear to mostly be validly used
    c(
      "BISHOP",
      "BROTHER",
      "DOCTOR",
      "ELDER",
      "JUNIOR",
      "MASTER",
      "MISTER",
      "PASTOR",
      "PROFESSOR"
    )
  ) %>% 
  glue::glue(x = . , "\\b{x}\\b") %>%  # must be words
  glue::glue_collapse(sep = "|") # search for any

x <- d %>% 
  dplyr::mutate(
    match = 
      first_name %>% 
      stringr::str_to_upper() %>% 
      stringr::str_replace_all(pattern = "[^ A-Z]", replacement = " ") %>% 
      stringr::str_squish() %>% 
      stringr::str_extract(pattern = w_regexp)
  ) %>% 
  dplyr::filter(!is.na(match))

nrow(x)

[1] 328

x %>% 
  dplyr::arrange(match, sex, last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex	match
AAL-ANUBIAIMHOTE	DR NGOZI	NA	NA	FEMALE	DR
WILMES	FATHER	JAMES	NA	MALE	FATHER
ARMOUR	I ELISABETH	NA	NA	FEMALE	I
BOYKIN-PRIDE	I’MAN	BRIANN	NA	FEMALE	I
BRADLEY	I-ASIA	VICTORIA-CHERIS	NA	FEMALE	I
BRITTON	I-CHI	GUO	NA	FEMALE	I
BROOME	I SONYA	TIERRA	NA	FEMALE	I
BULLARD	NANCY I	W	NA	FEMALE	I
CARLYLE	I	E	NA	FEMALE	I
CARTER	I JEANNETTE	GUILBE	NA	FEMALE	I
CHANG	I-WEN	NA	NA	FEMALE	I
COLEMAN	JANA’I	D	NA	FEMALE	I
CSEH	MON I	WANG	NA	FEMALE	I
DESROSIERS	I	DARLENE	NA	FEMALE	I
DOSHI	I	NA	NA	FEMALE	I
ERVIN	I MEI	CHOU	NA	FEMALE	I
GAYE	I COLEEN	M	NA	FEMALE	I
GLASPIE	I	CHARLOTTE	NA	FEMALE	I
GREEN	I	A	NA	FEMALE	I
HALL	I’MESHA	L	NA	FEMALE	I
HEYWARD	I	CARTER	NA	FEMALE	I
HU	EDNA I	JEN	NA	FEMALE	I
HUNEYCUTT	I	SUZANNE EUDY	NA	FEMALE	I
JAN	I-RAN	HO	NA	FEMALE	I
KUEHR-MCLAREN	I	WENDY	NA	FEMALE	I
LANE	I	E MRS	NA	FEMALE	I
LEWIS	LEISA I	OITERONG	NA	FEMALE	I
MARTIN	I	MARY	NA	FEMALE	I
MENG	CHENG-I	C	NA	FEMALE	I
MOORING	I’RISHA	ORCHA’	NA	FEMALE	I
MORRIS	I LANE	NA	NA	FEMALE	I
MULLIS	LISA I	BELL	NA	FEMALE	I
NEAL	I	M	NA	FEMALE	I
PERRY	I	SUN	NA	FEMALE	I
POPE	I-ASIA	COX	NA	FEMALE	I
ROSS	I	G MRS	NA	FEMALE	I
SAUNDERS	VICKI I	SUTTON	NA	FEMALE	I
SHERWOOD	I-LI	BETH	NA	FEMALE	I
SIMMONS	I-EESHA	D	NA	FEMALE	I
SIU	I-MEI	NA	NA	FEMALE	I
SOUTHERLAND	I	KATHLEEN	NA	FEMALE	I
SUMMEY	I	V	NA	FEMALE	I
SUTARIA	DEBORAH I	S	NA	FEMALE	I
TAI	CHIH-I	NA	NA	FEMALE	I
TUTTLE	I	BESSIE	NA	FEMALE	I
WASHINGTON	CAROLINE I	HALEY	NA	FEMALE	I
WEIR	I	SUN	NA	FEMALE	I
WILEY	I’AIESHA	SHANTEA	NA	FEMALE	I
WOOD	I	F	JR	FEMALE	I
ARNOLD	I	B	NA	MALE	I
BATES	I	C	NA	MALE	I
BREWER	I	V	NA	MALE	I
CALDWELL	I	M	NA	MALE	I
CHEO	I-DEH	NA	NA	MALE	I
CLARKE	I	MITCHELL	NA	MALE	I
COLLEY	I	D	NA	MALE	I
DOWNS	I V	NA	NA	MALE	I
EDWARDS	I	J	JR	MALE	I
FERGUSON	I	M	JR	MALE	I
FU	I-KONG	BATOR	NA	MALE	I
GORDON	I	BRYCE	NA	MALE	I
GUNTER	I	W	NA	MALE	I
HAIG	I REID	S	NA	MALE	I
HICKS	I FAISON	NA	NA	MALE	I
HINES	I	ALAN	NA	MALE	I
HOOD	I	G	NA	MALE	I
HOWARD	I	CLARENCE	NA	MALE	I
JENKINS	I	D	III	MALE	I
JOHNSON	I	M	NA	MALE	I
JOHNSTON	I	C	NA	MALE	I
KELLY	I	PERRY	NA	MALE	I
KINLAW	I	W	NA	MALE	I
LAKE	I	BEVERLY	JR	MALE	I
LITTLE	I	MAYO	JR	MALE	I
LONGMUIR	I	S	NA	MALE	I
LYONS	I	CHARLES	NA	MALE	I
MANESS	I	M	NA	MALE	I
MCNEIL	I	J	NA	MALE	I
MILLER	I	J	NA	MALE	I
MILLER	I	D MCGILVRAY	NA	MALE	I
PALMER	I	JEREMIAH	NA	MALE	I
PATTERSON	I	EUGENE	NA	MALE	I
PAUL	I	B	NA	MALE	I
PLYLER	I	F	JR	MALE	I
POPE	I	H	JR	MALE	I
POWELL	I	HILL	NA	MALE	I
POWELL	STEVEN I	VANROOY	NA	MALE	I
QUINN	I	J	NA	MALE	I
QUINN	I	J	JR	MALE	I
RUSS	I	V	NA	MALE	I
SMITH	I	BRUCE	NA	MALE	I
SMITH	I	MELVIN	NA	MALE	I
SOLOMON	I	S	NA	MALE	I
STONE	I	L	NA	MALE	I
TERRY	I	B	III	MALE	I
TRAVIS	I	A	NA	MALE	I
WAKEFIELD	I	NELSON	NA	MALE	I
WALLACE	I	J	NA	MALE	I
WARREN	I	NA	NA	MALE	I
WU	I-CHAN	JOHN	NA	MALE	I
MANUEL	WALTER III	NA	NA	MALE	III
MCPHERSON	VAN III	NA	NA	MALE	III
NASH	SAMUEL III	NA	NA	MALE	III
PATALANO	LOUIS III	NA	NA	MALE	III
SCOTT	CALVIN III	NA	NA	MALE	III
SILVER	III	HAYDEN	NA	MALE	III
COPELAND	IV	EDWARD JAMES	NA	MALE	IV
ANDERSON	ELBERT JR	NA	NA	MALE	JR
BARNEY	LEO JR	NA	NA	MALE	JR
BOWLES	ROBERT JR	NA	NA	MALE	JR
BRYANT	FREDDIE JR	NA	NA	MALE	JR
COLLINS	JACK JR	NA	NA	MALE	JR
DARRELL	JAMES JR	NA	NA	MALE	JR
DAVIS	HENRY JR	NA	NA	MALE	JR
GERTZ	JR	RICHARD	NA	MALE	JR
HOAGLAND	JR	SANDY	NA	MALE	JR
HOLLEY	JR	JOHN MARSHAL	NA	MALE	JR
JONES	JR	MICHAEL	NA	MALE	JR
JOYNER	JR	EARNEST	NA	MALE	JR
MCADAMS	WILL,JR	NA	NA	MALE	JR
MCCLELLAND	ERNEST JR	NA	NA	MALE	JR
MCCOY	JR	RICHARD TUNN	NA	MALE	JR
MCIVER	SIM JR	NA	NA	MALE	JR
MCLEOD	WILLIE JR	NA	NA	MALE	JR
MULL	MADISON JR	NA	NA	MALE	JR
PALMS	DONALD JR	NA	NA	MALE	JR
PEOPLES	LONZO JR	NA	NA	MALE	JR
ROSADO	ALEJANDRO JR	NA	NA	MALE	JR
THOMPSON	JOSEPHUS JR	NA	NA	MALE	JR
TILLMAN	BENNIE JR	NA	NA	MALE	JR
TOOLE	JR	NA	NA	MALE	JR
WOODS	HOUSTON JR	NA	NA	MALE	JR
STOCKELL	MD	COOPER	III	MALE	MD
SPEIGHT	MISS STEPHANI	RENEE’	NA	FEMALE	MISS
FATE	MR	NA	NA	MALE	MR
KANE	MR	NA	NA	MALE	MR
BECK	MRS WILLIAM	E	NA	FEMALE	MRS
BINGMAN	GRAY MRS	NA	NA	FEMALE	MRS
BURKE	MRS GEORGE	W	NA	FEMALE	MRS
CARTER	PAUL MRS	NA	JR	FEMALE	MRS
CHATMAN	MRS H	L	NA	FEMALE	MRS
COVINGTON	EDNA(MRS	PERRY, JR)	NA	FEMALE	MRS
CROMER	BETTY MRS	A	NA	FEMALE	MRS
DAVENPORT	MRS H	T	NA	FEMALE	MRS
DODSON	RAY MRS	NA	NA	FEMALE	MRS
EATON	MRS JOHN	C	NA	FEMALE	MRS
ESTES	ALMA MRS	A	NA	FEMALE	MRS
FIELDS	MRS G	CLINTON	NA	FEMALE	MRS
FIELDS	MRS JAMES	C	NA	FEMALE	MRS
FULP	JAMES MRS	C	NA	FEMALE	MRS
GIBSON	H MRS	L	NA	FEMALE	MRS
GOOLSBY	EUGENE MRS	NA	NA	FEMALE	MRS
GURGANIOUS	JOHN MRS	HALLIE	NA	FEMALE	MRS
HAMRICK	JOHN R MRS	MARGARET	NA	FEMALE	MRS
HARRIS	MRS FRED	W	NA	FEMALE	MRS
HARRIS	MRS P	D	NA	FEMALE	MRS
HARRIS	MRS WILLIAM	W	NA	FEMALE	MRS
HARTIS	FRANK E MRS	THAMES	NA	FEMALE	MRS
HOLLIDAY	MRS JOSEPH	NA	NA	FEMALE	MRS
JEFFERSON	MRS ATHOL	G	NA	FEMALE	MRS
JOHNSON	MRS CLYDE	W	NA	FEMALE	MRS
LAMB	WILSON MRS	C	NA	FEMALE	MRS
LARIMORE	WILLIAM MRS	NA	NA	FEMALE	MRS
LUU	MRS	NA	NA	FEMALE	MRS
MABE	STEVE MRS	NA	NA	FEMALE	MRS
MARTIN	JAMES MRS	H	NA	FEMALE	MRS
MASSAGEE	JAMES H MRS	SUE	NA	FEMALE	MRS
MOODY	MRS WILLARD	W	NA	FEMALE	MRS
MORGAN	MRS ROY	A	NA	FEMALE	MRS
NICHOLS	DORIS ( MRS W	NA	NA	FEMALE	MRS
POPE	MRS O	N	JR	FEMALE	MRS
REICH	MRS LESTER	G	NA	FEMALE	MRS
RHONEY	ROBERT MRS	T	NA	FEMALE	MRS
RIVES	MRS WILBUR	A	NA	FEMALE	MRS
SCALES	BETTY MRS	H	NA	FEMALE	MRS
SMITH	MRS WILLIAM JOE	DAVIS	NA	FEMALE	MRS
TIMMONS	THOMAS MRS	E	NA	FEMALE	MRS
TRULL	JAMES MRS	T	NA	FEMALE	MRS
WARD	MARVIN MRS	M	NA	FEMALE	MRS
WHITE	JOE MRS	MRS	NA	FEMALE	MRS
WOODLEY	MRS WALLACE	( RUTH )	NA	FEMALE	MRS
QUEEN	GERALDINE(NMN	NA	NA	FEMALE	NMN
BORDERS	EUGENE(NMN)	NA	NA	MALE	NMN
FOSTER	OTIS(NMN)	JR	NA	MALE	NMN
FEATHERSTONE	REV. ROBERT	A	NA	MALE	REV
GILDEA	SISTER	THERESINE	NA	FEMALE	SISTER
KELLY	SISTER	ANN	NA	FEMALE	SISTER
PEGUESE	SISTER	GIRTRUE	NA	FEMALE	SISTER
ROSS	SISTER	S	NA	FEMALE	SISTER
TANCRAITOR	SISTER MAXINE	ELIZABETH	NA	FEMALE	SISTER
DUNTON	JULIAN SR	NA	NA	MALE	SR
GRAHAM	STEPHEN SR	LEGREE	NA	MALE	SR
PHILLIPS	SR	DAYLE KELLEY	NA	MALE	SR
ADAMS	V	JAN	NA	FEMALE	V
ANDERSON	V	RUTH K	NA	FEMALE	V
BATKIN	V	MARIA	NA	FEMALE	V
BENFIELD	RHONDA V	NA	NA	FEMALE	V
BOWDEN	V	RUTH	NA	FEMALE	V
BOYD	V	MARIE	NA	FEMALE	V
BRANDT	V	KATHLEEN GRY	NA	FEMALE	V
CALHOUN	V	ANNE	NA	FEMALE	V
CARLAND	V	ANN	NA	FEMALE	V
CARTER	PAUL V	MRS	NA	FEMALE	V
CAVENDER	V	DORIS	NA	FEMALE	V
COOK	INEZ V	CARY	NA	FEMALE	V
DALBERG	V	ANDREA	NA	FEMALE	V
DOTY	V’ONA	GILBERT	NA	FEMALE	V
EDWARDS	V	ERLINE	NA	FEMALE	V
EVANS-SMITH	V MARIE	HUMPHERY	NA	FEMALE	V
FINLEY	V	ANNE	NA	FEMALE	V
FUTRELL	V JEANINE	BOWDEN	NA	FEMALE	V
GIBBS	V	WILLA	NA	FEMALE	V
GLENN	V’SHATAVIA	D	NA	FEMALE	V
HALL	CATHEDRIA V	HOOKER	NA	FEMALE	V
HALL	V	JUANITA	NA	FEMALE	V
HAMILTON	V KAYE	NA	NA	FEMALE	V
JAYANTY	LAKSHMI S V	S	NA	FEMALE	V
JOHNSON	V	JOLINE	NA	FEMALE	V
KENNEDY	V0NCIEAL	LEE	NA	FEMALE	V
KRITES	V	C	NA	FEMALE	V
LANCASTER	ALDA V	LIMBAUGH	NA	FEMALE	V
LEE	V	JUANITA	NA	FEMALE	V
LEE	V	FLORENCE	NA	FEMALE	V
LYONS	V	BETTIE	NA	FEMALE	V
MARSHALL	CALLIE V.	LUTZ	NA	FEMALE	V
MAYBERRY	V	JACQUELINE	NA	FEMALE	V
MOCK	V CHARLENE	D	NA	FEMALE	V
MOORMAN	V	E	NA	FEMALE	V
MORTON	SANDRA V	GOSNELL	NA	FEMALE	V
OSLEY	V	BONITA NAFZIGER	NA	FEMALE	V
OWENSBY	V	ANN	NA	FEMALE	V
PAYNE	V	LUCILLE	NA	FEMALE	V
PERERA	V	MALLIKA	NA	FEMALE	V
POWELL	V	ESTELLE	NA	FEMALE	V
RASH	V	ANDERSON	NA	FEMALE	V
RAY	V	FRANCIS	NA	FEMALE	V
SEMONCHE	LAURA V	A	NA	FEMALE	V
SHAFFER	V LYNNE	STRICKLAND	NA	FEMALE	V
SHELF	V	S MRS	NA	FEMALE	V
SMELTZER	V	DIANE	NA	FEMALE	V
SMITH	V	RAE	NA	FEMALE	V
STANTON	V	GAYLE	NA	FEMALE	V
STERLING	V	LEE	NA	FEMALE	V
STODDARD	V	CHRISTIVE	NA	FEMALE	V
STREIFF	CONNIE V	R	NA	FEMALE	V
TEAGUE	V	MICHELLE	NA	FEMALE	V
TERRY	CAROLYN V	MASK	NA	FEMALE	V
THOMPSON	V	DELORES	NA	FEMALE	V
TINNEY	V	LEE W	NA	FEMALE	V
VANNOY	V	GAIL	NA	FEMALE	V
WAGGONER	V	C	NA	FEMALE	V
WALKER	V	FRANCES	NA	FEMALE	V
WHITE	V CAROLE	NA	NA	FEMALE	V
WILLIAMS	JACQUELYNE V.	MOORE	NA	FEMALE	V
WRIGHT	O V	LEDFORD	NA	FEMALE	V
ADAMS	A V	NA	NA	MALE	V
ADAMS	V	WAYNE	NA	MALE	V
ALLEN	V	B	NA	MALE	V
AVVA	V	SARMA	NA	MALE	V
BARBOUR	V	KEITH	NA	MALE	V
BAZEMORE	V	S	NA	MALE	V
BOWMAN	V	C	NA	MALE	V
BOYKIN	V	RAYMOND	JR	MALE	V
CLINE	V	OTHO	JR	MALE	V
CORRELL	V	C	NA	MALE	V
DEAL	R V	ROB	NA	MALE	V
DEHART	V	L	JR	MALE	V
DREYER	V	DEAN	NA	MALE	V
GORDON	V	H	NA	MALE	V
HELTON	V JOHNNY	NA	NA	MALE	V
HICKS	V	L	NA	MALE	V
HOLLAND	V	L	NA	MALE	V
HOLLINSHED	V	E	JR	MALE	V
HONEYCUTT	V	J	NA	MALE	V
HOUSEHOLDER	V	R	NA	MALE	V
IDOL	V	F	NA	MALE	V
IRAGGI	V	J	NA	MALE	V
IYER	V V	NA	NA	MALE	V
JACKSON	V	L	NA	MALE	V
JEFFRIES	V’GER	S	NA	MALE	V
JONES	V	W	NA	MALE	V
KRASNIEWICZ	V	A	NA	MALE	V
KRITES	V	C	NA	MALE	V
KRYSTOFIAK	V	L	NA	MALE	V
LEWIS	V	M	NA	MALE	V
LIND	V WILLIAM	NA	JR	MALE	V
LOCKAMY	V	B	NA	MALE	V
LOMBARDI	V ALAN	NA	NA	MALE	V
MANGIPUDI	V RAO	NA	NA	MALE	V
MANN	R V	NA	NA	MALE	V
MARTIN	V	GRAY	JR	MALE	V
MATHENY	V	O	JR	MALE	V
MCKINNEY	V	A	NA	MALE	V
MODLIN	V	WAYNE	NA	MALE	V
NORMAN	V WAYNE	NA	NA	MALE	V
OAKLEY	V	BRADSHER	III	MALE	V
OATES	A V	NA	NA	MALE	V
OGLESBY	V	BOYCE	JR	MALE	V
PFAHL	V KEVIN	NA	NA	MALE	V
PIERANNUNZI	V PAUL	NA	NA	MALE	V
PLAYER	V	STEPHEN	NA	MALE	V
POWELL	V	A	JR	MALE	V
RASH	A V	NA	NA	MALE	V
REDMOND	V	PRESTON	JR	MALE	V
REVELS	V D	NA	NA	MALE	V
REYNOLDS	V	FRANK	NA	MALE	V
RUMLEY	V	CLIFTON	NA	MALE	V
SCALDARA	A V	NA	NA	MALE	V
SHIELDS	V	E	NA	MALE	V
SLADE	V	T	NA	MALE	V
TEMPLE	V	W	NA	MALE	V
WARD	V	STUART	JR	MALE	V
WHITE	A V	NA	JR	MALE	V
WHITSON	V	L	NA	MALE	V
WOOTEN	V	ALDENE	NA	MALE	V
WYATT	V	CHARLES	NA	MALE	V
ANTHONY	VI	JOHNSON	NA	FEMALE	VI
DO	VI	THUY	NA	FEMALE	VI
GREENE	VI	HEGE	NA	FEMALE	VI
HUTCHINSON	VI	THI	NA	FEMALE	VI
LAI	VI	LE	NA	FEMALE	VI
NGUYEN	VI	THOAI	NA	FEMALE	VI
NGUYEN	VI	TUONG	NA	FEMALE	VI
TOWNSEND	VI	S	NA	FEMALE	VI
VO	VI	PHUONG	NA	FEMALE	VI
GALLOWAY	VI CKY	RONALD	NA	MALE	VI
THAI	VI	KY	NA	MALE	VI
TRAN	VI	TAN	NA	MALE	VI

I eyeballed the results and removed words which appeared to be mostly validly used.

Invalid words:

As whole field: FATHER, III, IV, JR, MD, MR, MRS, SISTER, SR
As first word: DR, MISS, MRS, REV, SISTER
As last word: III, JR, MRS, NMN, SR
As internal word: MRS

4.4.3 Middle name

# regular expression to match words
w_regexp <- 
  c(w_hons, w_gen, w_spec, w_test) %>% # all special words
  unique() %>% # make it a set
  dplyr::setdiff( # remove words that appear to mostly be validly used
    c(
      "BISHOP",
      "BLIND",
      "BR",
      "BROTHER",
      "DOCTOR",
      "ELDER",
      "FIRST",
      "JR", # invalid & too many to display 
      "JUNIOR",
      "MASTER",
      "MISTER",
      "MRS", # invalid & too many to display
      "NMN", # invalid & too many to display
      "PASTOR",
      "SENIOR",
      "SISTER",
      "I",
      "V",
      "VI",
      "VOTER"
    )
  ) %>% 
  glue::glue(x = . , "\\b{x}\\b") %>%  # must be words
  glue::glue_collapse(sep = "|") # search for any

x <- d %>% 
  dplyr::mutate(
    match = 
      midl_name %>% 
      stringr::str_to_upper() %>% 
      stringr::str_replace_all(pattern = "[^ A-Z]", replacement = " ") %>% 
      stringr::str_squish() %>% 
      stringr::str_extract(pattern = w_regexp)
  ) %>% 
  dplyr::filter(!is.na(match))

nrow(x)

[1] 98

x %>% 
  dplyr::arrange(match, sex, last_name, first_name) %>% 
  knitr::kable()

last_name	first_name	midl_name	name_sufx_cd	sex	match
WISE	DIANA	AKA	NA	FEMALE	AKA
CACCAMO	KATHLEEN	DR	NA	FEMALE	DR
DUNCAN	ROSALYN	DR	NA	FEMALE	DR
GEORGE	AMAY	DR	NA	FEMALE	DR
VANN	ELLEN	DR	NA	FEMALE	DR
ELESHA	WILLIAM	DR	NA	MALE	DR
ROBICSEK	FRANCIS	DR	NA	MALE	DR
ROPER	THOMAS	E DR	NA	MALE	DR
VETTER	JOHN	S DR	NA	MALE	DR
BIRCHFIELD	HARRY	LYNN II	NA	MALE	II
DINGMAN	LEONARD	ALAN II	NA	MALE	II
FRADY	ROBERT	GLENN II	NA	MALE	II
GLOVER	CHARLES	WORTH II	NA	MALE	II
HAWKINS	ROGER	LARRY II	NA	MALE	II
HUNTER	ERNEST	II	NA	MALE	II
KELLY	DAVID	LEE II	NA	MALE	II
KERR	JAMES	II	NA	MALE	II
KUHNE	KURT	II	NA	MALE	II
ROGERS	SYLVESTER	II	SR	MALE	II
SHERWOOD	GEORGE	ROYALL II	NA	MALE	II
SOGLUIZZO	JOSEPH	JOHN II	NA	MALE	II
VAN GORDER	CHARLES	OSCAR II	NA	MALE	II
WALSTON	CHARLES	EDWARD II	NA	MALE	II
WATKINS	MONROE	II	NA	MALE	II
YOUNGMAN	THOMAS	ARDEN II	NA	MALE	II
BROWN	HARRY	III	NA	MALE	III
BROWN	MILES	III	NA	MALE	III
COOPER	DALTON	III	NA	MALE	III
DAILEY	LANGRA	III	NA	MALE	III
FUNDERBURK	TRAVIS	III	NA	MALE	III
GADISON	NATHANIEL	III	NA	MALE	III
GAY	ROBERT	HENRY, III.	NA	MALE	III
GEE	LAWRENCE	III	NA	MALE	III
HARPER	GUS	III	NA	MALE	III
HOLT	ISAAC	III	NA	MALE	III
HUMPHREY	ROLAND	M III	NA	MALE	III
JOHNSON	SHADE	III	NA	MALE	III
JOYNER	DOUGLAS	III	NA	MALE	III
LYNCH	ABRAHAM	III	NA	MALE	III
MCGILVERY	ROBERT	III	NA	MALE	III
MCILWAIN	FERRY	III	NA	MALE	III
PHILLIPS	ALEXANDER ROW	III	NA	MALE	III
PRICE	PAUL	III	III	MALE	III
STEELE	HARVEY	III	NA	MALE	III
TERRY	GEORGE	III	NA	MALE	III
THOMAS	PAUL	III	NA	MALE	III
BAKER	LOUIS	IV	NA	MALE	IV
CROSS	EUGENE	IV	NA	MALE	IV
ESPOSITO	VINCENT	JOHN IV	NA	MALE	IV
GUNNOE	ROBERT	FELIX IV	NA	MALE	IV
HORNEY	HARRISON	MARTIN IV	NA	MALE	IV
HUMBERT	JOHN	LAWRENCE IV	NA	MALE	IV
BRONSON	JENNIFER	MD	NA	FEMALE	MD
MCGIMSEY	JAMES	F JR MD	NA	MALE	MD
BOLES	FAUSTINE	MISS	NA	FEMALE	MISS
BREEZE	ALMA	EARL MISS	NA	FEMALE	MISS
DAVIS	JULIA	MISS	NA	FEMALE	MISS
GARBER	CORNELIA	MISS	NA	FEMALE	MISS
HAM	MABLE	MISS	NA	FEMALE	MISS
MCKOY	CAROL	MISS	NA	FEMALE	MISS
MORRISON	LULA	MISS	NA	FEMALE	MISS
MOSER	ROSE	MISS	NA	FEMALE	MISS
PHILSON	CHERYL	MISS	NA	FEMALE	MISS
ATKINS	DAVID	GLEN MR	NA	MALE	MR
LIVENGOOD	THURMOND	MS	NA	FEMALE	MS
STINTZI	MANDI	LY NN	NA	FEMALE	NN
CRISSMAN	JASON	LY NN	NA	MALE	NN
GREENE	LESTER	D(NN)	NA	MALE	NN
LUKER	DANIEL	B(NN)	NA	MALE	NN
JOHNSON	ROBERT	REV	NA	MALE	REV
WORKMAN	NATHANIEL	REV	NA	MALE	REV
ABBAS	MOHAMED	SR	NA	MALE	SR
ANSTEAD	LENDELL	SR	NA	MALE	SR
ANTHONY	EVERETT	SR	NA	MALE	SR
ARMSTON	MILTON	SR	NA	MALE	SR
ARRINGTON	LEROY	SR	NA	MALE	SR
BATTLE	NATHANIEL	SR	NA	MALE	SR
BERRY	RALPH	SR	NA	MALE	SR
BROWN	NELSON	SR	NA	MALE	SR
CARTER	FOREST	SR	NA	MALE	SR
CLARK	JEFFERY	SR	NA	MALE	SR
DEGRAFFENRIED	EDWARD	(NMN)SR	NA	MALE	SR
EUBANKS	ALBERT	SR	NA	MALE	SR
HARRIS	MARION	SR	NA	MALE	SR
JOHNSON	FRED	ALAN SR	NA	MALE	SR
JONES	WALTER	SR	NA	MALE	SR
LANE	LORENZA	SR	NA	MALE	SR
LUPTON	DENNIS	WAYNE SR	NA	MALE	SR
LYNCH	LOUIS	SR	NA	MALE	SR
MILLER	CLARENCE	SR	NA	MALE	SR
OSBORNE	JOHN	SR	NA	MALE	SR
PERAGINE	PAUL	SR	NA	MALE	SR
SELLARS	LARRY	SR	NA	MALE	SR
STRICKLAND	TIMOTHY	SR	NA	MALE	SR
WHITAKER	WILLIAM	SR	NA	MALE	SR
WHITNEY	WILLIAM	PRESTON SR	NA	MALE	SR
WIGGINS	MINOR	SR	NA	MALE	SR
WILLIAMS	ERVIN	W., SR.,	NA	MALE	SR

I eyeballed the results and removed words which appeared to be mostly validly used.

Invalid words:

As whole field: AKA, DR, II, III, IV, JR, MD, MISS, MRS, MS, NMN, REV, SR
As first word: JR, MRS
As last word: DR, II, III, IV, JR, MD, MISS, MR, MRS, NMN, NN, SR
As internal word: JR

Timing

Computation time (excl. render): 498.294 sec elapsed

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.10

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] hexbin_1.28.2   glue_1.4.2      knitr_1.30      skimr_2.1.2    
 [5] fst_0.9.4       forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
 [9] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.4   
[13] ggplot2_3.3.3   tidyverse_1.3.0 tictoc_1.0      here_1.0.1     
[17] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5        lattice_0.20-41   lubridate_1.7.9.2 utf8_1.1.4       
 [5] assertthat_0.2.1  rprojroot_2.0.2   digest_0.6.27     repr_1.1.0       
 [9] R6_2.5.0          cellranger_1.1.0  backports_1.2.1   reprex_0.3.0     
[13] evaluate_0.14     highr_0.8         httr_1.4.2        pillar_1.4.7     
[17] rlang_0.4.10      readxl_1.3.1      rstudioapi_0.13   whisker_0.4      
[21] rmarkdown_2.6     labeling_0.4.2    munsell_0.5.0     broom_0.7.3      
[25] compiler_4.0.3    httpuv_1.5.4      modelr_0.1.8      xfun_0.20        
[29] base64enc_0.1-3   pkgconfig_2.0.3   htmltools_0.5.0   tidyselect_1.1.0 
[33] bookdown_0.21     fansi_0.4.1       crayon_1.3.4      dbplyr_2.0.0     
[37] withr_2.3.0       later_1.1.0.1     grid_4.0.3        jsonlite_1.7.2   
[41] gtable_0.3.0      lifecycle_0.2.0   DBI_1.1.0         git2r_0.28.0     
[45] magrittr_2.0.1    scales_1.1.1      cli_2.2.0         stringi_1.5.3    
[49] farver_2.0.3      renv_0.12.5       fs_1.5.0          promises_1.1.1   
[53] xml2_1.3.2        ellipsis_0.3.1    generics_0.1.0    vctrs_0.3.6      
[57] tools_4.0.3       hms_0.5.3         parallel_4.0.3    yaml_2.2.1       
[61] colorspace_2.0-0  rvest_0.3.6       haven_2.3.1

01-5_check_name

Check name variables

Ross Gayler

2021-01-12

1 Introduction

2 Name length

2.1 last_name

2.2 first_name

2.3 midl_name

3 name_sufx_cd

4 Standardisation

4.1 Lower-case letters.

4.2 Non-alphanumeric

4.2.1 Hyphen

4.2.2 Quote

4.2.3 Period

4.2.4 Comma

4.2.5 Other non-alphanumeric

4.3 Digits

4.3.1 Zero

4.3.2 One

4.3.3 Other digits

4.4 Special words

4.4.1 Last name

4.4.2 First name

4.4.3 Middle name

Timing