01-3_check_resid

Check residence variables

workflowr

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Last updated: 2021-01-12

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Knit directory: fa_sim_cal/

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---

# 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 (01-3_check_resid) characterises the residence variables in the saved subset of the data. These are the residential address and the phone number (which is tied to the address if the telephone is a land-line).

We have no intention of using the residence variables as predictors for entity resolution. However, they may be of use for manually checking the results of entity resolution. Consequently, the checking done here is minimal.

---

Define the residence variables.

vars_resid <- c(
  "unit_num", "house_num",      
  "half_code", "street_dir", "street_name", "street_type_cd", "street_sufx_cd",
  "res_city_desc", "state_cd", "zip_code",
  "area_cd", "phone_num"
)

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 = vars_resid
) %>% 
  tibble::as_tibble()
dim(d)

[1] 4099699      12

Look at some examples.

Residential address:

d %>% 
  dplyr::select(unit_num : zip_code) %>% 
  dplyr::slice_sample(n = 20) %>% 
  knitr::kable()

unit_num	house_num	half_code	street_dir	street_name	street_type_cd	street_sufx_cd	res_city_desc	state_cd	zip_code
NA	2883	NA	NA	EASY	ST	NA	DUNN	NC	28334
NA	665	NA	NA	MOUNT OLIVE CHURCH	RD	NA	FRANKLINTON	NC	27525
NA	417	NA	NA	EDINBURGH	DR	NA	FAYETTEVILLE	NC	28303
NA	2517	NA	NA	KERR	AVE	N	WILMINGTON	NC	28405
NA	2337	NA	NA	CLEVELAND	AVE	NA	FAYETTEVILLE	NC	28312
NA	9435	NA	NA	NC HWY 87	NA	N	PITTSBORO	NC	27312
NA	215	NA	NA	ORANGE	ST	NA	ROCKY MOUNT	NC	27801
A317	733	NA	NA	PLANTATION ESTATES	DR	NA	MATTHEWS	NC	28105
2	5311	NA	NA	25 70	HWY	NA	MARSHALL	NC	28753
NA	424	NA	N	KING	ST	NA	ST PAULS	NC	28384
NA	13429	NA	NA	OLD STAGE	RD	NA	WILLOW SPRINGS	NC	27592
D	3247	NA	NA	CYPRESS PARK	RD	NA	GREENSBORO	NC	27407
NA	9925	NA	NA	PALLISERS	TER	NA	CHARLOTTE	NC	28210
NA	114	NA	NA	GIBBS	RD	NA	NEW BERN	NC	28560
NA	8885	NA	NA	ROCKY RIVER	RD	NA	HARRISBURG	NC	28075
A	105	NA	NA	TAMALPAIS	PT	NA	CHAPEL HILL	NC	27514
NA	102	NA	N	ROXFORD	RD	NA	KINGS MOUNTAIN	NC	28086
NA	551	NA	NA	NICK MCLEAN	RD	NA	BUNNLEVEL	NC	28323
NA	2913	NA	NA	WINDSOR	AVE	NA	CHARLOTTE	NC	28209
NA	2200	NA	NA	GRESHAM LAKE	RD	NA	RALEIGH	NC	27615

Telephone number:

d %>% 
  dplyr::select(area_cd : phone_num) %>% 
  dplyr::slice_sample(n = 20) %>% 
  knitr::kable()

area_cd	phone_num
252	5364553
252	5235950
NA	NA
828	9269783
910	5755969
NA	NA
NA	NA
NA	NA
NA	NA
704	8065796
NA	NA
704	3920844
NA	NA
828	6705254
252	7474543
252	6366840
NA	NA
NA	NA
NA	NA
910	8924300

2 Dwelling

unit_num Residential address unit number
house_num Residential address street number
half_code Residential address street number half code

d %>% 
  dplyr::select(unit_num, house_num, half_code) %>% 
  skimr::skim()

Warning in grepl("^\\s+$", x): input string 13013 is invalid in this locale

Warning in grepl("^\\s+$", x): input string 27075 is invalid in this locale

Warning in grepl("^\\s+$", x): input string 35910 is invalid in this locale

Warning in grepl("^\\s+$", x): input string 40713 is invalid in this locale

Warning in grepl("^\\s+$", x): input string 49523 is invalid in this locale

Table 2.1: Data summary

Name	Piped data
Number of rows	4099699
Number of columns	3
_______________________
Column type frequency:
character	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
unit_num	3755239	0.08	1	7	0	16116	0
house_num	0	1.00	1	6	0	27534	0
half_code	4088996	0.00	1	1	0	41	0

• unit_num 8% filled

• house_num 100% filled

• half_code 0.3% filled

  • Warning messages indicate some invalid multibyte strings

Look at half_code.

table(d$half_code, useNA = "ifany")

      -       /       `       +    \xab    \xbd       0       1       2       3 
      1       8       5      32       3    1730       1      44      35       5 
      4       5       6       7       8       9       A       B       C       D 
     10       6       6       5       4       1    3313    2725     948     569 
      E       F       G       H       I       J       K       L       M       N 
    273     214     154     174      36      78      58      48      48      33 
      O       P       Q       R       S       T       U       V       W       X 
      6      21       7      13      38      13       3       6      24       4 
      Y    <NA> 
      1 4088996 

d %>% 
  dplyr::filter(!is.na(half_code)) %>% 
  dplyr::select(unit_num : street_type_cd) %>% 
  dplyr::slice_sample(n = 20)

# A tibble: 20 x 6
   unit_num house_num half_code street_dir street_name         street_type_cd
   <chr>    <chr>     <chr>     <chr>      <chr>               <chr>         
 1 <NA>     640       "\xab"    N          LOUISIANA           AVE           
 2 <NA>     21        "B"       <NA>       SPICEWOOD           RD            
 3 <NA>     803       "C"       <NA>       DOUGLAS             DR            
 4 <NA>     601       "B"       W          GRAHAM              ST            
 5 <NA>     102       "\xbd"    <NA>       FAIRWAY             DR            
 6 A        141       "A"       <NA>       HERRON              AVE           
 7 <NA>     795       "\xbd"    <NA>       HENDERSONVILLE      RD            
 8 <NA>     1005      "C"       <NA>       EAST                ST            
 9 <NA>     2840      "D"       <NA>       ROGERS              RD            
10 <NA>     717       "C"       <NA>       DEEP FORD           RD            
11 <NA>     3211      "A"       <NA>       BIG WOODS           RD            
12 <NA>     626       "A"       <NA>       LUMBEE              ST            
13 <NA>     910       "D"       <NA>       CULBRETH            AVE           
14 <NA>     155       "B"       <NA>       HIBRITEN MOUNTAIN   RD            
15 <NA>     901       "B"       <NA>       OAKLAWN             DR            
16 <NA>     1314      "A"       <NA>       BEAVER CREEK SCHOOL RD            
17 <NA>     100       "A"       <NA>       CIRCLE              CT            
18 <NA>     301       "`"       <NA>       CHAMPION            DR            
19 <NA>     21        "B"       <NA>       MASTERS COURT       DR            
20 <NA>     114       "D"       <NA>       SPRUCE HILL         LN            

half_code appears to indicate where there are multiple buildings on one street-numbered block. Typical values would be A, B, …

3 Street

d %>% 
  dplyr::select(starts_with("street_")) %>% 
  skimr::skim()

Table 3.1: Data summary

Name	Piped data
Number of rows	4099699
Number of columns	4
_______________________
Column type frequency:
character	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
street_dir	3812561	0.07	1	2	0	8	0
street_name	7	1.00	1	30	0	83244	0
street_type_cd	154594	0.96	2	4	0	119	0
street_sufx_cd	3941004	0.04	1	3	0	11	0

• street_dir 7% filled
• street_name ~100% filled (7 missing)
• street_type_cd 96% filled
• street_sufx_cd 4% filled

3.1 street_dir

street_dir Residential address street direction (N,S,E,W,NE,SW, etc.)

table(d$street_dir, useNA = "ifany")

      E       N      NE      NW       S      SE      SW       W    <NA> 
  71244   72784    2161     911   68612    1221     729   69476 3812561 

3.2 street_name

street_name Residential address street name

Seven records are missing street name. Look at them.

d %>% 
  dplyr::filter(is.na(street_name)) %>% 
  knitr::kable()

unit_num	house_num	half_code	street_dir	street_name	street_type_cd	street_sufx_cd	res_city_desc	state_cd	zip_code	area_cd	phone_num
NA	0	NA	NA	NA	NA	NA	STONY POINT	NC	28678	NA	NA
NA	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	0	NA	NA	NA	NA	NA	BELMONT	NC	28012	NA	NA

• Seven records have no residential address. Are these homeless people?

Some street names are very short. Look at the distribution of length of street name.

summary(stringr::str_length(d$street_name))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1.00    6.00    8.00    8.78   11.00   30.00       7 

d %>% 
  ggplot() +
  geom_histogram(aes(x = stringr::str_length(street_name)), binwidth = 1) +
  scale_y_sqrt()

Warning: Removed 7 rows containing non-finite values (stat_bin).

Look at examples of short street names.

d %>% 
  dplyr::filter(stringr::str_length(street_name) == 1) %>% 
  dplyr::select(starts_with("street_"), res_city_desc) %>%
  dplyr::slice_sample(n = 20) %>% 
  knitr::kable()

street_dir	street_name	street_type_cd	street_sufx_cd	res_city_desc
NA	C	ST	NA	NEWPORT
W	C	ST	NA	ERWIN
NA	A	ST	NA	CAMP LEJEUNE
E	I	ST	NA	ERWIN
NA	F	AVE	NA	KURE BEACH
NA	A	ST	NA	LANSING
E	K	ST	NA	ERWIN
E	C	ST	NA	BUTNER
NA	D	ST	NA	SPRING LAKE
NA	B	AVE	NA	CHARLOTTE
E	B	ST	NA	BUTNER
W	B	ST	NA	KANNAPOLIS
E	C	ST	NA	BUTNER
E	L	ST	NA	ERWIN
NA	A	ST	NA	CAMP LEJEUNE
NA	E	ST	NA	NORTH WILKESBORO
NA	J	AVE	NA	KURE BEACH
W	C	ST	NA	BUTNER
E	J	ST	NA	MAIDEN
NA	E	ST	NA	NORTH WILKESBORO

d %>% 
  dplyr::filter(stringr::str_length(street_name) == 2) %>% 
  dplyr::select(starts_with("street_"), res_city_desc) %>%
  dplyr::slice_sample(n = 20) %>% 
  knitr::kable()

street_dir	street_name	street_type_cd	street_sufx_cd	res_city_desc
NA	DJ	DR	NA	BENSON
NA	KY	FLDS	NA	WEAVERVILLE
NA	63	HWY	NA	LEICESTER
NA	KY	FLDS	NA	WEAVERVILLE
NA	OW	LN	NA	DUNN
N	ST	NA	NA	ASHEVILLE
NA	KY	FLDS	NA	WEAVERVILLE
NA	RH	RD	NA	HICKORY
NA	23	HWY	NA	MARS HILL
NA	DJ	DR	NA	STATESVILLE
NA	CY	CIR	NA	CONCORD
NA	JR	RD	NA	TIMBERLAKE
NA	WR	LN	NA	CLAREMONT
NA	63	HWY	NA	LEICESTER
NA	19	HWY	NA	MARS HILL
NA	JR	RD	NA	TIMBERLAKE
NA	23	HWY	NA	MARS HILL
NA	AA	DR	NA	TAYLORSVILLE
NA	BR	DR	NA	MARION
NA	RH	RD	NA	HICKORY

I checked some of these examples against a map.

• Some streets have names like A, B, C, …
• “JC Road” is a valid street name

Look at examples of long street names.

d %>% 
  dplyr::filter(stringr::str_length(street_name) >= 28) %>% 
  dplyr::select(starts_with("street_"), res_city_desc) %>%
  dplyr::distinct(.keep_all = TRUE) %>% 
  dplyr::arrange(street_name) %>% 
  knitr::kable()

street_dir	street_name	street_type_cd	street_sufx_cd	res_city_desc
NA	BROOKFIELD RETIREMENT CENTER	NA	NA	LILLINGTON
NA	CROSS MEMORIAL BAPTIST CHURCH	LOOP	NA	MARION
NA	KINGSDALE MANOR NURSING CENTER	NA	NA	LUMBERTON
NA	LOMBARDY VILLAGE MOBILE HOME	PARK	NA	LUMBERTON
NA	LOMBARDY VILLAGE MOBILE HOME	PARK	NA	SHANNON
NA	LOMBARDY VILLAGE MOBILE HOME	PARK	NA	REX
NA	NC HWY 197N NEAR COVY ROCK CHU	NA	NA	BURNSVILLE
NA	OLD TAMMANY-OINE ROOKER DAIRY	RD	NA	NORLINA
NA	SWEPSONVILLE-METHODIST CHURCH	RD	NA	GRAHAM
NA	WHEELING VILLAGE TRAILER PARK	NA	NA	WINSTON SALEM
NA	WILDLIFE RECREATION AREA ACC	RD	NA	LEXINGTON
NA	WISE-FIVE FORKS- ROBERSON FERR	NA	NA	MACON

• Long street names are multi-word phrases

  • Some appear to have been truncated

3.3 street_type_cd

street_type_cd Residential address street type (RD, ST, DR, BLVD, etc.)

table(d$street_type_cd, useNA = "ifany")

    ALY     ANX     ARC     AVE     BCH     BLF    BLVD     BND      BR     BRG 
    266      23       7  207808       1      47   27022     250     166       7 
    BRK     BTM     BYP     CIR     CLB     CMN    CMNS     COR    COVE    CRES 
     56       1    1205   99489      18      28      20      44       3     534 
    CRK    CRSE    CRST    CSWY      CT     CTR      CV      DL      DM    DORM 
    141       8      12      22  249682      15    2527       4       1    1756 
     DR     EST    ESTS    EXPY     EXT    FALL    FLDS     FLS     FLT    FLTS 
 926884     457     203      18    4931       2      64      19       8       4 
    FRD     FRK    FRST      FT     FWY     GDN    GDNS     GLN     GRN     GRV 
      9      13      22       1       4       6     110      61       6      94 
    HBR      HL     HLS    HOLW     HTS     HVN     HWY      IS     JCT     KNL 
     38     513     261     200    1684      24   44189      19       6     162 
   KNLS    LAND     LDG      LK     LKS      LN    LNDG    LOOP     MDW    MDWS 
     51       2       6      51      16  347932     371    9505       2      37 
   MEWS     MHP     MNR     MTN    ORCH    OVAL    PARK    PASS    PATH    PKWY 
    102       2     222      78       7       2    2013     171    1035    6665 
     PL     PLZ    PNES    PSGE      PT    RAMP      rd      RD     RDG     ROW 
  82253     211      21      21    2263       1       1 1287597    2124     333 
    RST     RTE     RUN     SHR    SHRS     SMT     SPG    SPGS    SPUR      SQ 
      2    2731    5219       3      33      11       6      32      58    1169 
     ST     STA     TER    TPKE    TRAK    TRCE     TRL    TRLR     VIA     VIS 
 512983       9    7959      13      10    1319   44565      26       2      60 
     VL     VLG     VLY      VW    WALK     WAY    WYND    XING    XRDS    <NA> 
      1     991      67     217     581   51625     328     652     163  154594 

3.4 street_sufx_cd

street_sufx_cd Residential address street suffix (BUS, EXT, and directional)

table(d$street_sufx_cd, useNA = "ifany")

    BUS       E     EXT       I       N      NE      NW       S      SE      SW 
    169   13021    1481       1   17869   24755   29472   18870   13095   26587 
      W    <NA> 
  13375 3941004 

4 Locality

res_city_desc Residential address city name
state_cd Residential address state code
zip_code Residential address zip code

d %>% 
  dplyr::select(res_city_desc : zip_code) %>% 
  skimr::skim()

Table 4.1: Data summary

Name	Piped data
Number of rows	4099699
Number of columns	3
_______________________
Column type frequency:
character	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
res_city_desc	19	1	3	20	0	783	0
state_cd	18	1	2	2	0	5	0
zip_code	21	1	5	9	0	902	0

• res_city_desc ~100% filled (19 missing)
• state_cd ~100% filled (19 missing)
• zip_code ~100% filled (19 missing)

Look at the addresses with any missing locality variable.

d %>% 
  dplyr::filter(is.na(res_city_desc) | is.na(state_cd) | is.na(zip_code)) %>% 
  dplyr::select(house_num : zip_code) %>%
  dplyr::distinct(.keep_all = TRUE) %>%
  dplyr::arrange(state_cd, res_city_desc, zip_code) %>% 
  knitr::kable()

house_num	half_code	street_dir	street_name	street_type_cd	street_sufx_cd	res_city_desc	state_cd	zip_code
5189	NA	NA	COVE	RD	NA	MARION	NC	NA
5030	NA	NA	COVE	RD	NA	MARION	NC	NA
0	NA	NA	H & N MOBILE HOME	PARK	NA	NA	NC	NA
0	NA	NA	UNKNOWN	NA	NA	NA	NC	NA
1407	NA	NA	OVERLOOK	DR	NA	LENOIR	NA	28645
0	NA	NA	CONFIDENTIAL	NA	NA	NA	NA	NA
0	NA	NA	NA	NA	NA	NA	NA	NA

• Some appear to be good addresses, apart from a missing zip code
• One appears to be a good address, apart from a missing state code
• Some are CONFIDENTIAL addresses, with all details missing
• Some appear to be completely missing the address (homeless persons?)

4.1 state_cd

state_cd Residential address state code

table(d$state_cd, useNA = "ifany")

     GA      NC      SC      TN      VA    <NA> 
     13 4099631       1      29       7      18 

• Residential state codes are almost entirely Georgia

  • There are a small number in neighbouring states (non-resident voters?)

4.2 zip_code

zip_code Residential address zip code

The zip codes are not all the same length. Look at the distribution of length of zip code

table(stringr::str_length(d$zip_code), useNA = "ifany")

      5       9    <NA> 
4099667      11      21 

Look at the 9-digit zip codes.

d %>% 
  dplyr::filter(stringr::str_length(zip_code) == 9) %>% 
  dplyr::select(street_name : zip_code) %>%
  dplyr::arrange(zip_code) %>% 
  knitr::kable()

street_name	street_type_cd	street_sufx_cd	res_city_desc	state_cd	zip_code
ECHO	LN	NA	SANFORD	NC	273308492
UNKNOWN	NA	NA	LILLINGTON	NC	275468949
LINCOLN MCKAY	DR	NA	LILLINGTON	NC	275469001
SMITH	ST	NA	ALBEMARLE	NC	280014351
INDIAN MOUND	RD	NA	ALBEMARLE	NC	280019245
POND	ST	NA	ALBEMARLE	NC	280019766
NC 731 HWY	NA	NA	NORWOOD	NC	281289420
FRIENDLY MCLEOD	LN	NA	DUNN	NC	283349250
BEAVER DAM	RD	NA	ERWIN	NC	283399790
WIRE	RD	NA	LINDEN	NC	283569413
WALKER	RD	NA	LINDEN	NC	283569416

• 5-digit and 9-digit zip codes are valid

Timing

Computation time (excl. render): 29.517 sec elapsed

Session information

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