Last updated: 2021-03-28
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# NOTE this notebook can be run manually or automatically by {targets}
# So load the packages required by this notebook here
# rather than relying on _targets.R to load them.
# Set up the project environment, because {workflowr} knits each Rmd file
# in a new R session, and doesn't execute the project .Rprofile
library(targets) # access data from the targets cache
library(tictoc) # capture execution time
library(here) # construct file paths relative to project root
library(fs) # file system operations
library(vroom) # fast reading of delimited text files
library(tibble) # enhanced data frames
library(stringr) # string matching
# start the execution time clock
tictoc::tic("Computation time (excl. render)")
# Get the path to the raw entity data file
# This is a target managed by {targets}
f_entity_raw_tsv <- tar_read(c_raw_entity_data_file)The aim of this set of meta notebooks is to work out how to read the raw
entity data. and get it sufficiently neatened so that we can construct
standardised names and modelling features without needing any further
neatening. To be clear, the target (c_raw_entity_data) corresponding
to the objective of this set of notebooks is the neatened raw data,
before constructing any modelling features.
This notebook documents the process of excluding data rows that are not useful for this project.
The subsequent notebooks in this set will check that all the columns have been read correctly and work out how to fix them, if necessary.
There are four variables dealing with voter status Preliminary examination of these variables shows that some records correspond to people who have been removed from the electoral roll. This project focuses on ambiguity arising from the fact that some names are common Therefore, we want the entity data to be as accurate as possible and free of duplicate records so that we don’t introduce ambiguity because of data quality issues.
Speaking of data quality issues, it is my experience that large databases often contain test records.
I will exclude all the data rows that have any of these data quality issues. I will do this early in the pipeline to minimise the number of records processed and to avoid including these records in the subsequent quality analyses.
Read the raw entity data file using the previously defined function,
raw_entity_data_read().
# Show the data file name
fs::path_file(f_entity_raw_tsv)[1] "VR_20051125.txt.xz"d <- raw_entity_data_read(f_entity_raw_tsv)Check the internal consistency of the voter status variables.
The data dictionary describes these as two pairs of variables, where each pair consists of a code variable and a label variable. I would expect the code and label values to be in a 1:1 relationship.
d %>%
dplyr::distinct(status_cd, voter_status_desc) %>%
dplyr::arrange(status_cd, voter_status_desc) %>%
knitr::kable()| status_cd | voter_status_desc |
|---|---|
| A | ACTIVE |
| D | DENIED |
| I | INACTIVE |
| R | REMOVED |
| S | TEMPORARY REGISTRATION |
| NA | NA |
voter_status_desc) because it is more meaningful.Look at the distribution of values across the data.
table(Label = d$voter_status_desc, useNA = "always") %>% knitr::kable()| Label | Freq |
|---|---|
| ACTIVE | 4914521 |
| DENIED | 41348 |
| INACTIVE | 495603 |
| REMOVED | 2546485 |
| TEMPORARY REGISTRATION | 5334 |
| NA | 2 |
d %>%
dplyr::distinct(reason_cd, voter_status_reason_desc) %>%
dplyr::arrange(reason_cd, voter_status_reason_desc) %>%
knitr::kable()| reason_cd | voter_status_reason_desc |
|---|---|
| A1 | UNVERIFIED |
| A2 | CONFIRMATION PENDING |
| AA | ARMED FORCES |
| AL | LEGACY DATA |
| AN | UNVERIFIED NEW |
| AP | VERIFICATION PENDING |
| AV | VERIFIED |
| DI | UNAVAILABLE ESSENTIAL INFORMATION |
| DU | VERIFICATION RETURNED UNDELIVERABLE |
| IL | LEGACY - CONVERSION |
| IN | CONFIRMATION NOT RETURNED |
| IU | CONFIRMATION RETURNED UNDELIVERABLE |
| R2 | DUPLICATE |
| RA | ADMINISTRATIVE |
| RC | REMOVED DUE TO SUSTAINED CHALLENGE |
| RD | DECEASED |
| RF | FELONY CONVICTION |
| RL | MOVED FROM COUNTY |
| RM | REMOVED AFTER 2 FED GENERAL ELECTIONS IN INACTIVE STATUS |
| RP | REMOVED UNDER OLD PURGE LAW |
| RQ | REQUEST FROM VOTER |
| RS | MOVED FROM STATE |
| RT | TEMPORARY REGISTRANT |
| SM | MILITARY |
| SO | OVERSEAS CITIZEN |
| SP | PREVIOUSLY REGISTERED |
| NA | NA |
voter_status_reason_desc) because it is more meaningful.Look at the distribution of values across the data.
table(Label = d$voter_status_reason_desc, useNA = "always") %>% knitr::kable()| Label | Freq |
|---|---|
| ADMINISTRATIVE | 59008 |
| ARMED FORCES | 50 |
| CONFIRMATION NOT RETURNED | 181320 |
| CONFIRMATION PENDING | 71296 |
| CONFIRMATION RETURNED UNDELIVERABLE | 303197 |
| DECEASED | 443486 |
| DUPLICATE | 78951 |
| FELONY CONVICTION | 63501 |
| LEGACY - CONVERSION | 10585 |
| LEGACY DATA | 523899 |
| MILITARY | 3975 |
| MOVED FROM COUNTY | 888056 |
| MOVED FROM STATE | 89049 |
| OVERSEAS CITIZEN | 1307 |
| PREVIOUSLY REGISTERED | 51 |
| REMOVED AFTER 2 FED GENERAL ELECTIONS IN INACTIVE STATUS | 551073 |
| REMOVED DUE TO SUSTAINED CHALLENGE | 662 |
| REMOVED UNDER OLD PURGE LAW | 367511 |
| REQUEST FROM VOTER | 4194 |
| TEMPORARY REGISTRANT | 729 |
| UNAVAILABLE ESSENTIAL INFORMATION | 6991 |
| UNVERIFIED | 13737 |
| UNVERIFIED NEW | 7517 |
| VERIFICATION PENDING | 198333 |
| VERIFICATION RETURNED UNDELIVERABLE | 34357 |
| VERIFIED | 4100220 |
| NA | 238 |
Look at the combinations of the two variables.
d %>%
with(table(voter_status_reason_desc, voter_status_desc, useNA = "always")) %>%
knitr::kable()| ACTIVE | DENIED | INACTIVE | REMOVED | TEMPORARY REGISTRATION | NA | |
|---|---|---|---|---|---|---|
| ADMINISTRATIVE | 0 | 0 | 0 | 59008 | 0 | 0 |
| ARMED FORCES | 50 | 0 | 0 | 0 | 0 | 0 |
| CONFIRMATION NOT RETURNED | 0 | 0 | 181320 | 0 | 0 | 0 |
| CONFIRMATION PENDING | 71295 | 0 | 0 | 1 | 0 | 0 |
| CONFIRMATION RETURNED UNDELIVERABLE | 0 | 0 | 303197 | 0 | 0 | 0 |
| DECEASED | 0 | 0 | 0 | 443486 | 0 | 0 |
| DUPLICATE | 0 | 0 | 0 | 78951 | 0 | 0 |
| FELONY CONVICTION | 0 | 0 | 0 | 63501 | 0 | 0 |
| LEGACY - CONVERSION | 1 | 0 | 10584 | 0 | 0 | 0 |
| LEGACY DATA | 523897 | 0 | 2 | 0 | 0 | 0 |
| MILITARY | 0 | 0 | 0 | 0 | 3975 | 0 |
| MOVED FROM COUNTY | 0 | 0 | 0 | 888055 | 0 | 1 |
| MOVED FROM STATE | 0 | 0 | 0 | 89049 | 0 | 0 |
| OVERSEAS CITIZEN | 0 | 0 | 0 | 0 | 1307 | 0 |
| PREVIOUSLY REGISTERED | 0 | 0 | 0 | 1 | 50 | 0 |
| REMOVED AFTER 2 FED GENERAL ELECTIONS IN INACTIVE STATUS | 0 | 0 | 0 | 551072 | 0 | 1 |
| REMOVED DUE TO SUSTAINED CHALLENGE | 0 | 0 | 0 | 662 | 0 | 0 |
| REMOVED UNDER OLD PURGE LAW | 0 | 0 | 0 | 367511 | 0 | 0 |
| REQUEST FROM VOTER | 0 | 0 | 0 | 4194 | 0 | 0 |
| TEMPORARY REGISTRANT | 0 | 0 | 0 | 729 | 0 | 0 |
| UNAVAILABLE ESSENTIAL INFORMATION | 0 | 6990 | 0 | 1 | 0 | 0 |
| UNVERIFIED | 13731 | 0 | 0 | 4 | 2 | 0 |
| UNVERIFIED NEW | 7516 | 0 | 0 | 1 | 0 | 0 |
| VERIFICATION PENDING | 198331 | 0 | 1 | 1 | 0 | 0 |
| VERIFICATION RETURNED UNDELIVERABLE | 0 | 34357 | 0 | 0 | 0 | 0 |
| VERIFIED | 4099700 | 1 | 499 | 20 | 0 | 0 |
| NA | 0 | 0 | 0 | 238 | 0 | 0 |
On a common-sense interpretation of the labels, these are the records that have survived the registration checking process, so are most likely free of errors and duplicates.
Write a function to filter the records to keep only those that are “ACTIVE” and “VERIFIED”.
# Function to exclude records based on voter status
raw_entity_data_excl_status <- function(
d # data frame - raw entity data
) {
d %>%
dplyr::filter(
voter_status_desc == "ACTIVE" & voter_status_reason_desc == "VERIFIED"
)
}Apply the filter before moving on to the next exclusion condition and track the number of rows before and after the filter.
# number of rows before filtering
nrow(d)[1] 8003293d <- d %>% raw_entity_data_excl_status()
# number of rows before filtering
nrow(d)[1] 4099700Look for any records that contain the “test” as a word (i.e. enclosed by word boundaries).
d %>%
dplyr::select(ends_with("_name"), res_city_desc, area_cd, phone_num) %>%
dplyr::filter(
stringr::str_detect(last_name, regex("\\btest\\b", ignore_case = TRUE)) |
stringr::str_detect(first_name, regex("\\btest\\b", ignore_case = TRUE)) |
stringr::str_detect(midl_name, regex("\\btest\\b", ignore_case = TRUE)) |
stringr::str_detect(street_name, regex("\\btest\\b", ignore_case = TRUE))
) %>%
knitr::kable()| last_name | first_name | midl_name | street_name | res_city_desc | area_cd | phone_num |
|---|---|---|---|---|---|---|
| TEAGUE | NORMA | JEAN | TEST FARM | WAYNESVILLE | NA | NA |
| TEST | KAY | ANN | ELM | WELDON | NA | NA |
| TEST | FREDERICK | HAROLD | HENDERSONVILLE | ASHEVILLE | NA | NA |
| PHILLIPS | DEBORAH | MOORE | TEST FARM | WALLACE | 910 | 2859781 |
| PHILLIPS | MICHAEL | DOWNING | TEST FARM | WALLACE | 910 | 2859781 |
| SOLES | SHANNON | ACACIA | TEST FARM | WALLACE | 910 | 2852575 |
| TEST | DAVID | W | GREENWAY | CHARLOTTE | NA | NA |
| TEST | DANIEL | W | HICKORY KNOLL | GREENSBORO | NA | NA |
| TEST | GEORGE | A | ROSWELL | CHARLOTTE | NA | NA |
| TEST | DORIS | L | ROSWELL | CHARLOTTE | NA | NA |
| TEST | KATHERINE | COOKE | GREENWAY | CHARLOTTE | NA | NA |
| TEST | KENNETH | FARREL | WAVERLY | CLAYTON | NA | NA |
| TEST | THIS | NA | HIGH POINT | JAMESTOWN | NA | NA |
Write a function to filter the records to exclude those with name “TEST, This”.
There are likely to be many other test cases not detected by this filter. However, they are likely to be only a tiny fraction of the records - so it’s not a big problem if they are missed. If I find any other test cases later I will come back here and revise the exclusion criteria.
# Function to exclude test records based on names
raw_entity_data_excl_test <- function(
d # data frame - raw entity data
) {
d %>%
dplyr::filter(
! (
stringr::str_detect(last_name, regex("\\bTEST\\b", ignore_case = TRUE)) &
stringr::str_detect(first_name, regex("\\bTHIS\\b", ignore_case = TRUE))
)
)
}Apply the filter and track the number of rows before and after the filter.
# number of rows before filtering
nrow(d)[1] 4099700d <- d %>% raw_entity_data_excl_test()
# number of rows before filtering
nrow(d)[1] 4099699Computation time (excl. render): 65.754 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] stringr_1.4.0 tibble_3.1.0 vroom_1.4.0 fs_1.5.0
[5] tictoc_1.0 here_1.0.1 workflowr_1.6.2 targets_0.3.0
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.22 bslib_0.2.4 purrr_0.3.4
[5] vctrs_0.3.6 generics_0.1.0 htmltools_0.5.1.1 yaml_2.2.1
[9] utf8_1.2.1 rlang_0.4.10 later_1.1.0.1 pillar_1.5.1
[13] jquerylib_0.1.3 DBI_1.1.1 glue_1.4.2 withr_2.4.1
[17] bit64_4.0.5 lifecycle_1.0.0 codetools_0.2-18 evaluate_0.14
[21] knitr_1.31 callr_3.5.1 httpuv_1.5.5 ps_1.6.0
[25] parallel_4.0.3 fansi_0.4.2 highr_0.8 Rcpp_1.0.6
[29] renv_0.13.1 promises_1.2.0.1 jsonlite_1.7.2 bit_4.0.4
[33] digest_0.6.27 stringi_1.5.3 bookdown_0.21 processx_3.5.0
[37] dplyr_1.0.5 rprojroot_2.0.2 cli_2.3.1 tools_4.0.3
[41] magrittr_2.0.1 sass_0.3.1 crayon_1.4.1 pkgconfig_2.0.3
[45] ellipsis_0.3.1 data.table_1.14.0 assertthat_0.2.1 rmarkdown_2.7
[49] R6_2.5.0 igraph_1.2.6 compiler_4.0.3 git2r_0.28.0