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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 roothere() starts at /home/ross/RG/projects/academic/entity_resolution/fa_sim_cal_TOP/fa_sim_callibrary(fs) # file system operations
library(dplyr) # data wrangling
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(gt) # table formatting
library(stringr) # string matching
library(vroom) # fast reading of delimited text files
library(skimr) # compact summary of each variable
# 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)These meta notebooks document the development of functions that will be applied in the core pipeline.
The aim of the m_01 set of meta notebooks is to work out how to read the
raw entity data, drop excluded cases, discard irrelevant variables,
apply any cleaning, and construct standardised names. This does not
include construction of any modelling features. To be clear, the target
(c_raw_entity_data) corresponding to the objective of this set of
notebooks is the cleaned and standardised raw data, before constructing
any modelling features.
This notebook documents the process of dropping variables with no variation in values, because they are uninformative.
The subsequent notebooks in this set will develop the other functions needed to generate the cleaned and standardised data.
Read the raw entity data file and drop the excluded rows using the
previously defined core pipeline functions, raw_entity_data_read(),
raw_entity_data_excl_status(), and raw_entity_data_excl_test.
# 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) %>%
raw_entity_data_excl_status() %>%
raw_entity_data_excl_test()
dim(d)[1] 4099699 32Take a quick look at the distributions of all the variables.
skimr::skim(d)| Name | d |
| Number of rows | 4099699 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| character | 32 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| snapshot_dt | 0 | 1.00 | 19 | 19 | 0 | 1 | 0 |
| county_id | 0 | 1.00 | 1 | 3 | 0 | 100 | 0 |
| county_desc | 0 | 1.00 | 3 | 12 | 0 | 100 | 0 |
| voter_reg_num | 0 | 1.00 | 12 | 12 | 0 | 1786064 | 0 |
| ncid | 4099699 | 0.00 | NA | NA | 0 | 0 | 0 |
| status_cd | 0 | 1.00 | 1 | 1 | 0 | 1 | 0 |
| voter_status_desc | 0 | 1.00 | 6 | 6 | 0 | 1 | 0 |
| reason_cd | 0 | 1.00 | 2 | 2 | 0 | 1 | 0 |
| voter_status_reason_desc | 0 | 1.00 | 8 | 8 | 0 | 1 | 0 |
| last_name | 0 | 1.00 | 1 | 21 | 0 | 191996 | 0 |
| first_name | 23 | 1.00 | 1 | 19 | 0 | 126589 | 0 |
| midl_name | 252695 | 0.94 | 1 | 20 | 0 | 175742 | 0 |
| name_sufx_cd | 3869063 | 0.06 | 1 | 3 | 0 | 101 | 0 |
| house_num | 0 | 1.00 | 1 | 6 | 0 | 27534 | 0 |
| half_code | 4088996 | 0.00 | 1 | 1 | 0 | 41 | 0 |
| 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 |
| unit_num | 3755239 | 0.08 | 1 | 7 | 0 | 16116 | 0 |
| res_city_desc | 19 | 1.00 | 3 | 20 | 0 | 783 | 0 |
| state_cd | 18 | 1.00 | 2 | 2 | 0 | 5 | 0 |
| zip_code | 21 | 1.00 | 5 | 9 | 0 | 902 | 0 |
| area_cd | 2628117 | 0.36 | 1 | 3 | 0 | 507 | 0 |
| phone_num | 2540990 | 0.38 | 1 | 7 | 0 | 1072592 | 0 |
| sex_code | 0 | 1.00 | 1 | 1 | 0 | 3 | 0 |
| sex | 0 | 1.00 | 3 | 6 | 0 | 3 | 0 |
| age | 0 | 1.00 | 1 | 3 | 0 | 135 | 0 |
| birth_place | 718647 | 0.82 | 2 | 2 | 0 | 56 | 0 |
| registr_dt | 0 | 1.00 | 19 | 29 | 0 | 60788 | 0 |
| cancellation_dt | 4095558 | 0.00 | 19 | 19 | 0 | 248 | 0 |
| load_dt | 0 | 1.00 | 29 | 29 | 0 | 1 | 0 |
The most useful column to look at in the skim tables is n_unique. This
shows the number of unique values of the variable.
The following variable is entirely missing values:
ncid - North Carolina identification number (NCID) of voterThe following variables have exactly one unique nonmissing value:
snapshot_dt - Date of snapshotload_dt - Data load dateThe following variables have exactly one unique nonmissing value because of selecting ACTIVE & VERIFIED records:
status_cd Status code for voter registrationvoter_status_desc Status code descriptionreason_cd Reason code for voter registration statusvoter_status_reason_desc Reason code descriptionThose seven variables can not possibly be useful for analyses. Write a function to drop them from the data.
# Function to drop variables with no variation
raw_entity_data_drop_novar <- function(
d # data frame - raw entity data
) {
d %>%
dplyr::select(
-c(ncid, snapshot_dt, load_dt,
status_cd, voter_status_desc, reason_cd, voter_status_reason_desc)
)
}Apply the filter and track the number of rows before and after the filter.
# number of columns before dropping
d %>% names() %>% length[1] 32d %>%
raw_entity_data_drop_novar() %>%
# number of columns after dropping
names() %>% length[1] 25Computation time (excl. render): 823.018 sec elapsed
sessionInfo()R version 4.1.0 (2021-05-18)
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] skimr_2.1.3 vroom_1.4.0 stringr_1.4.0 gt_0.3.0 dplyr_1.0.6
[6] fs_1.5.0 here_1.0.1 tictoc_1.0.1 targets_0.4.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.23 bslib_0.2.5 repr_1.1.3
[5] purrr_0.3.4 colorspace_2.0-1 vctrs_0.3.8 generics_0.1.0
[9] htmltools_0.5.1.1 base64enc_0.1-3 yaml_2.2.1 utf8_1.2.1
[13] rlang_0.4.11 jquerylib_0.1.4 later_1.2.0 pillar_1.6.1
[17] glue_1.4.2 withr_2.4.2 bit64_4.0.5 lifecycle_1.0.0
[21] munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2 codetools_0.2-18
[25] evaluate_0.14 knitr_1.33 callr_3.7.0 httpuv_1.6.1
[29] ps_1.6.0 parallel_4.1.0 fansi_0.4.2 highr_0.9
[33] Rcpp_1.0.6 renv_0.13.2 promises_1.2.0.1 scales_1.1.1
[37] jsonlite_1.7.2 bit_4.0.4 ggplot2_3.3.3 digest_0.6.27
[41] stringi_1.6.2 bookdown_0.22 processx_3.5.2 rprojroot_2.0.2
[45] grid_4.1.0 cli_2.5.0 tools_4.1.0 magrittr_2.0.1
[49] sass_0.4.0 tibble_3.1.2 tidyr_1.1.3 crayon_1.4.1
[53] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.2 data.table_1.14.0
[57] rmarkdown_2.8 R6_2.5.0 igraph_1.2.6 git2r_0.28.0
[61] compiler_4.1.0