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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(lubridate) # date parsing
Attaching package: 'lubridate'The following objects are masked from 'package:base':
date, intersect, setdiff, union# 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 parsing the dates from character strings. This is necessary because the subsequent analyses need dates rather than strings that look like dates.
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, drop the excluded rows, and drop the
variables with no variation using the previously defined core pipeline
functions, raw_entity_data_read(), raw_entity_data_excl_status(),
raw_entity_data_excl_test(), and raw_entity_data_drop_novar().
# 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() %>%
raw_entity_data_drop_novar()
dim(d)[1] 4099699 25Show some values for all the date columns.
d %>%
dplyr::select(ends_with("_dt")) %>%
dplyr::group_by(is.na(cancellation_dt)) %>%
dplyr::slice_sample(n = 10) %>%
dplyr::ungroup() %>%
dplyr::select(-starts_with("is.na")) %>%
gt::gt() %>%
gt::opt_row_striping() %>%
gt::tab_style(style = gt::cell_text(weight = "bold"), locations = gt::cells_column_labels()) %>%
gt::fmt_missing(columns = everything(), missing_text = "<NA>")| registr_dt | cancellation_dt |
|---|---|
| 1982-09-24 00:00:00 | 1996-12-20 00:00:00 |
| 1997-01-16 00:00:00 | 1997-01-29 00:00:00 |
| 1997-01-22 00:00:00 | 1997-01-23 00:00:00 |
| 1988-10-03 00:00:00 | 1997-01-16 00:00:00 |
| 1996-09-26 00:00:00 | 1997-01-13 00:00:00 |
| 1996-12-30 00:00:00 | 1997-01-09 00:00:00 |
| 1996-09-20 00:00:00 | 1997-01-08 00:00:00 |
| 1996-10-10 00:00:00 | 1997-01-27 00:00:00 |
| 1983-10-10 00:00:00 | 1996-08-06 00:00:00 |
| 1996-05-06 00:00:00 | 1997-02-20 00:00:00 |
| 2002-10-08 17:08:00 | <NA> |
| 1998-07-29 00:00:00 | <NA> |
| 2004-12-28 00:00:00 | <NA> |
| 1984-08-29 00:00:00 | <NA> |
| 2004-02-24 00:00:00 | <NA> |
| 1966-10-12 00:00:00 | <NA> |
| 1984-10-08 00:00:00 | <NA> |
| 2004-05-19 00:00:00 | <NA> |
| 2004-07-26 00:00:00 | <NA> |
| 1956-01-01 00:00:00 | <NA> |
Write a function to parse the date columns. We only need the date component of each date-time.
# Function to parse the date strings in the raw entity data
raw_entity_data_parse_dates <- function(
d # data frame - raw entity data
) {
d %>%
dplyr::mutate( # convert the datetime cols to dates
registr_dt = lubridate::as_date(registr_dt),
cancellation_dt = lubridate::as_date(cancellation_dt)
)
}Test to see if all the dates are parsed and that missing strings map to missing dates.
# get just the character date columns
d_char <- d %>%
dplyr::select(ends_with("_dt"))
# parse the date columns
d_date <- d_char %>%
raw_entity_data_parse_dates()
# check that the missing values are identically located in both frames
all( is.na(d_char) == is.na(d_date) )[1] TRUEAll missing values are identically located in both frames, so:
d_char and missing in d_date)Computation time (excl. render): 84.634 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] lubridate_1.7.10 vroom_1.4.0 stringr_1.4.0 gt_0.3.0
[5] dplyr_1.0.6 fs_1.5.0 here_1.0.1 tictoc_1.0.1
[9] targets_0.4.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.23 bslib_0.2.5 purrr_0.3.4
[5] colorspace_2.0-1 vctrs_0.3.8 generics_0.1.0 htmltools_0.5.1.1
[9] yaml_2.2.1 utf8_1.2.1 rlang_0.4.11 jquerylib_0.1.4
[13] later_1.2.0 pillar_1.6.1 glue_1.4.2 withr_2.4.2
[17] bit64_4.0.5 lifecycle_1.0.0 munsell_0.5.0 gtable_0.3.0
[21] workflowr_1.6.2 codetools_0.2-18 evaluate_0.14 knitr_1.33
[25] callr_3.7.0 httpuv_1.6.1 ps_1.6.0 parallel_4.1.0
[29] fansi_0.4.2 Rcpp_1.0.6 backports_1.2.1 checkmate_2.0.0
[33] renv_0.13.2 promises_1.2.0.1 scales_1.1.1 jsonlite_1.7.2
[37] bit_4.0.4 ggplot2_3.3.3 digest_0.6.27 stringi_1.6.2
[41] bookdown_0.22 processx_3.5.2 rprojroot_2.0.2 grid_4.1.0
[45] cli_2.5.0 tools_4.1.0 magrittr_2.0.1 sass_0.4.0
[49] tibble_3.1.2 crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[53] ellipsis_0.3.2 data.table_1.14.0 rmarkdown_2.8 R6_2.5.0
[57] igraph_1.2.6 git2r_0.28.0 compiler_4.1.0