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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(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)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 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 check that all the columns
have been read correctly and work out how to fix them, if necessary. The
final notebook in this set works out how to save the neatened data
(c_raw_entity_data).
Read the raw entity data file using the previously defined function,
raw_entity_data_get().
# Show the data file name
fs::path_file(f_entity_raw_tsv)[1] "VR_20051125.txt.xz"d <- raw_entity_data_get(f_entity_raw_tsv)Show some values for all the date columns.
d %>%
dplyr::select(ends_with("_dt")) %>%
dplyr::slice_sample(n = 10) %>%
knitr::kable()| snapshot_dt | registr_dt | cancellation_dt | load_dt |
|---|---|---|---|
| 2005-11-25 00:00:00 | 1998-02-10 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 1995-04-07 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 2000-03-10 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 1980-10-01 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 1970-03-21 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 1988-10-07 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 1974-04-08 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 2003-08-15 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 1983-01-01 00:00:00 | 2000-09-06 00:00:00 | 2014-07-15 22:21:54.150000000 |
| 2005-11-25 00:00:00 | 2001-01-25 00:00:00 | NA | 2014-07-15 22:21:54.150000000 |
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
snapshot_dt = lubridate::as_date(snapshot_dt),
registr_dt = lubridate::as_date(registr_dt),
cancellation_dt = lubridate::as_date(cancellation_dt),
load_dt = lubridate::as_date(load_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] TRUEd_char and missing in d_date)Computation time (excl. render): 119.342 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] lubridate_1.7.10 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_0.2.0
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 stringr_1.4.0 codetools_0.2-18
[21] evaluate_0.14 knitr_1.31 callr_3.5.1 httpuv_1.5.5
[25] ps_1.6.0 parallel_4.0.3 fansi_0.4.2 highr_0.8
[29] Rcpp_1.0.6 renv_0.13.1 promises_1.2.0.1 jsonlite_1.7.2
[33] bit_4.0.4 digest_0.6.27 stringi_1.5.3 dplyr_1.0.5
[37] bookdown_0.21 processx_3.4.5 rprojroot_2.0.2 cli_2.3.1
[41] tools_4.0.3 magrittr_2.0.1 sass_0.3.1 crayon_1.4.1
[45] pkgconfig_2.0.3 ellipsis_0.3.1 data.table_1.14.0 assertthat_0.2.1
[49] rmarkdown_2.7 R6_2.5.0 igraph_1.2.6 compiler_4.0.3
[53] git2r_0.28.0