Last updated: 2021-01-15
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | c674a51 | Ross Gayler | 2021-01-15 | Add 01-6 clean vars |
html | 44538d8 | Ross Gayler | 2021-01-12 | Build site. |
html | 8cd5fa1 | Ross Gayler | 2021-01-12 | Build site. |
Rmd | 7a1ce01 | Ross Gayler | 2021-01-12 | wflow_publish(c(“analysis/index.Rmd”, "analysis/01-4*.Rmd")) |
html | abb201f | Ross Gayler | 2021-01-12 | Build site. |
Rmd | 2ae8660 | Ross Gayler | 2021-01-12 | Add 01-4 check demog |
# 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)")
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 (01-4_check_demog
) characterises the demographic
variables in the saved subset of the data. These are the non-name
variables that are reasonably interpretable as properties of the person.
We will probably use some of these variables as predictors in a compatibility model and/or as blocking variables.
Define the demographic variables.
vars_resid <- c(
"sex_code", "sex", "age", "birth_place"
)
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
fs::path_file(f_entity_raw_fst)
[1] "ent_raw.fst"
# get data for next section of analyses
d <- fst::read_fst(
f_entity_raw_fst,
columns = vars_resid
) %>%
tibble::as_tibble()
dim(d)
[1] 4099699 4
Take a quick look at the distributions.
d %>% skimr::skim()
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 |
---|---|---|---|---|---|---|---|
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 |
sex_code
100% filledsex
100% filledage
100% filledbirth_place
82% filledsex_code
Gender code
sex
Gender description
d %>%
with(table(sex_code, sex, useNA = "ifany"))
sex
sex_code FEMALE MALE UNK
F 2239888 0 0
M 0 1844220 0
U 0 0 15591
sex_code
and sex
in 1-1 relationshipbirth_place
Birth place
table(d$birth_place, useNA = "ifany") %>% sort() %>% rev()
NC <NA> NY VA SC PA OC OH FL NJ
1875088 718647 189726 131356 97827 93496 76209 75765 69032 67588
GA CA MI IL WV TX TN MD MA IN
56176 50560 48081 47613 42143 37852 36797 36369 33519 26719
KY AL DC CT MO WI LA CO MS IA
24276 23877 22563 22394 16066 15663 15362 12803 12047 10891
MN OK WA KS AR ME RI NE DE AZ
10388 9609 9083 8656 6614 6284 6039 5592 5373 5043
NH HI VT OR NM AK UT PR ND SD
4880 3870 3783 3764 3435 3201 3088 2591 2399 2240
ID MT NV WY VI GU AS
2003 1901 1542 1280 355 149 32
birth_place
values appear to be 2-character US state abbreviationsage
Age (years)
I presume that the source documents actually record date of birth rather than age, and that age is reported in these files as a gesture to privacy.
Look at the distribution of age.
x <- d %>%
dplyr::mutate(age = as.integer(age))
x$age %>% summary()
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 33.00 45.00 46.93 58.00 221.00
x$age %>% quantile(probs = c(0.003, 0.004, 0.995, 0.996, 0.997, 0.998, 0.999))
0.3% 0.4% 99.5% 99.6% 99.7% 99.8% 99.9%
0 18 98 105 105 105 204
x %>%
# dplyr::filter(age >= 80) %>%
ggplot() +
geom_vline(xintercept = c(17, 105, 125, 204), colour = "orange") +
geom_histogram(aes(x = age), binwidth = 1) +
scale_y_log10()
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Removed 87 rows containing missing values (geom_bar).
Version | Author | Date |
---|---|---|
abb201f | Ross Gayler | 2021-01-12 |
Computation time (excl. render): 12.083 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] hexbin_1.28.2 glue_1.4.2 knitr_1.30 skimr_2.1.2
[5] fst_0.9.4 fs_1.5.0 forcats_0.5.0 stringr_1.4.0
[9] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[13] tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0 tictoc_1.0
[17] here_1.0.1 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 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 repr_1.1.0 R6_2.5.0
[9] cellranger_1.1.0 backports_1.2.1 reprex_0.3.0 evaluate_0.14
[13] highr_0.8 httr_1.4.2 pillar_1.4.7 rlang_0.4.10
[17] readxl_1.3.1 rstudioapi_0.13 whisker_0.4 rmarkdown_2.6
[21] labeling_0.4.2 munsell_0.5.0 broom_0.7.3 compiler_4.0.3
[25] httpuv_1.5.4 modelr_0.1.8 xfun_0.20 base64enc_0.1-3
[29] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0 bookdown_0.21
[33] fansi_0.4.1 crayon_1.3.4 dbplyr_2.0.0 withr_2.3.0
[37] later_1.1.0.1 grid_4.0.3 jsonlite_1.7.2 gtable_0.3.0
[41] lifecycle_0.2.0 DBI_1.1.0 git2r_0.28.0 magrittr_2.0.1
[45] scales_1.1.1 cli_2.2.0 stringi_1.5.3 farver_2.0.3
[49] renv_0.12.5 promises_1.1.1 xml2_1.3.2 ellipsis_0.3.1
[53] generics_0.1.0 vctrs_0.3.6 tools_4.0.3 hms_0.5.3
[57] parallel_4.0.3 yaml_2.2.1 colorspace_2.0-0 rvest_0.3.6
[61] haven_2.3.1