01-4_check_demog
Check demographic variables
Ross Gayler
2021-01-12
Last updated: 2021-01-12
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Knit directory:
fa_sim_cal/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 8cd5fa1 | Ross Gayler | 2021-01-12 | Build site. |
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| 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)")1 Introduction
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
f_entity_fst[1] "/home/ross/RG/projects/academic/entity_resolution/fa_sim_cal_TOP/fa_sim_cal/output/ent_raw.fst"# get data for next section of analyses
d <- fst::read_fst(
f_entity_fst,
columns = vars_resid
) %>%
tibble::as_tibble()
dim(d)[1] 4099699 4Take 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_code100% filledsex100% filledage100% filledbirth_place82% filled
2 sex_code & sex
sex_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 15591sex_codeandsexin 1-1 relationship
3 birth_place
birth_place Birth place
table(d$birth_place, useNA = "ifany") %>% sort() %>% rev()
NC <NA> NY VA SC PA OC OH FL NJ
1875088 718647 189726 131356 97827 93496 76209 75765 69032 67588
GA CA MI IL WV TX TN MD MA IN
56176 50560 48081 47613 42143 37852 36797 36369 33519 26719
KY AL DC CT MO WI LA CO MS IA
24276 23877 22563 22394 16066 15663 15362 12803 12047 10891
MN OK WA KS AR ME RI NE DE AZ
10388 9609 9083 8656 6614 6284 6039 5592 5373 5043
NH HI VT OR NM AK UT PR ND SD
4880 3870 3783 3764 3435 3201 3088 2591 2399 2240
ID MT NV WY VI GU AS
2003 1901 1542 1280 355 149 32 birth_placevalues appear to be 2-character US state abbreviations- OC might mean “other country”
- VI, GU, AS are probably Virgin Islands, Guam, American Samoa
4 age
age Age (years)
I presume that the source documents actually record date of birth rather than age, and that age is reported in these files as a gesture to privacy.
Look at the distribution of age.
x <- d %>%
dplyr::mutate(age = as.integer(age))
x$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-axisWarning: Removed 87 rows containing missing values (geom_bar).
| Version | Author | Date |
|---|---|---|
| abb201f | Ross Gayler | 2021-01-12 |
- Voters may be pre-registered at 17 years
- The ages less than 17 years (especially zero) are effectively missing
- The spikes at 105, 125, and 204 years must be from some special process which generates those specific ages.
- Any age greater than or equal to 105 years, seems very implausible.
Timing
Computation time (excl. render): 13.273 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 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[13] ggplot2_3.3.3 tidyverse_1.3.0 tictoc_1.0 here_1.0.1
[17] 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 fs_1.5.0 promises_1.1.1 xml2_1.3.2
[53] ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.6 tools_4.0.3
[57] hms_0.5.3 parallel_4.0.3 yaml_2.2.1 colorspace_2.0-0
[61] rvest_0.3.6 haven_2.3.1