Last updated: 2024-01-22
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Knit directory: PODFRIDGE/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | c280b6f | Tina Lasisi | 2023-04-16 | Build site. |
| html | f89a90f | Tina Lasisi | 2023-04-16 | Build site. |
| html | 5f805fe | Tina Lasisi | 2023-03-06 | Build site. |
| Rmd | 5106bab | Tina Lasisi | 2023-03-06 | add analyses |
| html | c3948af | Tina Lasisi | 2023-03-04 | Build site. |
| html | f02bc38 | Tina Lasisi | 2023-03-03 | Build site. |
| html | c9130d5 | Tina Lasisi | 2023-03-03 | wflow_git_commit(all = TRUE) |
| html | a4a7d45 | Tina Lasisi | 2023-03-03 | Build site. |
| html | 00073fd | Tina Lasisi | 2023-03-03 | Build site. |
| html | 51ed5a6 | Tina Lasisi | 2023-03-02 | Build site. |
| Rmd | 13ed9ae | Tina Lasisi | 2023-03-02 | Publishing POPFORGE |
| html | 13ed9ae | Tina Lasisi | 2023-03-02 | Publishing POPFORGE |
The demographic data that the analyses will rely on for now is based on a combination of Hacker et al 2020, US Census and Graham Coop’s inputs for Edge & Coop 2019.
This GitHub Repo contains an .xls file with multiple sheets that contain the different inputs (here). Below is the final file that was uploaded and used in the analyses:
library(readr)
est_pop_combo <- read_csv("data/est-pop-combo.csv")Rows: 39 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (4): Year, Total, White, Black
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.est_pop_combo# A tibble: 39 × 4
Year Total White Black
<dbl> <dbl> <dbl> <dbl>
1 1610 350 350 0
2 1620 2302 2282 20
3 1630 4646 4587 59
4 1640 26634 26049 585
5 1650 50368 48800 1568
6 1660 75058 72196 2862
7 1670 111935 107491 4444
8 1680 151507 144675 6832
9 1690 210372 193978 16394
10 1700 250888 223082 27806
# ℹ 29 more rowsWhat are realistic database sizes for US European- and African American populations? From 23andMe publications it seems that 80% of their customers identify as White (non-Hispanic) and that around 3% of their customers identify as African American or Black (see here where they say that their sample represents their customer database and the US population). This broadly agrees with data seen in a 23andme poster presented in 2011 (see here)
For now, the analyses will use an estimate of 80% for White Americans and 5% for Black Americans in the DTC databases.
The website here has some figures from an exploratory analysis of birth rate-related data from IPUMS. We will revisit this when we simulate populations and need to draw from this data for the parameters.
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.2.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readr_2.1.5 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] bit_4.0.5 jsonlite_1.8.8 crayon_1.5.2 compiler_4.3.2
[5] promises_1.2.1 tidyselect_1.2.0 Rcpp_1.0.12 stringr_1.5.1
[9] git2r_0.33.0 parallel_4.3.2 callr_3.7.3 later_1.3.2
[13] jquerylib_0.1.4 yaml_2.3.8 fastmap_1.1.1 R6_2.5.1
[17] knitr_1.45 tibble_3.2.1 rprojroot_2.0.4 tzdb_0.4.0
[21] bslib_0.6.1 pillar_1.9.0 rlang_1.1.3 utf8_1.2.4
[25] cachem_1.0.8 stringi_1.8.3 httpuv_1.6.13 xfun_0.41
[29] getPass_0.2-4 fs_1.6.3 sass_0.4.8 bit64_4.0.5
[33] cli_3.6.2 magrittr_2.0.3 ps_1.7.5 digest_0.6.34
[37] processx_3.8.3 vroom_1.6.5 rstudioapi_0.15.0 hms_1.1.3
[41] lifecycle_1.0.4 vctrs_0.6.5 evaluate_0.23 glue_1.7.0
[45] whisker_0.4.1 fansi_1.0.6 rmarkdown_2.25 httr_1.4.7
[49] tools_4.3.2 pkgconfig_2.0.3 htmltools_0.5.7