- Forensic
- Analysis of Demographic Group Representation in Database vs Population
- Data Loading and Preparation
- Data Cleaning
- Data Visualization
- Summary Table
- Summary of representation across Forensic and DTC databases
- Direct-to-Consumer Database Statistics
- Accessibility of Direct-to-Consumer Databases
- Direct-to-Consumer Database Total Size
- Racial Composition of Direct-to-Consumer Databases
Database composition
Tina Lasisi, Stella BooydeGraaff
2024-09-16 21:02:13
Last updated: 2024-09-16
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| Rmd | c7eb982 | Stella-Joy | 2024-09-04 | Small color shift in Race gradient |
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Forensic
Data based on Murphy & Tong
Analysis of Demographic Group Representation in Database vs Population
This document presents an analysis of the representation of various demographic groups in a database compared to their representation in the general population across different states.
Data Loading and Preparation
We start by loading the necessary libraries and the dataset.
Rows: 77 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): State, Demographic Group, Context, Value
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.
Rows: 14 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): Demographic, Region, Value
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.Data Cleaning
The data contains demographic information with percentages in two contexts: ‘Database’ and ‘Population’. We need to clean and transform the data for analysis.
Data Visualization
We create a visualization to show the differences in representation for each demographic group by state.
Warning: Removed 5 rows containing missing values or values outside the scale range
(`geom_bar()`).
Summary Table
Finally, we create a summary table showing the difference in representation for each state and demographic group.
`summarise()` has grouped output by 'State'. You can override using the
`.groups` argument.# A tibble: 35 × 3
State `Demographic Group` Difference
<chr> <chr> <dbl>
1 California Asian -0.134
2 California Black 0.116
3 California Hispanic -0.045
4 California Native American NA
5 California White -0.0740
6 Florida Asian -0.0267
7 Florida Black 0.183
8 Florida Hispanic -0.232
9 Florida Native American -0.0044
10 Florida White 0.0730
# ℹ 25 more rows
Direct-to-Consumer Database Statistics
Accessibility of Direct-to-Consumer Databases
Each Direct-to-Consumer company has different policies on whether their databases are accessible to law enforcement or to the public. The following chart shows the ratio of which of the six main companies allow for which group to access them.
| Version | Author | Date |
|---|---|---|
| 70651c7 | Stella-Joy | 2024-09-04 |
The companies that law enforcement can access are FamilyTreeDNA and GEDmatch. FamilyTreeDNA allows users to opt out but have a history of violating their Terms of Service. GEDmatch also sells access to law enforcement and has a history of violating their Terms of Service, but since the law enforcement used this database to find the Golden State Killer in 2018, they have created an opt-in option for sharing data with law enforcement.
The company whose database is open to the public is MyHeritage. Genetic data is not publicly available, but records such as birth, marriage, death, burial, census, military, immigration, yearbooks are. Family sites and certain settings can allow for publicly accessible genetic data as well.
The other Direct-to-Consumer companies are characterized as allowing access to neither law enforcement or the public. Ancestry DNA requires valid legal reasoning to share data with law enforcement. 23andMe has an opt-in option for scientific researcher that 81% of users do. Living DNA shares anonymous data with third parties, and has an opt-in option to share data with researchers, however, they do not sell data to law enforcement unless feel they are legally compelled.
Direct-to-Consumer Database Total Size
The following graph demonstrates the total number of people in each Direct-to-Consumer Database. AncestryDNA has the largest DNA database at 25,000,000. The second-largest database is 23andMe, which has 10,000,000 DNA samples. Living DNA has the smallest database, with 300,000 DNA samples.
| Version | Author | Date |
|---|---|---|
| 70651c7 | Stella-Joy | 2024-09-04 |
Another option is to show this on a log scale:
| Version | Author | Date |
|---|---|---|
| 70651c7 | Stella-Joy | 2024-09-04 |
Racial Composition of Direct-to-Consumer Databases
Most Direct-to-Consumer companies do not publish the racial or gender breakdown of their DNA samples. We have data from the 23andMe Research Innovation Collaborations and Replications Program, which is a portion of their database used for scientific research. The total number of DNA samples in this cohort is 8,000,000. The racial breakdown of this sample of the 23andMe database is shown below.
| Version | Author | Date |
|---|---|---|
| 70651c7 | Stella-Joy | 2024-09-04 |
From this data, we see that the majority of the 23andMe database is White (67.5%), then the order of biggest proportion to smallest proportion is Hispanic (16.4%), Black (5.5%), Asian (5%), Other (4.6%), and West Asian and North African (1%).
R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Sonoma 14.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
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] patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 stringi_1.8.4
[5] hms_1.1.3 digest_0.6.36 magrittr_2.0.3 timechange_0.3.0
[9] evaluate_0.24.0 grid_4.4.1 fastmap_1.2.0 rprojroot_2.0.4
[13] jsonlite_1.8.8 processx_3.8.4 whisker_0.4.1 ps_1.7.7
[17] promises_1.3.0 httr_1.4.7 fansi_1.0.6 scales_1.3.0
[21] jquerylib_0.1.4 cli_3.6.3 crayon_1.5.3 rlang_1.1.4
[25] bit64_4.0.5 munsell_0.5.1 withr_3.0.0 cachem_1.1.0
[29] yaml_2.3.9 parallel_4.4.1 tools_4.4.1 tzdb_0.4.0
[33] colorspace_2.1-0 httpuv_1.6.15 vctrs_0.6.5 R6_2.5.1
[37] lifecycle_1.0.4 git2r_0.33.0 bit_4.0.5 fs_1.6.4
[41] vroom_1.6.5 pkgconfig_2.0.3 callr_3.7.6 pillar_1.9.0
[45] bslib_0.7.0 later_1.3.2 gtable_0.3.5 glue_1.7.0
[49] Rcpp_1.0.12 highr_0.11 xfun_0.45 tidyselect_1.2.1
[53] rstudioapi_0.16.0 knitr_1.48 farver_2.1.2 htmltools_0.5.8.1
[57] labeling_0.4.3 rmarkdown_2.27 compiler_4.4.1 getPass_0.2-4