• Forensic
    • Analysis of Demographic Group Representation in Database vs Population
      • Data Loading and Preparation
      • Data Cleaning
      • Data Visualization
      • Summary Table

Last updated: 2024-03-01

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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.

library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Load the dataset
data <- read_csv("data/df_state-breakdown.csv")
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.
data_total <- read_csv("data/df_state_total.csv")
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.

# Convert the 'Value' column to numeric
data <- data %>%
  mutate(Value = as.numeric(str_remove(Value, "%")) / 100)

# Separate the data into database and population datasets
database_data <- data %>% filter(Context == "Database")
population_data <- data %>% filter(Context == "Population")

# Merge the two datasets
merged_data <- database_data %>%
  inner_join(population_data, by = c("State", "Demographic Group")) %>%
  rename(Value_Database = Value.x, Value_Population = Value.y) %>%
  mutate(Difference = Value_Database - Value_Population)

Data Visualization

We create a visualization to show the differences in representation for each demographic group by state.

# Plotting the differences
ggplot(merged_data, aes(x = State, y = Difference, fill = `Demographic Group`)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  theme_minimal() +
  labs(title = "Difference in Demographic Group Representation: Database vs Population by State",
       x = "State", y = "Difference in Representation") +
  geom_hline(yintercept = 0, linetype = 1)
Warning: Removed 5 rows containing missing values (`geom_bar()`).

Version Author Date
9e71347 Tina Lasisi 2024-01-22

Summary Table

Finally, we create a summary table showing the difference in representation for each state and demographic group.

# Creating the summary table
state_group_summary <- merged_data %>%
  group_by(State, `Demographic Group`) %>%
  summarize(Difference = mean(Difference)) %>%
  ungroup()
`summarise()` has grouped output by 'State'. You can override using the
`.groups` argument.
state_group_summary
# 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

sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3.1

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] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
 [5] purrr_1.0.2     readr_2.1.5     tidyr_1.3.0     tibble_3.2.1   
 [9] ggplot2_3.4.4   tidyverse_2.0.0 workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] sass_0.4.8        utf8_1.2.4        generics_0.1.3    stringi_1.8.3    
 [5] hms_1.1.3         digest_0.6.34     magrittr_2.0.3    timechange_0.2.0 
 [9] evaluate_0.23     grid_4.3.2        fastmap_1.1.1     rprojroot_2.0.4  
[13] jsonlite_1.8.8    processx_3.8.3    whisker_0.4.1     ps_1.7.5         
[17] promises_1.2.1    httr_1.4.7        fansi_1.0.6       scales_1.3.0     
[21] jquerylib_0.1.4   cli_3.6.2         crayon_1.5.2      rlang_1.1.3      
[25] bit64_4.0.5       munsell_0.5.0     withr_2.5.2       cachem_1.0.8     
[29] yaml_2.3.8        parallel_4.3.2    tools_4.3.2       tzdb_0.4.0       
[33] colorspace_2.1-0  httpuv_1.6.13     vctrs_0.6.5       R6_2.5.1         
[37] lifecycle_1.0.4   git2r_0.33.0      bit_4.0.5         fs_1.6.3         
[41] vroom_1.6.5       pkgconfig_2.0.3   callr_3.7.3       pillar_1.9.0     
[45] bslib_0.6.1       later_1.3.2       gtable_0.3.4      glue_1.7.0       
[49] Rcpp_1.0.12       highr_0.10        xfun_0.41         tidyselect_1.2.0 
[53] rstudioapi_0.15.0 knitr_1.45        farver_2.1.1      htmltools_0.5.7  
[57] labeling_0.4.3    rmarkdown_2.25    compiler_4.3.2    getPass_0.2-4