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# Load necessary libraries
library(readr)
library(tidycensus)
library(tidyverse)
library(knitr)
library(jtools)
library(sandwich)

# Load prison data
prison_data = read.csv("./data/populations_states.csv")

# Load CODIS data
codis_data = read.csv("./data/CODIS_data.csv")

# Create a data frame for Murphy and Tong profiles
murphy.tong = data.frame(
  state = c("California", "Florida", "Indiana", "Maine", "Nevada", "South Dakota", "Texas"), 
  total = c(2768269, 1350667, 307714, 32847, 167726, 67600, 960985,2768269, 1350667, 307714, 32847, 167726, 67600, 960985),
  mt.percent = c(0.296, 0.614, 0.7, 0.928, 0.694, 0.668, 0.373,0.171, 0.352, 0.26, 0.039, 0.256, 0.06, 0.291),
  race = c("White","White","White","White","White","White","White","Black","Black","Black","Black","Black","Black","Black")
)
# Load necessary libraries

# Calculate the number of White and Black profiles in Murphy and Tong data
murphy.tong$n = murphy.tong$total * murphy.tong$mt.percent

# Extract year from the date and filter data for 2022
prison_data$year = substring(prison_data$date, 1, 4)
prison_data_2022 = prison_data[which(prison_data$year == "2022"),]
prison_data_2022 = prison_data_2022[!duplicated(prison_data_2022[, 'state']),]

# Load census data for each state
# P1_002N is the total population, P1_003N is the total White population, and P1_004N is the total Black population
us_state_density <- get_decennial(
  geography = "state",
  variables = c(all = "P1_002N", white = "P1_003N", black = "P1_004N"),
  year = 2020,
  geometry = TRUE,
  keep_geo_vars = TRUE
)

# Spread the data into a wider format
us_state_density = spread(us_state_density, variable, value)

# Calculate the proportion of Black and White populations
us_state_density$census.percent.black = us_state_density$black / us_state_density$all
us_state_density$census.percent.white = us_state_density$white / us_state_density$all

# Rename column for merging
us_state_density$state = us_state_density$NAME.x

# Merge census data with prison data
us_state_density = merge(us_state_density, prison_data_2022, by = "state")

# Calculate the proportion of Black and White incarcerated individuals
us_state_density$percent.black.incarc = us_state_density$incarcerated_black / us_state_density$incarcerated_total
us_state_density$percent.white.incarc = us_state_density$incarcerated_white / us_state_density$incarcerated_total

# Merge with CODIS data
us_state_density = merge(us_state_density, codis_data, by = "state")

# Calculate the number of Black and White profiles in CODIS
us_state_density$black_profiles = us_state_density$percent.black.incarc * (us_state_density$offender_profiles)
us_state_density$white_profiles = us_state_density$percent.white.incarc * (us_state_density$offender_profiles)

us_state_density_black = as.data.frame(us_state_density[,c("state","all","black","census.percent.black","incarcerated_total",
                                       "incarcerated_black","percent.black.incarc","arrestee_profiles","offender_profiles","black_profiles")])
us_state_density_black$race = "Black"
colnames(us_state_density_black) = c("state","all","population","census.percent","incarcerated_total",
                                     "incarcerated_race","percent.incar","arrestee_profiles","offender_profiles","geometry","race_profiles","race")

us_state_density_white = as.data.frame(us_state_density[,c("state","all","white","census.percent.white","incarcerated_total",
                                             "incarcerated_white","percent.white.incarc", "arrestee_profiles","offender_profiles","white_profiles")])
us_state_density_white$race = "White"
colnames(us_state_density_white) = c("state","all","population","census.percent","incarcerated_total",
                                     "incarcerated_race","percent.incar","arrestee_profiles","offender_profiles","geometry","race_profiles","race")

inferred_data = rbind(us_state_density_black, us_state_density_white)

# Combine Murphy and Tong data with the merged dataset
combined = merge(murphy.tong, inferred_data, by = c("state","race"), all.x = TRUE)
combined$race_bin = ifelse(combined$race == "White",0,1)
# Fit linear models for Black and White population proportions
model.all <- lm(
    mt.percent ~           # Outcome: Murphy & Tong numbers for 7 states
    census.percent +       # Main effect of census proportion on CODIS proportion
    percent.incar +        # Main effect of prison proportion on CODIS proportion
    race_bin+                 # Main effect of race. This will estimate the change in CODIS proportion for different races relative to a baseline race.
    race_bin:census.percent +  # Interaction between race and census proportion.
                           # This checks if the effect of census proportion on CODIS proportion varies by race.
    race_bin:percent.incar,    # Interaction between race and prison proportion.
                           # This checks if the effect of prison proportion on CODIS proportion varies by race.
    data = combined        # The data set containing the variables
)

summary(model.all)
plot_summs(model.all, robust = TRUE)

Version Author Date
1eb6d2c hcvw 2024-06-25 
# Run regression model for different combinations of the following predictors:
# race, census proportion of race, estimated population from Klein data
formula_a = "census.percent + percent.incar + race_bin" # no interactions
formula_b = "percent.incar + race_bin + race_bin:percent.incar" # no census
formula_c = "percent.incar + race_bin"  # no census or interaction
formula_d = "census.percent + percent.incar" # no race

formulas = c(formula_a, formula_b, formula_c, formula_d)

model_df = as.data.frame(matrix(0,nrow = 4, ncol = 3))
colnames(model_df) = c("model","R^2","Anova")
for(i in 1:4){
  form = formulas[[i]]
  model <- lm(paste0('mt.percent ~', form), data = combined)
  p.val = round(anova(model, model.all)[[6]][2],2)
  model_df[i,] = c(form, round(summary(model)$adj.r.squared,2),p.val)
}
kable(model_df)

# generate predictions based on the model with all coefficients
combined$prediction = predict(model.all, combined)
# Plot the results with linear regression lines
ggplot(data = combined) +
  geom_point(aes(x = prediction, y = mt.percent,fill=state,shape = race),size=5,col="black") +
  scale_shape_manual(name="Race",labels = c("Black","White"),values = c(21,24)) +
  geom_abline(intercept = 0, slope = 1,col ="grey30", linetype = "dashed",size=1.5) +
  theme_classic() + xlab("Estimated CODIS profiles") + ylab("Murphy & Tong profiles (FOIA)") +
  annotate("text", x=0.2, y=0.75, size = 6, label= paste0(as.expression("R² = "), round(summary(model)$adj.r.squared,2))) +
  annotate("text", x=0.6, y=0.56, size = 4, label= "Perfect prediction line",angle = 46) +
  guides(fill=guide_legend(override.aes=list(shape=21))) +
  theme(axis.text.x = element_text(color = "black", size = 16, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(color = "black", size = 16, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(color = "black", size = 16, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(color = "black", size = 16, angle = 90, hjust = .5, vjust = .5, face = "plain"),
        aspect.ratio=1)

Version Author Date
1eb6d2c hcvw 2024-06-25 
combined$Dif <- combined$prediction - combined$mt.percent
combined$Avg <- (combined$prediction + combined$mt.percent) / 2

ggplot(combined, aes(x = Avg, y = Dif)) +
  geom_point(aes(shape = race,fill=state),size=4,col="black") +
  scale_fill_discrete(name = "State") +
  guides(fill=guide_legend(override.aes=list(shape=21))) +
  scale_shape_manual(name="Race",labels = c("Black","White"),values = c(21,24)) +
  geom_hline(yintercept = mean(combined$Dif), colour = "black", size = 1) +
  geom_hline(yintercept = mean(combined$Dif) - (1.96 * sd(combined$Dif)), colour = "grey30", size = 0.5) +
  geom_hline(yintercept = mean(combined$Dif) + (1.96 * sd(combined$Dif)), colour = "grey30", size = 0.5) +
  ylab("Difference Between Measures") +
  xlab("Average Measure") + theme_light() +
  theme(axis.text.x = element_text(color = "black", size = 16, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(color = "black", size = 16, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(color = "black", size = 16, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(color = "black", size = 16, angle = 90, hjust = .5, vjust = .5, face = "plain"),
        aspect.ratio=1)

Version Author Date
1eb6d2c hcvw 2024-06-25 

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] sandwich_3.1-0   jtools_2.2.2     knitr_1.46       lubridate_1.9.3 
 [5] forcats_1.0.0    stringr_1.5.0    dplyr_1.1.4      purrr_1.0.2     
 [9] tidyr_1.3.0      tibble_3.2.1     ggplot2_3.5.0    tidyverse_2.0.0 
[13] tidycensus_1.4.4 readr_2.1.4      workflowr_1.7.1 

loaded via a namespace (and not attached):
 [1] gtable_0.3.3       xfun_0.44          bslib_0.5.1        tigris_2.0.3      
 [5] processx_3.8.4     lattice_0.21-8     callr_3.7.3        tzdb_0.4.0        
 [9] vctrs_0.6.5        tools_4.3.1        ps_1.7.6           generics_0.1.3    
[13] curl_5.0.2         proxy_0.4-27       fansi_1.0.6        highr_0.10        
[17] pkgconfig_2.0.3    KernSmooth_2.23-22 uuid_1.1-1         lifecycle_1.0.4   
[21] farver_2.1.1       compiler_4.3.1     git2r_0.33.0       munsell_0.5.0     
[25] getPass_0.2-4      httpuv_1.6.11      htmltools_0.5.6    class_7.3-22      
[29] sass_0.4.7         yaml_2.3.7         later_1.3.1        pillar_1.9.0      
[33] crayon_1.5.2       jquerylib_0.1.4    whisker_0.4.1      ellipsis_0.3.2    
[37] classInt_0.4-9     cachem_1.0.8       tidyselect_1.2.0   rvest_1.0.3       
[41] digest_0.6.33      stringi_1.7.12     pander_0.6.5       sf_1.0-14         
[45] labeling_0.4.2     rprojroot_2.0.3    fastmap_1.1.1      grid_4.3.1        
[49] colorspace_2.1-0   cli_3.6.2          magrittr_2.0.3     utf8_1.2.4        
[53] broom_1.0.5        e1071_1.7-13       withr_3.0.0        backports_1.4.1   
[57] scales_1.3.0       promises_1.2.1     rappdirs_0.3.3     timechange_0.2.0  
[61] rmarkdown_2.25     httr_1.4.7         zoo_1.8-12         hms_1.1.3         
[65] evaluate_0.21      rlang_1.1.3        Rcpp_1.0.11        glue_1.7.0        
[69] DBI_1.1.3          xml2_1.3.5         rstudioapi_0.15.0  jsonlite_1.8.8    
[73] R6_2.5.1           fs_1.6.3           units_0.8-3