Cleanup and working on birth reg

2025-11-07 18:32:24 -07:00 · 2025-11-07 18:32:24 -07:00 · 9d0ba09898
commit 9d0ba09898
parent 89da07fdae
29 changed files with 110 additions and 128458 deletions
--- a/.gitignore
+++ b/.gitignore
@ -1,5 +1,7 @@
 # ---> R
 # 
+Data/Raw_Data/Population/
+Data/Raw_Data/Demographics/
 *.png
 *.csv
 Data/Cleaned_Data/
--- a/Birth_Rate_Regression.r
+++ b/Birth_Rate_Regression.r
@ -1,33 +0,0 @@
-##########################Model Population Trends
-##Run Regression 
-#Pull in Demographic data and create categories for key groups in the regression, male/female population with high fertility, children under one and two (but not zero). This data is broken down by each age group so aggregate to the county, year level for the final regression.
-#Fertility age bounds were informed by the regression found in the file ./Scripts/Other_Analysis/Select_Range_of_Male_Female_Fertility.r Which qualitatively supports that the number of people in these age ranges (18-28 Women, 18-30 Men) have the most significance in predicting birth rates. These two are combined into one variable which represent the minimum number of people in the key fertility window between the sexes, this is the binding fertility constraint and has more explanatory power than including either the number of men or women in the fertility window alone, providing a good trade off for including more variables or reducing variance.
-  DEMOGRAPHIC_DATA <- readRDS("Data/Cleaned_Data/Wyoming_County_Demographic_Data.Rds") %>% mutate(Male_Window=Age>=18 & Age<=30,Female_Window=Age>=18 & Age<=28) %>% group_by(County,Year) %>% summarize(Female_Birth_Group=sum(Num_Female*Female_Window),Male_Birth_Group=sum(Num_Male*Male_Window),Min_Birth_Group=ifelse(Female_Birth_Group<Male_Birth_Group,Female_Birth_Group,Male_Birth_Group))
-  #Extract the population trend data to connect with demographics (Population,births,deaths)
-  POP_DATA <- readRDS("Data/Cleaned_Data/Wyoming_County_Population.Rds") %>% mutate(LN=ifelse(County=="Lincoln",1,0))
-  #Merger the two data sets and drop any records that cannot be used in the regression (this makes the "predict" function output the right number of records)
-  REG_DATA <- POP_DATA %>% full_join(DEMOGRAPHIC_DATA) 
-  REG_DATA <- REG_DATA %>% group_by(County) %>% mutate(PREV_BIRTH=lag(Births),PREV_TWO_BIRTH=lag(Births,2)) %>% ungroup %>% filter(!is.na(PREV_TWO_BIRTH),!is.na(Min_Birth_Group))
-  REG_DATA$County <- factor(REG_DATA$County)
-  FIRST_PREDICT_YEAR_POPULATION_DATA <-  REG_DATA %>% filter(Year==2023,County=='Lincoln') %>% select(-LN,-Female_Birth_Group,-Male_Birth_Group) #Store the data set of only the first year needing a birth forecast, to start the birth Monte Carlo simulations.
-  REG_DATA <- REG_DATA %>% filter(!is.na(Births)) #Remove any values with missing births for a simpler regression which includes only complete data
-
-
-
-###Predict the number of Births
-MOD_BIRTHS <- feols(log(Births)~log(PREV_BIRTH)+log(PREV_TWO_BIRTH)+log(Min_Birth_Group)+Year*County,cluster=~Year+County, data=REG_DATA ) #Lower AIC
-	#AIC(MOD_BIRTHS)
-#MOD_BIRTHS <- feols(log(Births)~log(PREV_BIRTH)+log(Min_Birth_Group)+Year*County,cluster=~Year+County, data=REG_DATA )
-	#AIC(MOD_BIRTHS)
- #Optional: Review the ACF and PACF for validity. Model made on October 24nd appears to have uncorrelated lags of residuals accept year three.
-  RES_DATA <- REG_DATA #Data to create visuals with, without changing the main file. Can be used for ggplot, or residual tests
-  RES_DATA$RESID <- resid(MOD_BIRTHS)
-  acf(RES_DATA %>% pull(RESID))
-  pacf(RES_DATA %>% pull(RESID))
-saveRDS(RES_DATA,"Data/Regression_Results/Birth_Regression_Data_Set.Rds")
-  saveRDS(MOD_BIRTHS,BIRTH_RATE_REG_RESULTS)
-  saveRDS(FIRST_PREDICT_YEAR_POPULATION_DATA,START_DEMOGRAPHIC_DATA) #Save the cleaned data set for later use when starting the simulation.
-  #Cleanup data no longer needed, and save some RAM
-	  rm(POP_DATA,DEMOGRAPHIC_DATA,REG_DATA)
-	  gc()
-
--- a/Data/Cleaned_Data/Intiate_Simulation/Kemmerer_Demographic_Data.csv
+++ b/Data/Cleaned_Data/Intiate_Simulation/Kemmerer_Demographic_Data.csv
@ -1,87 +0,0 @@
-County,Year,Age,Num_Male,Num_Female
-Kemmerer,2024,0,10,19
-Kemmerer,2024,1,11,26
-Kemmerer,2024,2,10,22
-Kemmerer,2024,3,12,26
-Kemmerer,2024,4,10,24
-Kemmerer,2024,5,15,17
-Kemmerer,2024,6,19,19
-Kemmerer,2024,7,21,22
-Kemmerer,2024,8,17,19
-Kemmerer,2024,9,21,20
-Kemmerer,2024,10,23,20
-Kemmerer,2024,11,25,20
-Kemmerer,2024,12,25,19
-Kemmerer,2024,13,24,23
-Kemmerer,2024,14,27,24
-Kemmerer,2024,15,14,13
-Kemmerer,2024,16,14,13
-Kemmerer,2024,17,15,12
-Kemmerer,2024,18,8,8
-Kemmerer,2024,19,7,6
-Kemmerer,2024,20,9,28
-Kemmerer,2024,21,0,6
-Kemmerer,2024,22,10,16
-Kemmerer,2024,23,9,14
-Kemmerer,2024,24,11,14
-Kemmerer,2024,25,14,14
-Kemmerer,2024,26,13,15
-Kemmerer,2024,27,13,14
-Kemmerer,2024,28,9,14
-Kemmerer,2024,29,13,16
-Kemmerer,2024,30,10,16
-Kemmerer,2024,31,11,16
-Kemmerer,2024,32,9,17
-Kemmerer,2024,33,10,17
-Kemmerer,2024,34,10,21
-Kemmerer,2024,35,23,14
-Kemmerer,2024,36,24,15
-Kemmerer,2024,37,21,16
-Kemmerer,2024,38,23,17
-Kemmerer,2024,39,29,16
-Kemmerer,2024,40,18,12
-Kemmerer,2024,41,18,13
-Kemmerer,2024,42,17,13
-Kemmerer,2024,43,18,15
-Kemmerer,2024,44,20,15
-Kemmerer,2024,45,10,21
-Kemmerer,2024,46,11,17
-Kemmerer,2024,47,11,17
-Kemmerer,2024,48,10,16
-Kemmerer,2024,49,8,17
-Kemmerer,2024,50,12,17
-Kemmerer,2024,51,13,19
-Kemmerer,2024,52,13,21
-Kemmerer,2024,53,12,21
-Kemmerer,2024,54,10,18
-Kemmerer,2024,55,27,18
-Kemmerer,2024,56,28,19
-Kemmerer,2024,57,25,20
-Kemmerer,2024,58,30,22
-Kemmerer,2024,59,33,22
-Kemmerer,2024,60,25,14
-Kemmerer,2024,61,26,15
-Kemmerer,2024,62,23,17
-Kemmerer,2024,63,23,17
-Kemmerer,2024,64,24,17
-Kemmerer,2024,65,18,31
-Kemmerer,2024,66,20,32
-Kemmerer,2024,67,32,32
-Kemmerer,2024,68,32,33
-Kemmerer,2024,69,29,31
-Kemmerer,2024,70,27,15
-Kemmerer,2024,71,26,15
-Kemmerer,2024,72,22,13
-Kemmerer,2024,73,20,12
-Kemmerer,2024,74,20,12
-Kemmerer,2024,75,27,9
-Kemmerer,2024,76,32,9
-Kemmerer,2024,77,19,5
-Kemmerer,2024,78,17,5
-Kemmerer,2024,79,16,5
-Kemmerer,2024,80,8,6
-Kemmerer,2024,81,5,5
-Kemmerer,2024,82,4,5
-Kemmerer,2024,83,4,4
-Kemmerer,2024,84,3,4
-Kemmerer,2024,85,21,18
--- a/Data/Cleaned_Data/Intiate_Simulation/Kemmerer_Summary_Start_Data.csv
+++ b/Data/Cleaned_Data/Intiate_Simulation/Kemmerer_Summary_Start_Data.csv
@ -1,2 +0,0 @@
-Year,County,Population,Births,Deaths,Migration,Min_Birth_Group,PREV_BIRTH,PREV_TWO_BIRTH
-2024,Kemmerer,2895,NA,NA,NA,126,29,37
--- a/Data/Cleaned_Data/Lincoln_Demographic_Data.Rds
+++ b/Data/Cleaned_Data/Lincoln_Demographic_Data.Rds
--- a/Data/Cleaned_Data/Lincoln_Demographic_Data.csv
+++ b/Data/Cleaned_Data/Lincoln_Demographic_Data.csv
--- a/Data/Cleaned_Data/Lincoln_Mortality_Rate.Rds
+++ b/Data/Cleaned_Data/Lincoln_Mortality_Rate.Rds
--- a/Data/Cleaned_Data/Lincoln_Mortality_Rate.csv
+++ b/Data/Cleaned_Data/Lincoln_Mortality_Rate.csv
@ -1,17 +0,0 @@
-County,Sex,Min_Age,Max_Age,Death_Rate,Rate_SD,Imparted_Rate,Trend,Trend_SD,Imparted_Trend
-Lincoln,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Lincoln,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Lincoln,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Lincoln,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Lincoln,Female,40,64,0.0033410000000000002,4.6096938775510205e-4,FALSE,9e-6,9.948979591836735e-6,FALSE
-Lincoln,Female,65,74,0.009472999999999999,0.0012964285714285714,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Lincoln,Female,75,84,0.043425,0.004336734693877551,FALSE,-2e-6,8.418367346938775e-6,FALSE
-Lincoln,Female,85,Inf,0.145245,0.013969642857142858,FALSE,-6e-6,6.3775510204081635e-6,FALSE
-Lincoln,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Lincoln,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Lincoln,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Lincoln,Male,20,39,0.00293,5.543367346938776e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Lincoln,Male,40,64,0.005426,5.724489795918367e-4,FALSE,5e-6,8.163265306122448e-6,FALSE
-Lincoln,Male,65,74,0.017908,0.0017096938775510205,FALSE,-1.5e-5,1.0459183673469388e-5,FALSE
-Lincoln,Male,75,84,0.049228999999999995,0.004641581632653062,FALSE,-1.5e-5,7.397959183673469e-6,FALSE
-Lincoln,Male,85,Inf,0.15788,0.017563265306122452,FALSE,-1.54e-4,6.173469387755103e-5,FALSE
--- a/Data/Cleaned_Data/Not_Used/Wyoming_County_Mortality_Rate.Rds
+++ b/Data/Cleaned_Data/Not_Used/Wyoming_County_Mortality_Rate.Rds
--- a/Data/Cleaned_Data/Not_Used/Wyoming_County_Mortality_Rate.csv
+++ b/Data/Cleaned_Data/Not_Used/Wyoming_County_Mortality_Rate.csv
@ -1,401 +0,0 @@
-County,Sex,Min_Age,Max_Age,Death_Rate,Rate_SD,Imparted_Rate,Trend,Trend_SD,Imparted_Trend
-Wyoming,Female,0,0,0.005673999999999999,6.415816326530612e-4,FALSE,5e-6,6.122448979591837e-6,TRUE
-United States,Female,0,0,0.005039,2.3724489795918395e-5,FALSE,5e-6,6.122448979591837e-6,FALSE
-Laramie,Female,0,0,0.00635,0.0016502551020408163,FALSE,5e-6,6.122448979591837e-6,TRUE
-Albany,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Big Horn,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Campbell,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Carbon,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Converse,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Crook,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Fremont,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Goshen,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Hot Springs,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Johnson,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Lincoln,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Natrona,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Niobrara,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Park,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Platte,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Sheridan,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Sublette,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Sweetwater,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Teton,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Uinta,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Washakie,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Weston,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
-Wyoming,Female,1,9,1.79e-4,3.64795918367347e-5,FALSE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-United States,Female,1,9,1.5800000000000002e-4,1.2755102040816327e-6,FALSE,3.7999999999999995e-5,9.693877551020408e-6,FALSE
-Albany,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Big Horn,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Campbell,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Carbon,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Converse,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Crook,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Fremont,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Goshen,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Hot Springs,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Johnson,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Laramie,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Lincoln,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Natrona,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Niobrara,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Park,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Platte,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Sheridan,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Sublette,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Sweetwater,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Teton,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Uinta,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Washakie,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Weston,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
-Wyoming,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,FALSE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-United States,Female,10,19,2.2600000000000002e-4,1.5306122448979538e-6,FALSE,2.2000000000000003e-5,4.846938775510204e-6,FALSE
-Albany,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Big Horn,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Campbell,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Carbon,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Converse,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Crook,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Fremont,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Goshen,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Hot Springs,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Johnson,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Laramie,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Lincoln,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Natrona,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Niobrara,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Park,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Platte,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Sheridan,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Sublette,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Sweetwater,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Teton,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Uinta,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Washakie,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Weston,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
-Wyoming,Female,20,39,0.001086,5.5867346938775525e-5,FALSE,1e-5,4.3367346938775506e-6,FALSE
-United States,Female,20,39,9.96e-4,2.040816326530605e-6,FALSE,0,9.948979591836735e-6,FALSE
-Fremont,Female,20,39,0.003397,4.048469387755102e-4,FALSE,1e-5,4.3367346938775506e-6,TRUE
-Sweetwater,Female,20,39,0.001521,2.4821428571428575e-4,FALSE,1e-5,4.3367346938775506e-6,TRUE
-Park,Female,20,39,0.001124,2.834183673469388e-4,FALSE,1e-5,4.3367346938775506e-6,TRUE
-Natrona,Female,20,39,0.0011070000000000001,1.5127551020408165e-4,FALSE,1e-5,4.3367346938775506e-6,TRUE
-Laramie,Female,20,39,0.001044,1.3112244897959185e-4,FALSE,1e-5,4.3367346938775506e-6,TRUE
-Campbell,Female,20,39,8.79e-4,1.8290816326530613e-4,FALSE,1e-5,4.3367346938775506e-6,TRUE
-Albany,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Big Horn,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Carbon,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Converse,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Crook,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Goshen,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Hot Springs,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Johnson,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Lincoln,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Niobrara,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Platte,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Sheridan,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Sublette,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Teton,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Uinta,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Washakie,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Weston,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
-Wyoming,Female,40,64,0.004525,1.0025510204081622e-4,FALSE,-3e-6,1.1224489795918369e-5,FALSE
-United States,Female,40,64,0.0040869999999999995,3.826530612244898e-6,FALSE,-8e-6,7.653061224489796e-6,FALSE
-Fremont,Female,40,64,0.007469,5.158163265306123e-4,FALSE,2.5e-5,7.653061224489796e-6,FALSE
-Uinta,Female,40,64,0.006474,6.68877551020408e-4,FALSE,6.3e-5,4.2602040816326545e-5,FALSE
-Hot Springs,Female,40,64,0.006397000000000001,0.001375,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Sweetwater,Female,40,64,0.005991,4.3214285714285707e-4,FALSE,5.1e-5,3.188775510204082e-5,FALSE
-Carbon,Female,40,64,0.00577,7.267857142857144e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Goshen,Female,40,64,0.005709,8.801020408163265e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Natrona,Female,40,64,0.0049960000000000004,2.816326530612246e-4,FALSE,1.1000000000000001e-5,1.9387755102040817e-5,FALSE
-Converse,Female,40,64,0.004393,6.57908163265306e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Platte,Female,40,64,0.004347,8.665816326530613e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Washakie,Female,40,64,0.00421,8.451530612244897e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Laramie,Female,40,64,0.004162,2.3010204081632652e-4,FALSE,-1e-6,4.846938775510204e-6,FALSE
-Park,Female,40,64,0.004129,4.392857142857142e-4,FALSE,1e-5,1.5051020408163266e-5,FALSE
-Campbell,Female,40,64,0.004102,3.423469387755102e-4,FALSE,-3e-6,7.908163265306124e-6,FALSE
-Sheridan,Female,40,64,0.003923,3.895408163265306e-4,FALSE,1.1000000000000001e-5,1.0204081632653061e-5,FALSE
-Big Horn,Female,40,64,0.0037739999999999996,6.591836734693878e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Crook,Female,40,64,0.003632,9.025510204081632e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Albany,Female,40,64,0.0035910000000000004,4.1428571428571426e-4,FALSE,0,7.397959183673469e-6,FALSE
-Johnson,Female,40,64,0.003374,7.191326530612244e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Lincoln,Female,40,64,0.0033410000000000002,4.6096938775510205e-4,FALSE,9e-6,9.948979591836735e-6,FALSE
-Sublette,Female,40,64,0.002813,6.80612244897959e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Teton,Female,40,64,0.001251,2.670918367346939e-4,FALSE,-3e-6,1.1224489795918369e-5,TRUE
-Niobrara,Female,40,64,0.004525,1.0025510204081622e-4,TRUE,-3e-6,1.1224489795918369e-5,TRUE
-Weston,Female,40,64,0.004525,1.0025510204081622e-4,TRUE,-3e-6,1.1224489795918369e-5,TRUE
-Wyoming,Female,65,74,0.016364,3.255102040816324e-4,FALSE,-1.7e-5,1.6326530612244897e-5,FALSE
-United States,Female,65,74,0.015801,1.352040816326519e-5,FALSE,-1.1000000000000001e-5,6.632653061224491e-6,FALSE
-Big Horn,Female,65,74,0.022495,0.0026020408163265306,FALSE,1.5e-5,1.2755102040816327e-5,FALSE
-Fremont,Female,65,74,0.019869,0.0013096938775510208,FALSE,-3e-6,7.653061224489796e-6,FALSE
-Natrona,Female,65,74,0.019782,0.001003571428571428,FALSE,0,5.102040816326531e-6,FALSE
-Laramie,Female,65,74,0.019148,8.607142857142859e-4,FALSE,7.2e-5,3.112244897959183e-5,FALSE
-Sweetwater,Female,65,74,0.018866,0.0014732142857142858,FALSE,-9e-6,6.3775510204081635e-6,FALSE
-Campbell,Female,65,74,0.017571,0.0014079081632653063,FALSE,-1.2e-5,8.418367346938776e-6,FALSE
-Hot Springs,Female,65,74,0.017042,0.003117091836734694,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Carbon,Female,65,74,0.016312,0.0021163265306122447,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Weston,Female,65,74,0.016185,0.002942091836734694,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Converse,Female,65,74,0.016108,0.0022005102040816332,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Goshen,Female,65,74,0.015045,0.0020487244897959183,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Uinta,Female,65,74,0.014986,0.0017719387755102038,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Washakie,Female,65,74,0.014965,0.0025765306122448976,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Crook,Female,65,74,0.014662,0.0025719387755102035,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Platte,Female,65,74,0.014537000000000001,0.0022316326530612243,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Park,Female,65,74,0.014377000000000001,0.0011780612244897959,FALSE,-7e-6,5.612244897959184e-6,FALSE
-Albany,Female,65,74,0.01376,0.0013933673469387756,FALSE,-2.2000000000000003e-5,1.2499999999999997e-5,FALSE
-Sheridan,Female,65,74,0.013214,0.0011525510204081631,FALSE,-1.3000000000000001e-5,8.673469387755101e-6,FALSE
-Johnson,Female,65,74,0.011374,0.0019609693877551022,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Sublette,Female,65,74,0.010197999999999999,0.0020364795918367345,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Lincoln,Female,65,74,0.009472999999999999,0.0012964285714285714,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Teton,Female,65,74,0.0054589999999999994,0.0010316326530612244,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
-Niobrara,Female,65,74,0.016364,3.255102040816324e-4,TRUE,-1.7e-5,1.6326530612244897e-5,TRUE
-Wyoming,Female,75,84,0.04426,7.709183673469382e-4,FALSE,1.5e-5,1.1989795918367348e-5,FALSE
-United States,Female,75,84,0.041162,2.984693877551206e-5,FALSE,-1.7e-5,6.122448979591838e-6,FALSE
-Weston,Female,75,84,0.064473,0.008212244897959181,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Hot Springs,Female,75,84,0.058228,0.007786479591836735,FALSE,1.2e-5,1.1479591836734695e-5,FALSE
-Big Horn,Female,75,84,0.05412,0.005449489795918368,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Fremont,Female,75,84,0.052425,0.0030892857142857146,FALSE,4e-6,6.3775510204081635e-6,FALSE
-Natrona,Female,75,84,0.049432,0.0023053571428571424,FALSE,2e-6,3.826530612244898e-6,FALSE
-Campbell,Female,75,84,0.049208,0.003808163265306123,FALSE,-5e-6,6.887755102040817e-6,FALSE
-Laramie,Female,75,84,0.048791,0.0019788265306122446,FALSE,2e-6,4.336734693877551e-6,FALSE
-Sweetwater,Female,75,84,0.047755,0.0035678571428571417,FALSE,-2e-6,6.3775510204081635e-6,FALSE
-Converse,Female,75,84,0.046121999999999996,0.005119387755102041,FALSE,9e-6,8.92857142857143e-6,FALSE
-Goshen,Female,75,84,0.044093999999999994,0.004294387755102041,FALSE,-2e-6,7.908163265306122e-6,FALSE
-Niobrara,Female,75,84,0.04366,0.010155612244897957,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Lincoln,Female,75,84,0.043425,0.004336734693877551,FALSE,-2e-6,8.418367346938775e-6,FALSE
-Platte,Female,75,84,0.04324600000000001,0.004982908163265305,FALSE,5e-6,7.142857142857143e-6,FALSE
-Carbon,Female,75,84,0.042355,0.00491173469387755,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Albany,Female,75,84,0.040271,0.0034446428571428566,FALSE,-3e-6,8.418367346938775e-6,FALSE
-Washakie,Female,75,84,0.039571999999999996,0.005434948979591836,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Sheridan,Female,75,84,0.038169,0.0027647959183673466,FALSE,-1.4e-5,6.122448979591837e-6,FALSE
-Uinta,Female,75,84,0.037538,0.004328571428571428,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Park,Female,75,84,0.034838,0.0025653061224489796,FALSE,-1.3000000000000001e-5,6.3775510204081635e-6,FALSE
-Johnson,Female,75,84,0.033609,0.0047066326530612245,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Crook,Female,75,84,0.032882,0.005749234693877551,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Sublette,Female,75,84,0.03058,0.004828826530612244,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Teton,Female,75,84,0.021231999999999997,0.002872959183673469,FALSE,1.5e-5,1.1989795918367348e-5,TRUE
-Wyoming,Female,85,Inf,0.14438700000000002,0.0021448979591836763,FALSE,9e-6,1.1479591836734695e-5,FALSE
-United States,Female,85,Inf,0.139983,8.39285714285705e-5,FALSE,-1.1000000000000001e-5,9.438775510204082e-6,FALSE
-Hot Springs,Female,85,Inf,0.18055900000000003,0.01969591836734694,FALSE,2e-5,1.1989795918367348e-5,FALSE
-Crook,Female,85,Inf,0.165957,0.020828061224489797,FALSE,9e-6,1.1479591836734695e-5,TRUE
-Albany,Female,85,Inf,0.16516099999999997,0.011379336734693879,FALSE,1.8999999999999998e-5,2.3979591836734696e-5,FALSE
-Campbell,Female,85,Inf,0.161304,0.011592091836734689,FALSE,-3e-6,6.887755102040817e-6,FALSE
-Natrona,Female,85,Inf,0.156174,0.0059295918367346925,FALSE,1.4e-5,7.142857142857143e-6,FALSE
-Washakie,Female,85,Inf,0.15390600000000002,0.015426020408163266,FALSE,0,8.418367346938775e-6,FALSE
-Johnson,Female,85,Inf,0.152618,0.015923979591836737,FALSE,1.2e-5,7.908163265306122e-6,FALSE
-Sweetwater,Female,85,Inf,0.149978,0.009936734693877554,FALSE,-5e-6,7.397959183673469e-6,FALSE
-Sheridan,Female,85,Inf,0.14934799999999998,0.008692091836734691,FALSE,-8e-6,4.081632653061224e-6,FALSE
-Goshen,Female,85,Inf,0.148905,0.011026530612244897,FALSE,1.1e-4,5.637755102040817e-5,FALSE
-Laramie,Female,85,Inf,0.145436,0.005139795918367349,FALSE,5.3e-5,2.7295918367346937e-5,FALSE
-Lincoln,Female,85,Inf,0.145245,0.013969642857142858,FALSE,-6e-6,6.3775510204081635e-6,FALSE
-Big Horn,Female,85,Inf,0.143643,0.012999744897959188,FALSE,-1.3000000000000001e-5,6.3775510204081635e-6,FALSE
-Converse,Female,85,Inf,0.140676,0.014763010204081628,FALSE,-6e-6,1.1989795918367346e-5,FALSE
-Carbon,Female,85,Inf,0.138829,0.014641071428571427,FALSE,-1.7e-5,9.183673469387756e-6,FALSE
-Fremont,Female,85,Inf,0.135523,0.007590051020408162,FALSE,-5e-6,4.591836734693877e-6,FALSE
-Park,Female,85,Inf,0.135181,0.007483418367346939,FALSE,2e-6,6.122448979591837e-6,FALSE
-Weston,Female,85,Inf,0.126442,0.015349744897959185,FALSE,9e-6,1.1479591836734695e-5,TRUE
-Platte,Female,85,Inf,0.119165,0.012705357142857142,FALSE,-1.2e-5,1.1989795918367348e-5,FALSE
-Niobrara,Female,85,Inf,0.11524,0.026242091836734698,FALSE,9e-6,1.1479591836734695e-5,TRUE
-Teton,Female,85,Inf,0.11490299999999999,0.01195433673469388,FALSE,9e-6,1.1479591836734695e-5,TRUE
-Uinta,Female,85,Inf,0.111856,0.011629846938775513,FALSE,-2.7000000000000002e-5,8.92857142857143e-6,FALSE
-Sublette,Female,85,Inf,0.091725,0.015146428571428571,FALSE,9e-6,1.1479591836734695e-5,TRUE
-Wyoming,Male,0,0,0.005242,5.926020408163265e-4,FALSE,-1e-5,2.040816326530612e-6,TRUE
-United States,Male,0,0,0.005962,2.5255102040816556e-5,FALSE,-1e-5,2.040816326530612e-6,FALSE
-Natrona,Male,0,0,0.0075320000000000005,0.0019571428571428574,FALSE,-1e-5,2.040816326530612e-6,TRUE
-Laramie,Male,0,0,0.005385,0.0014459183673469385,FALSE,-1e-5,2.040816326530612e-6,TRUE
-Albany,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Big Horn,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Campbell,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Carbon,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Converse,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Crook,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Fremont,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Goshen,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Hot Springs,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Johnson,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Lincoln,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Niobrara,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Park,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Platte,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Sheridan,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Sublette,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Sweetwater,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Teton,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Uinta,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Washakie,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Weston,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
-Wyoming,Male,1,9,2.07e-4,3.775510204081633e-5,FALSE,4e-5,8.673469387755103e-6,TRUE
-United States,Male,1,9,1.94e-4,1.2755102040816327e-6,FALSE,4e-5,8.673469387755103e-6,FALSE
-Albany,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Big Horn,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Campbell,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Carbon,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Converse,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Crook,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Fremont,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Goshen,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Hot Springs,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Johnson,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Laramie,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Lincoln,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Natrona,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Niobrara,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Park,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Platte,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Sheridan,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Sublette,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Sweetwater,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Teton,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Uinta,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Washakie,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Weston,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
-Wyoming,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,FALSE,-1e-6,8.673469387755103e-6,FALSE
-United States,Male,10,19,4.94e-4,2.040816326530605e-6,FALSE,3.2e-5,5.867346938775511e-6,FALSE
-Fremont,Male,10,19,0.001368,3.25765306122449e-4,FALSE,-1e-6,8.673469387755103e-6,TRUE
-Laramie,Male,10,19,7.559999999999999e-4,1.596938775510204e-4,FALSE,-1e-6,8.673469387755103e-6,TRUE
-Natrona,Male,10,19,6.2e-4,1.6096938775510204e-4,FALSE,-1e-6,8.673469387755103e-6,TRUE
-Albany,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Big Horn,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Campbell,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Carbon,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Converse,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Crook,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Goshen,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Hot Springs,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Johnson,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Lincoln,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Niobrara,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Park,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Platte,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Sheridan,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Sublette,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Sweetwater,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Teton,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Uinta,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Washakie,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Weston,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
-Wyoming,Male,20,39,0.002489,8.086734693877556e-5,FALSE,5.6e-5,3.214285714285715e-5,FALSE
-United States,Male,20,39,0.002212,3.316326530612274e-6,FALSE,1.2e-5,1.1224489795918369e-5,FALSE
-Fremont,Male,20,39,0.005635,5.081632653061224e-4,FALSE,1.2e-5,9.183673469387756e-6,FALSE
-Big Horn,Male,20,39,0.003754,8.112244897959184e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Uinta,Male,20,39,0.003661,5.752551020408163e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Sweetwater,Male,20,39,0.002951,3.3010204081632664e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Lincoln,Male,20,39,0.00293,5.543367346938776e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Converse,Male,20,39,0.002522,5.869897959183673e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Natrona,Male,20,39,0.002503,2.2015306122448976e-4,FALSE,4e-6,7.908163265306124e-6,FALSE
-Campbell,Male,20,39,0.002484,2.795918367346939e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Laramie,Male,20,39,0.002457,1.8775510204081632e-4,FALSE,2.1000000000000002e-5,8.92857142857143e-6,FALSE
-Carbon,Male,20,39,0.002385,5.056122448979592e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Sheridan,Male,20,39,0.001987,3.4693877551020415e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Park,Male,20,39,0.001602,3.3214285714285724e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Teton,Male,20,39,0.001359,3.025510204081633e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Albany,Male,20,39,0.0013169999999999998,2.3724489795918374e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
-Crook,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Goshen,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Hot Springs,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Johnson,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Niobrara,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Platte,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Sublette,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Washakie,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Weston,Male,20,39,0.002489,8.086734693877556e-5,TRUE,5.6e-5,3.214285714285715e-5,TRUE
-Wyoming,Male,40,64,0.006889,1.2066326530612233e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,FALSE
-United States,Male,40,64,0.006857,4.846938775510436e-6,FALSE,-2e-6,7.397959183673469e-6,FALSE
-Fremont,Male,40,64,0.010539000000000001,6.056122448979588e-4,FALSE,1.8e-5,8.673469387755101e-6,FALSE
-Hot Springs,Male,40,64,0.008534,0.0016448979591836734,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Natrona,Male,40,64,0.008146,3.558673469387755e-4,FALSE,-1.2e-5,3.112244897959183e-5,FALSE
-Uinta,Male,40,64,0.0077020000000000005,6.903061224489797e-4,FALSE,1.3000000000000001e-5,7.142857142857143e-6,FALSE
-Carbon,Male,40,64,0.0075639999999999995,7.90561224489796e-4,FALSE,1.4e-5,8.418367346938775e-6,FALSE
-Goshen,Male,40,64,0.007551,8.545918367346938e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Sweetwater,Male,40,64,0.007405,4.6096938775510215e-4,FALSE,5.7e-5,2.9336734693877552e-5,FALSE
-Laramie,Male,40,64,0.007075,2.9642857142857156e-4,FALSE,3e-6,4.591836734693877e-6,FALSE
-Big Horn,Male,40,64,0.006978,9.219387755102041e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Platte,Male,40,64,0.006887000000000001,0.0010561224489795918,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Crook,Male,40,64,0.00663,0.0011035714285714284,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Albany,Male,40,64,0.006542,5.428571428571431e-4,FALSE,5e-5,4.0816326530612245e-5,FALSE
-Campbell,Male,40,64,0.006417999999999999,4.14795918367347e-4,FALSE,7.599999999999999e-5,4.7448979591836735e-5,FALSE
-Park,Male,40,64,0.006296,5.252551020408163e-4,FALSE,1.7e-5,9.438775510204082e-6,FALSE
-Washakie,Male,40,64,0.006225,9.89795918367347e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Sheridan,Male,40,64,0.0059039999999999995,4.8545918367346927e-4,FALSE,-6e-6,5.102040816326531e-6,FALSE
-Weston,Male,40,64,0.005822999999999999,9.770408163265307e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Lincoln,Male,40,64,0.005426,5.724489795918367e-4,FALSE,5e-6,8.163265306122448e-6,FALSE
-Converse,Male,40,64,0.005411,6.875e-4,FALSE,2.8e-5,2.6785714285714288e-5,FALSE
-Sublette,Male,40,64,0.005327999999999999,8.441326530612244e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Johnson,Male,40,64,0.004399,7.793367346938775e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Teton,Male,40,64,0.002084,3.191326530612244e-4,FALSE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Niobrara,Male,40,64,0.006889,1.2066326530612233e-4,TRUE,-2.1000000000000002e-5,1.3520408163265308e-5,TRUE
-Wyoming,Male,65,74,0.023544000000000002,3.8392857142857147e-4,FALSE,-1e-5,1.0459183673469388e-5,FALSE
-United States,Male,65,74,0.024285,1.785714285714286e-5,FALSE,-1.1000000000000001e-5,6.3775510204081635e-6,FALSE
-Hot Springs,Male,65,74,0.038213000000000004,0.004755357142857143,FALSE,6.8e-5,8.64795918367347e-5,FALSE
-Carbon,Male,65,74,0.029925999999999998,0.0026982142857142856,FALSE,-4e-6,7.908163265306124e-6,FALSE
-Goshen,Male,65,74,0.027766,0.00267295918367347,FALSE,1.2e-5,7.908163265306124e-6,FALSE
-Platte,Male,65,74,0.027726999999999998,0.00297295918367347,FALSE,4.4000000000000006e-5,4.1581632653061226e-5,FALSE
-Natrona,Male,65,74,0.027261,0.0011729591836734687,FALSE,5.6e-5,3.2908163265306125e-5,FALSE
-Sweetwater,Male,65,74,0.02687,0.0016755102040816319,FALSE,2e-6,7.397959183673469e-6,FALSE
-Fremont,Male,65,74,0.026595,0.0015433673469387756,FALSE,-6e-6,6.887755102040817e-6,FALSE
-Laramie,Male,65,74,0.02637,0.0010372448979591835,FALSE,-1e-6,1.9387755102040817e-5,FALSE
-Big Horn,Male,65,74,0.025905,0.0027229591836734697,FALSE,-1e-5,8.418367346938775e-6,FALSE
-Sheridan,Male,65,74,0.025036,0.0015446428571428573,FALSE,3e-6,5.102040816326531e-6,FALSE
-Washakie,Male,65,74,0.025003,0.003276020408163265,FALSE,-1e-5,1.0459183673469388e-5,TRUE
-Converse,Male,65,74,0.024411,0.0025487244897959183,FALSE,-5e-6,7.908163265306122e-6,FALSE
-Crook,Male,65,74,0.023588,0.00314591836734694,FALSE,-1e-5,1.0459183673469388e-5,TRUE
-Uinta,Male,65,74,0.021993000000000002,0.00206530612244898,FALSE,-2.2000000000000003e-5,9.183673469387756e-6,FALSE
-Campbell,Male,65,74,0.020179000000000002,0.001477551020408163,FALSE,-2.7000000000000002e-5,7.142857142857143e-6,FALSE
-Park,Male,65,74,0.020058,0.0013683673469387758,FALSE,4.0999999999999994e-5,5.025510204081632e-5,FALSE
-Johnson,Male,65,74,0.01995,0.0024625,FALSE,-1e-5,1.0459183673469388e-5,TRUE
-Weston,Male,65,74,0.018955,0.0028454081632653064,FALSE,-1e-5,1.0459183673469388e-5,TRUE
-Niobrara,Male,65,74,0.018746,0.005038265306122449,FALSE,-1e-5,1.0459183673469388e-5,TRUE
-Lincoln,Male,65,74,0.017908,0.0017096938775510205,FALSE,-1.5e-5,1.0459183673469388e-5,FALSE
-Sublette,Male,65,74,0.016860999999999998,0.0023632653061224492,FALSE,-1e-5,1.0459183673469388e-5,TRUE
-Albany,Male,65,74,0.016844,0.0015127551020408163,FALSE,-1.2e-5,6.632653061224491e-6,FALSE
-Teton,Male,65,74,0.006832,0.0010502551020408165,FALSE,-1e-5,1.0459183673469388e-5,TRUE
-Wyoming,Male,75,84,0.057741999999999995,9.255102040816332e-4,FALSE,-3.4e-5,1.2244897959183674e-5,FALSE
-United States,Male,75,84,0.056594,3.9540816326530615e-5,FALSE,-2.2000000000000003e-5,6.3775510204081635e-6,FALSE
-Hot Springs,Male,75,84,0.076458,0.009242602040816327,FALSE,-3.4e-5,1.2244897959183674e-5,TRUE
-Big Horn,Male,75,84,0.072239,0.006390561224489798,FALSE,2e-6,6.632653061224491e-6,FALSE
-Campbell,Male,75,84,0.069989,0.005278571428571428,FALSE,-9e-6,7.908163265306122e-6,FALSE
-Niobrara,Male,75,84,0.06786600000000001,0.012880357142857143,FALSE,-3.4e-5,1.2244897959183674e-5,TRUE
-Fremont,Male,75,84,0.065606,0.0035793367346938785,FALSE,0,5.86734693877551e-6,FALSE
-Laramie,Male,75,84,0.064939,0.0024443877551020405,FALSE,1.8e-5,2.193877551020408e-5,FALSE
-Sweetwater,Male,75,84,0.06423,0.004277551020408164,FALSE,-4e-6,5.86734693877551e-6,FALSE
-Natrona,Male,75,84,0.06383899999999999,0.0029150510204081627,FALSE,-5e-6,5.357142857142857e-6,FALSE
-Uinta,Male,75,84,0.06299300000000001,0.005786734693877551,FALSE,-2e-6,6.3775510204081635e-6,FALSE
-Converse,Male,75,84,0.062525,0.006411734693877553,FALSE,2e-6,1.0459183673469388e-5,FALSE
-Goshen,Male,75,84,0.062253,0.005486989795918366,FALSE,-2e-6,8.418367346938775e-6,FALSE
-Washakie,Male,75,84,0.059549,0.006653061224489797,FALSE,-1.1000000000000001e-5,7.653061224489796e-6,FALSE
-Platte,Male,75,84,0.058323999999999994,0.005893367346938775,FALSE,-9e-6,1.0714285714285714e-5,FALSE
-Weston,Male,75,84,0.057823,0.008339795918367346,FALSE,-3.4e-5,1.2244897959183674e-5,TRUE
-Crook,Male,75,84,0.050622,0.007015306122448979,FALSE,-3.4e-5,1.2244897959183674e-5,TRUE
-Carbon,Male,75,84,0.050348000000000004,0.0053403061224489784,FALSE,-5e-6,7.653061224489796e-6,FALSE
-Johnson,Male,75,84,0.050296,0.005653571428571428,FALSE,-1.3000000000000001e-5,9.438775510204082e-6,FALSE
-Sheridan,Male,75,84,0.050053,0.003282653061224488,FALSE,-1.8e-5,5.612244897959184e-6,FALSE
-Park,Male,75,84,0.049348,0.0030900510204081638,FALSE,-8e-6,7.908163265306122e-6,FALSE
-Lincoln,Male,75,84,0.049228999999999995,0.004641581632653062,FALSE,-1.5e-5,7.397959183673469e-6,FALSE
-Albany,Male,75,84,0.041287,0.0037395408163265298,FALSE,-1.4e-5,9.438775510204082e-6,FALSE
-Sublette,Male,75,84,0.033843000000000005,0.004892857142857142,FALSE,-3.4e-5,1.2244897959183674e-5,TRUE
-Teton,Male,75,84,0.02924,0.00356811224489796,FALSE,-3.4e-5,1.2244897959183674e-5,TRUE
-Wyoming,Male,85,Inf,0.165538,0.0029581632653061236,FALSE,-3.5e-5,1.3775510204081634e-5,FALSE
-United States,Male,85,Inf,0.16886900000000002,1.252551020408219e-4,FALSE,-1.7e-5,7.908163265306124e-6,FALSE
-Crook,Male,85,Inf,0.20763,0.028573724489795917,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Weston,Male,85,Inf,0.18973,0.024948979591836735,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Natrona,Male,85,Inf,0.189003,0.00897168367346939,FALSE,1.6e-5,1.989795918367347e-5,FALSE
-Converse,Male,85,Inf,0.18817799999999998,0.0204295918367347,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Laramie,Male,85,Inf,0.186025,0.007594642857142854,FALSE,3.9e-5,2.8061224489795918e-5,FALSE
-Sheridan,Male,85,Inf,0.183472,0.01222908163265306,FALSE,0,7.397959183673469e-6,FALSE
-Albany,Male,85,Inf,0.18134099999999997,0.01609107142857143,FALSE,-2e-6,6.887755102040817e-6,FALSE
-Campbell,Male,85,Inf,0.17480099999999998,0.016131122448979594,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Platte,Male,85,Inf,0.174091,0.01871760204081633,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Big Horn,Male,85,Inf,0.172964,0.019225510204081635,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Sweetwater,Male,85,Inf,0.17237599999999997,0.014889540816326532,FALSE,-1.2e-5,6.122448979591837e-6,FALSE
-Fremont,Male,85,Inf,0.16275,0.01096811224489796,FALSE,-9e-6,7.39795918367347e-6,FALSE
-Hot Springs,Male,85,Inf,0.16177,0.023132397959183672,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Lincoln,Male,85,Inf,0.15788,0.017563265306122452,FALSE,-1.54e-4,6.173469387755103e-5,FALSE
-Carbon,Male,85,Inf,0.146163,0.017541071428571434,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Park,Male,85,Inf,0.145312,0.010388520408163264,FALSE,-6e-6,5.86734693877551e-6,FALSE
-Uinta,Male,85,Inf,0.142017,0.01692780612244898,FALSE,-1.2e-5,1.3520408163265305e-5,FALSE
-Goshen,Male,85,Inf,0.135845,0.014260459183673474,FALSE,-7e-6,8.163265306122448e-6,FALSE
-Washakie,Male,85,Inf,0.135288,0.018391581632653062,FALSE,-1.3000000000000001e-5,1.096938775510204e-5,FALSE
-Sublette,Male,85,Inf,0.12662700000000002,0.019481632653061225,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Niobrara,Male,85,Inf,0.115925,0.025661989795918362,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Johnson,Male,85,Inf,0.10347,0.01366658163265306,FALSE,-3.5e-5,1.3775510204081634e-5,TRUE
-Teton,Male,85,Inf,0.098478,0.012717857142857142,FALSE,-9.199999999999999e-5,6.147959183673469e-5,FALSE
--- a/Data/Cleaned_Data/Wyoming_City_Population.Rds
+++ b/Data/Cleaned_Data/Wyoming_City_Population.Rds
--- a/Data/Cleaned_Data/Wyoming_City_Population.csv
+++ b/Data/Cleaned_Data/Wyoming_City_Population.csv
--- a/Data/Cleaned_Data/Wyoming_County_Demographic_Data.Rds
+++ b/Data/Cleaned_Data/Wyoming_County_Demographic_Data.Rds
--- a/Data/Cleaned_Data/Wyoming_County_Demographic_Data.csv
+++ b/Data/Cleaned_Data/Wyoming_County_Demographic_Data.csv
--- a/Data/Cleaned_Data/Wyoming_County_Population.Rds
+++ b/Data/Cleaned_Data/Wyoming_County_Population.Rds
--- a/Data/Cleaned_Data/Wyoming_County_Population.csv
+++ b/Data/Cleaned_Data/Wyoming_County_Population.csv
--- a/Data/Raw_Data/ACS_Demographics/API_CENSUS_CODES.csv
+++ b/Data/Raw_Data/ACS_Demographics/API_CENSUS_CODES.csv
--- a/Data/Raw_Data/Mortality_Rates/README_MORTALITY_DATA.txt
+++ b/Data/Raw_Data/Mortality_Rates/README_MORTALITY_DATA.txt
@ -7,4 +7,4 @@ Each file is single age group, so age weighting does not apply despite the varia

 These files must be manually downloaded because there is a filter feature  on the web page that is used to select the county and age. While there may be a way to scrape the data with code the trade off on my time was not worth it. Future runs will need to check these records, and can download the files to match this directory structure, in order to process a update in death rates.

-Valid data as of Nov 6 2025 Alex Gebben
+Valid data as of Nov 6 2025 Alex Gebben
--- a/Scripts/1A_Download_and_Process_Population_Data.r
+++ b/Scripts/1A_Download_and_Process_Population_Data.r
--- a/Scripts/1B_Process_Existing_NIH_Mortality_Data.r
+++ b/Scripts/1B_Process_Existing_NIH_Mortality_Data.r
@ -82,18 +82,17 @@ LIN_MORTALITY <- MORTALITY_DATA_ALL  %>% filter(County=="Lincoln")
 	write_csv(LIN_MORTALITY,paste0(CSV_SAVE,"/Lincoln_County_Mortality_Rates.csv" ))
 #Create a short readme files to make the data sources more clear
 	#Save a raw data readme
-sink(file=paste0(SAVE_LOC_RAW,"/README_MORTALITY_DATA.txt"),append=FALSE)
-	cat("Data files gathered manually from:\n")
-	cat("https://hdpulse.nimhd.nih.gov/data-portal/mortality/table?cod=247&cod_options=cod_15&ratetype=aa&ratetype_options=ratetype_2&race=00&race_options=race_6&sex=2&sex_options=sex_3&age=177&age_options=age_11&ruralurban=0&ruralurban_options=ruralurban_3&yeargroup=5&yeargroup_options=year5yearmort_1&statefips=56&statefips_options=area_states&county=56000&county_options=counties_wyoming&comparison=counties_to_us&comparison_options=comparison_counties&radio_comparison=areas&radio_comparison_options=cods_or_areas\n")
-	cat("\nEach file is single age group, so age weighting does not apply despite the variable names\n")
+sink(file=paste0(SAVE_LOC_RAW_MORT ,"/README_MORTALITY_DATA.txt"),append=FALSE)
+cat("Data files gathered manually from:
+
+National Institute of Health HDPules: An Ecosystem of Health Disparities and Minority Health Resources at
+https://hdpulse.nimhd.nih.gov/data-portal/mortality/table?cod=247&cod_options=cod_15&ratetype=aa&ratetype_options=ratetype_2&race=00&race_options=race_6&sex=2&sex_options=sex_3&age=177&age_options=age_11&ruralurban=0&ruralurban_options=ruralurban_3&yeargroup=5&yeargroup_options=year5yearmort_1&statefips=56&statefips_options=area_states&county=56000&county_options=counties_wyoming&comparison=counties_to_us&comparison_options=comparison_counties&radio_comparison=areas&radio_comparison_options=cods_or_areas
+
+Each file is single age group, so age weighting does not apply despite the variable names. Each age group file is named sequentially with a prefix “A_” for the first age group and a prefix “I_” for the oldest. There are separate directories for each sex. The cleaning script uses this directory structure to extract the right files and merge them into one data set.
+
+These files must be manually downloaded because there is a filter feature  on the web page that is used to select the county and age. While there may be a way to scrape the data with code the trade off on my time was not worth it. Future runs will need to check these records, and can download the files to match this directory structure, in order to process a update in death rates.
+
+Valid data as of Nov 6 2025 Alex Gebben")
 sink()


-
-	#Save a processed raw data readme
-sink(file=paste0(SAVE_MORT_LOC,"/README_MORTALITY_DATA.txt"),append=FALSE)
-	cat("This is a processed file of NIH death rates by age and county. Data files first gathered manually from:\n")
-	cat("\nhttps://hdpulse.nimhd.nih.gov/data-portal/mortality/table?cod=247&cod_options=cod_15&ratetype=aa&ratetype_options=ratetype_2&race=00&race_options=race_6&sex=2&sex_options=sex_3&age=177&age_options=age_11&ruralurban=0&ruralurban_options=ruralurban_3&yeargroup=5&yeargroup_options=year5yearmort_1&statefips=56&statefips_options=area_states&county=56000&county_options=counties_wyoming&comparison=counties_to_us&comparison_options=comparison_counties&radio_comparison=areas&radio_comparison_options=cods_or_areas\n")
-	cat("\nThese manually saved files are in the raw data directory. Each file is single age group, so age weighting does not apply despite the variable names\n")
-sink()
-
--- a/Scripts/1C_Download_and_Process_Demographic_Data.r
+++ b/Scripts/1C_Download_and_Process_Demographic_Data.r
@ -6,13 +6,16 @@ library(readxl)
 	if(!exists("SAVE_LOC_RAW")){SAVE_LOC_RAW <-"./Data/Raw_Data/"}
 	RAW_DEMO_LOC <- paste0(SAVE_LOC_RAW,"Demographics/") 
 	dir.create(RAW_DEMO_LOC, recursive = TRUE, showWarnings = FALSE)
+	if(!exists("SAVE_LOC_RAW_POP")){SAVE_LOC_RAW_POP <-"./Data/Raw_Data/Population"}
+	dir.create(SAVE_LOC_RAW_POP, recursive = TRUE, showWarnings = FALSE)
+
 	#Demographic Reference data
 	if(!exists("SAVE_LOC_REF")){SAVE_LOC_REF <-paste0(RAW_DEMO_LOC,"Reference_Material_for_Demographics/")}
 	dir.create(SAVE_LOC_REF, recursive = TRUE, showWarnings = FALSE)


 	#Start a README file for the raw downloaded demographic data
-		sink(file=paste0(RAW_DEMO_LOC,"README_DEMOGRAPHIC_DATA.txt"),append=FALSE)
+		sink(file=paste0(RAW_DEMO_LOC,"/README_DEMOGRAPHIC_DATA.txt"),append=FALSE)
 			cat("Demographic data used to find age and sex distribution of county populations\n")
 		sink()
 #####Gather data
@ -83,7 +86,7 @@ DEM_DATA <- rbind(DEM_2020,DEM_DATA) %>% ungroup %>% arrange(Year,Age)  %>% uniq
 	saveRDS(LIN_DEM,paste0(RDS_SAVE,"/Full_Lincoln_County_Demographics.Rds" ))
 	write_csv(LIN_DEM,paste0(CSV_SAVE,"/Full_Lincoln_County_Demographics.csv" ))
 run_datetime <- format(Sys.time(), "%Y-%m-%d %H:%M:%S")
-sink(file=paste0(SAVE_LOC_RAW_POP,"README_POPULATION_DATA.txt"),append=TRUE)
+sink(file=paste0(SAVE_LOC_RAW_POP,"/README_POPULATION_DATA.txt"),append=TRUE)
 	cat(paste0("\n--- Run Date: ", run_datetime, " ---\n"))
 sink()

--- a/Scripts/1D_Use_ACS_Census_Data_to_Estimate_Kemmerer_Demographics.r
+++ b/Scripts/1D_Use_ACS_Census_Data_to_Estimate_Kemmerer_Demographics.r
@ -1,7 +1,8 @@
-#library(tidyverse);setwd("../")
+library(tidyverse)
 library(tidycensus)
 library(zipcodeR)
-source("Scripts/Get_Sim_Intial_Demographic_Data.r")
+#setwd("../")
+source("./Scripts/Load_Custom_Functions/Functions_To_Create_Lincoln_Demographic_Data_Using_ACS.r")
 if(!exists("SAVE_DEMO_LOC")){SAVE_DEMO_LOC <-"./Data/Cleaned_Data/Demographic_Sex_Age_Data"}
 ACS_END_YEAR <- 2023 #most recent in package as of Nov 4 2025
 #Pull the relevant median age variables the value moe (margine of error) can be converted to standard error, following the link below
@ -16,7 +17,7 @@ ACS_END_YEAR <- 2023 #most recent in package as of Nov 4 2025
 PROJ_TRACTS <- get_tracts(search_city('Kemmerer','WY')$zipcode)  %>% full_join(get_tracts(search_city('Diamondville','WY')$zipcode))
 PROJ_TRACTS <- PROJ_TRACTS  %>% select(GEOID) %>% mutate('IN_KEM'=1) %>% mutate(GEOID=as.character(GEOID))
 ###Load data manually created which links vairable names to sex-age census data
-CODES <- read_csv("Data/API_CENSUS_CODES.csv",skip=1) %>% mutate(Med_Age=(Min_Age+Max_Age)/2) %>% rename(variable=Code)
+CODES <- read_csv("./Data/Raw_Data/ACS_Demographics/API_CENSUS_CODES.csv",skip=1) %>% mutate(Med_Age=(Min_Age+Max_Age)/2) %>% rename(variable=Code)
 #Testing age Comparison between the two
 ###Extract census data for all tracts in Lincoln county, clean up the data, and indicate if the tract is in Kemmerer/Diamondvile or not.
 DEMO_DATA_ALL <- do.call(rbind,lapply(2009:ACS_END_YEAR,MAKE_KEM_DEMO_DATA_YEAR))
--- a/Scripts/2A_Birth_Rate_Regression.r
+++ b/Scripts/2A_Birth_Rate_Regression.r
@ -0,0 +1,85 @@
+library(tidyverse)
+library(fixest)
+#setwd("../")
+##########################Model Population Trends
+##Run Regression 
+#Pull in Demographic data and create categories for key groups in the regression, male/female population with high fertility, children under one and two (but not zero). This data is broken down by each age group so aggregate to the county, year level for the final regression.
+#Fertility age bounds were informed by the regression found in the file ./Scripts/Other_Analysis/Select_Range_of_Male_Female_Fertility.r Which qualitatively supports that the number of people in these age ranges (18-28 Women, 18-30 Men) have the most significance in predicting birth rates. These two are combined into one variable which represent the minimum number of people in the key fertility window between the sexes, this is the binding fertility constraint and has more explanatory power than including either the number of men or women in the fertility window alone, providing a good trade off for including more variables or reducing variance.
+if(!exists("DEMOGRAPHIC_COUNTY_LOC")){DEMOGRAPHIC_COUNTY_LOC <- "./Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/All_Wyoming_Counties_Demographics.Rds"}
+if(!exists("DEMOGRAPHIC_KEM_LOC")){DEMOGRAPHIC_KEM_LOC <- "./Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Kemmerer_Diamondville_Demographics.Rds"}
+if(!exists("DEMOGRAPHIC_OTHER_LIN_LOC")){DEMOGRAPHIC_OTHER_LIN_LOC <- "./Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Other_Lincoln_Demographics.Rds"}
+
+if(!exists("POPULATION_COUNTY_LOC")){POPULATION_COUNTY_LOC <- "./Data/Cleaned_Data/Population_Data/RDS/All_Wyoming_County_Populations.Rds"}
+if(!exists("POPULATION_CITY_LOC")){POPULATION_CITY_LOC <- "./Data/Cleaned_Data/Population_Data/RDS/All_Wyoming_City_Populations.Rds"}
+
+
+#Function to make the data consistent for each data set used to run a birth simulation in the Monte Carlo
+#DEMO_DATA <- readRDS(DEMOGRAPHIC_KEM_LOC);POP_DATA <- readRDS(POPULATION_CITY_LOC) 
+MAKE_REG_DATA <- function(DEMO_DATA){
+	return(DEMO_DATA %>% mutate(Male_Window=Age>=18 & Age<=30,Female_Window=Age>=18 & Age<=28) %>% group_by(County,Region,Year) %>% summarize(Female_Birth_Group=sum(Num_Female*Female_Window,na.rm=TRUE),Male_Birth_Group=sum(Num_Male*Male_Window,na.rm=TRUE),Min_Birth_Group=ifelse(Female_Birth_Group<Male_Birth_Group,Female_Birth_Group,Male_Birth_Group)) %>% ungroup)
+}
+DEMOGRAPHIC_COUNTY_DATA <- readRDS(DEMOGRAPHIC_COUNTY_LOC)
+COUNTY_POP <- readRDS(POPULATION_COUNTY_LOC)
+REG_DATA <- readRDS(POPULATION_COUNTY_LOC) %>% full_join(MAKE_REG_DATA(DEMOGRAPHIC_COUNTY_DATA))
+  REG_DATA <- REG_DATA %>% group_by(County,Region) %>% mutate(PREV_BIRTH=lag(Births),PREV_TWO_BIRTH=lag(Births,2)) %>% ungroup 
+  REG_DATA <-  REG_DATA %>% select(-Female_Birth_Group,-Male_Birth_Group) #Store the data set of only the first year needing a birth forecast, to start the birth Monte Carlo simulations.
+###Some of the years are missing births, previous births etc. Where missing fill this in by assuming all age zero children in the demographic  data (DEMOGRAPHIC_LOC) were born in the last year. This makes a more complete data set. Some test find a near perfect 1 to 1 with this method 
+  	#Data to fill in the missing records
+	FILL_IN_DATA <- DEMOGRAPHIC_COUNTY_DATA %>% mutate(POP=Num_Male+Num_Female,BIRTHS=ifelse(Age==0,POP,0)) %>% group_by(County,Region,Year)  %>% summarize(BIRTHS=sum(BIRTHS)) %>% arrange(County,Year) %>% mutate(ALT=lag(BIRTHS),ALT2=lag(BIRTHS,2)) %>% ungroup
+#Join and replace missing records
+REG_DATA <- REG_DATA %>% left_join(FILL_IN_DATA )  %>% mutate(Births=ifelse(is.na(Births),BIRTHS,Births),PREV_BIRTH=ifelse(is.na(PREV_BIRTH),ALT,PREV_BIRTH),PREV_TWO_BIRTH=ifelse(is.na(PREV_TWO_BIRTH),ALT2,PREV_TWO_BIRTH)) %>% select(-BIRTHS,-ALT,-ALT2) %>% select(Year,County,Region,everything()) %>% mutate(Region=County)
+	
+###Working on Kemmerer data
+DEMOGRAPHIC_KEM_DATA <-  readRDS(DEMOGRAPHIC_KEM_LOC)
+readRDS(POPULATION_CITY_LOC) %>% filter(City %in% c("Kemmerer","Diamondville")) %>% group_by(Year) %>% mutate(Population=sum(Population,na.rm=TRUE)) %>% mutate(City='Kemmerer') %>% rename(Region=City)
+MAKE_REG_DATA(readRDS(DEMOGRAPHIC_KEM_LOC))
+REG_DATA
+readRDS(DEMOGRAPHIC_KEM_LOC)%>% mutate(POP=Num_Male+Num_Female,Births=ifelse(Age==0,POP,0)) %>% group_by(County,Region,Year)  %>% summarize(Births=sum(Births)) %>% arrange(County,Year) %>% mutate(PREV_BIRTH=lag(Births),PREV_TWO_BIRTH=lag(Births,2)) %>% ungroup
+
+
+	 readRDS(DEMOGRAPHIC_KEM_LOC)%>% mutate(Male_Window=Age>=18 & Age<=30,Female_Window=Age>=18 & Age<=28) %>% group_by(County,Year) %>% summarize(Female_Birth_Group=sum(Num_Female*Female_Window,na.rm=TRUE),Male_Birth_Group=sum(Num_Male*Male_Window,na.rm=TRUE),Min_Birth_Group=ifelse(Female_Birth_Group<Male_Birth_Group,Female_Birth_Group,Male_Birth_Group)) %>% ungroup
+DEMOGRAPHIC_DATA 
+TEST <- readRDS(POPULATION_COUNTY_LOC) 
+if(!("Births" %in% colnames(TEST)))
+"Deaths" %in% colnames(TEST)
+"Migration" %in% colnames(TEST)
+"Migration" %in% colnames(TEST)
+
+
+readRDS(DEMOGRAPHIC_COUNTY_LOC)
+readRDS(POPULATION_COUNTY_LOC) 
+COUNTY_REG_DATA <-  MAKE_REG_DATA(readRDS(DEMOGRAPHIC_COUNTY_LOC),readRDS(POPULATION_COUNTY_LOC)  )
+readRDS(DEMOGRAPHIC_KEM_LOC)
+readRDS(POPULATION_CITY_LOC) %>% 
+readRDS(DEMOGRAPHIC_KEM_LOC)
+readRDS(DEMOGRAPHIC_KEM_LOC)
+  MAKE_REG_DATA(readRDS(DEMOGRAPHIC_KEM_LOC),readRDS(POPULATION_CITY_LOC)  ) %>% filter(!is.na(Region)) %>% pull(Region) %>% unique
+%>% pull(Region) %>% unique
+%>% filter(Region=='Kemmerer') 
+
+readRDS(POPULATION_CITY_LOC)
+MAKE_REG_DATA(readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Kemmerer_Diamondville_Demographics.Rds"),readRDS("Data/Cleaned_Data/Population_Data/RDS/All_Wyoming_City_Populations.Rds"))
+
+  #Extract the population trend data to connect with demographics (Population,births,deaths)
+  POP_DATA <- readRDS(POPULATION_LOC) 
+  #Merger the two data sets and drop any records that cannot be used in the regression (this makes the "predict" function output the right number of records)
+  REG_DATA <- POP_DATA %>% full_join(DEMOGRAPHIC_DATA) 
+
+
+###Predict the number of Births
+MOD_BIRTHS <- feols(log(Births)~log(PREV_BIRTH)+log(PREV_TWO_BIRTH)+log(Min_Birth_Group)+Year*County,cluster=~Year+County, data=REG_DATA ) #Higher AIC but worse acf
+#MOD_BIRTHS_ALT <- feols(log(Births)~log(PREV_BIRTH)+log(Min_Birth_Group)+Year*County,cluster=~Year+County, data=REG_DATA )
+#AIC(MOD_BIRTHS)<AIC(MOD_BIRTHS_ALT)
+ #Optional: Review the ACF and PACF for validity. Model made on October 24nd appears to have uncorrelated lags of residuals accept year three.
+#acf(resid(MOD_BIRTHS))
+#acf(resid(MOD_BIRTHS_ALT))
+  if(!exists("SAVE_REG_LOC")){SAVE_REG_LOC <- "Data/Intermediate_Inputs"}
+ dir.create(SAVE_REG_LOC , recursive = TRUE, showWarnings = FALSE)
+
+
+saveRDS(REG_DATA,SAVE_REG_LOC(paste0(SAVE_REG_LOC,"/Birth_Regression_Data_Set.Rds")))
+  saveRDS(FIRST_PREDICT_YEAR_POPULATION_DATA,START_DEMOGRAPHIC_DATA) #Save the cleaned data set for later use when starting the simulation.
+  #Cleanup data no longer needed, and save some RAM
+	  rm(POP_DATA,DEMOGRAPHIC_DATA,REG_DATA)
+	  gc()
+
--- a/Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r
+++ b/Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r
--- a/Scripts/Load_Custom_Functions/Death_Simulation_Functions.r
+++ b/Scripts/Load_Custom_Functions/Death_Simulation_Functions.r
--- a/Scripts/Load_Custom_Functions/Functions_To_Create_Lincoln_Demographic_Data_Using_ACS.r
+++ b/Scripts/Load_Custom_Functions/Functions_To_Create_Lincoln_Demographic_Data_Using_ACS.r
@ -18,8 +18,8 @@ GET_ACS_LIN_DATA <- function(ACS_YEAR,ACS_CODES=CODES,CENSUS_TRACTS=PROJ_TRACTS)
 	return(AGE_DATA)
 }
 ####################Loop to create data for each year. Projecting distribution of ages into Kemmerer, and returning a demographic distribution for Kemmerer/Diamondville in each year of the ACS (currently 2009 to 2023)
-MAKE_KEM_DEMO_DATA_YEAR <- function(ACS_YEAR){
-	LIN_DEMOGRAPHICS <-    	readRDS("Data/Cleaned_Data/Lincoln_Demographic_Data.Rds") %>% filter(Year==ACS_YEAR)
+MAKE_KEM_DEMO_DATA_YEAR <- function(ACS_YEAR,LIN_DEMOGRAPHOC_DATA_LOCATION="./Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/Full_Lincoln_County_Demographics.Rds"){
+	LIN_DEMOGRAPHICS <-    	readRDS(LIN_DEMOGRAPHOC_DATA_LOCATION) %>% filter(Year==ACS_YEAR)
 	AGE_DATA <- GET_ACS_LIN_DATA(ACS_YEAR)
 	for(i in 1:nrow(AGE_DATA)){
 		if(i==1 & exists("RES")){rm(RES)}
--- a/Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r
+++ b/Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r
--- a/Scripts/Load_Custom_Functions/Monte_Carlo_Functions.r
+++ b/Scripts/Load_Custom_Functions/Monte_Carlo_Functions.r
--- a/Scripts/Load_Custom_Functions/Mortality_Rate_Over_Time_Simulation_Function.r
+++ b/Scripts/Load_Custom_Functions/Mortality_Rate_Over_Time_Simulation_Function.r