Cleanup and working on birth reg
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# ---> R
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#
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Data/Raw_Data/Population/
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Data/Raw_Data/Demographics/
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*.png
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*.csv
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Data/Cleaned_Data/
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##########################Model Population Trends
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##Run Regression
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#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.
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#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.
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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))
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#Extract the population trend data to connect with demographics (Population,births,deaths)
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POP_DATA <- readRDS("Data/Cleaned_Data/Wyoming_County_Population.Rds") %>% mutate(LN=ifelse(County=="Lincoln",1,0))
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#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)
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REG_DATA <- POP_DATA %>% full_join(DEMOGRAPHIC_DATA)
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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))
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REG_DATA$County <- factor(REG_DATA$County)
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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.
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REG_DATA <- REG_DATA %>% filter(!is.na(Births)) #Remove any values with missing births for a simpler regression which includes only complete data
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###Predict the number of Births
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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
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#AIC(MOD_BIRTHS)
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#MOD_BIRTHS <- feols(log(Births)~log(PREV_BIRTH)+log(Min_Birth_Group)+Year*County,cluster=~Year+County, data=REG_DATA )
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#AIC(MOD_BIRTHS)
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#Optional: Review the ACF and PACF for validity. Model made on October 24nd appears to have uncorrelated lags of residuals accept year three.
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RES_DATA <- REG_DATA #Data to create visuals with, without changing the main file. Can be used for ggplot, or residual tests
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RES_DATA$RESID <- resid(MOD_BIRTHS)
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acf(RES_DATA %>% pull(RESID))
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pacf(RES_DATA %>% pull(RESID))
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saveRDS(RES_DATA,"Data/Regression_Results/Birth_Regression_Data_Set.Rds")
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saveRDS(MOD_BIRTHS,BIRTH_RATE_REG_RESULTS)
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saveRDS(FIRST_PREDICT_YEAR_POPULATION_DATA,START_DEMOGRAPHIC_DATA) #Save the cleaned data set for later use when starting the simulation.
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#Cleanup data no longer needed, and save some RAM
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rm(POP_DATA,DEMOGRAPHIC_DATA,REG_DATA)
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gc()
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@ -1,87 +0,0 @@
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County,Year,Age,Num_Male,Num_Female
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Kemmerer,2024,0,10,19
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Kemmerer,2024,1,11,26
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Kemmerer,2024,2,10,22
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Kemmerer,2024,3,12,26
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Kemmerer,2024,4,10,24
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Kemmerer,2024,5,15,17
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Kemmerer,2024,6,19,19
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Kemmerer,2024,7,21,22
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Kemmerer,2024,8,17,19
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Kemmerer,2024,9,21,20
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Kemmerer,2024,10,23,20
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Kemmerer,2024,11,25,20
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Kemmerer,2024,12,25,19
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Kemmerer,2024,13,24,23
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Kemmerer,2024,14,27,24
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Kemmerer,2024,15,14,13
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Kemmerer,2024,16,14,13
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Kemmerer,2024,17,15,12
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Kemmerer,2024,18,8,8
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Kemmerer,2024,19,7,6
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Kemmerer,2024,20,9,28
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Kemmerer,2024,21,0,6
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Kemmerer,2024,22,10,16
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Kemmerer,2024,23,9,14
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Kemmerer,2024,24,11,14
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Kemmerer,2024,25,14,14
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Kemmerer,2024,26,13,15
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Kemmerer,2024,27,13,14
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Kemmerer,2024,28,9,14
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Kemmerer,2024,29,13,16
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Kemmerer,2024,30,10,16
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Kemmerer,2024,31,11,16
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Kemmerer,2024,32,9,17
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Kemmerer,2024,33,10,17
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Kemmerer,2024,34,10,21
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Kemmerer,2024,35,23,14
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Kemmerer,2024,36,24,15
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Kemmerer,2024,37,21,16
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Kemmerer,2024,38,23,17
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Kemmerer,2024,39,29,16
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Kemmerer,2024,40,18,12
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Kemmerer,2024,41,18,13
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Kemmerer,2024,42,17,13
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Kemmerer,2024,43,18,15
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Kemmerer,2024,44,20,15
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Kemmerer,2024,45,10,21
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Kemmerer,2024,46,11,17
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Kemmerer,2024,47,11,17
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Kemmerer,2024,48,10,16
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Kemmerer,2024,49,8,17
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Kemmerer,2024,50,12,17
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Kemmerer,2024,51,13,19
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Kemmerer,2024,52,13,21
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Kemmerer,2024,53,12,21
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Kemmerer,2024,54,10,18
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Kemmerer,2024,55,27,18
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Kemmerer,2024,56,28,19
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Kemmerer,2024,57,25,20
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Kemmerer,2024,58,30,22
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Kemmerer,2024,59,33,22
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Kemmerer,2024,60,25,14
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Kemmerer,2024,61,26,15
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Kemmerer,2024,62,23,17
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Kemmerer,2024,63,23,17
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Kemmerer,2024,64,24,17
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Kemmerer,2024,65,18,31
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Kemmerer,2024,66,20,32
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Kemmerer,2024,67,32,32
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Kemmerer,2024,68,32,33
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Kemmerer,2024,69,29,31
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Kemmerer,2024,70,27,15
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Kemmerer,2024,71,26,15
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Kemmerer,2024,72,22,13
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Kemmerer,2024,73,20,12
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Kemmerer,2024,74,20,12
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Kemmerer,2024,75,27,9
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Kemmerer,2024,76,32,9
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Kemmerer,2024,77,19,5
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Kemmerer,2024,78,17,5
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Kemmerer,2024,79,16,5
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Kemmerer,2024,80,8,6
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Kemmerer,2024,81,5,5
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Kemmerer,2024,82,4,5
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Kemmerer,2024,83,4,4
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Kemmerer,2024,84,3,4
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Kemmerer,2024,85,21,18
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Year,County,Population,Births,Deaths,Migration,Min_Birth_Group,PREV_BIRTH,PREV_TWO_BIRTH
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2024,Kemmerer,2895,NA,NA,NA,126,29,37
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County,Sex,Min_Age,Max_Age,Death_Rate,Rate_SD,Imparted_Rate,Trend,Trend_SD,Imparted_Trend
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Lincoln,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Lincoln,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Lincoln,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
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Lincoln,Female,20,39,0.001086,5.5867346938775525e-5,TRUE,1e-5,4.3367346938775506e-6,TRUE
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Lincoln,Female,40,64,0.0033410000000000002,4.6096938775510205e-4,FALSE,9e-6,9.948979591836735e-6,FALSE
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Lincoln,Female,65,74,0.009472999999999999,0.0012964285714285714,FALSE,-1.7e-5,1.6326530612244897e-5,TRUE
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Lincoln,Female,75,84,0.043425,0.004336734693877551,FALSE,-2e-6,8.418367346938775e-6,FALSE
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Lincoln,Female,85,Inf,0.145245,0.013969642857142858,FALSE,-6e-6,6.3775510204081635e-6,FALSE
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Lincoln,Male,0,0,0.005242,5.926020408163265e-4,TRUE,-1e-5,2.040816326530612e-6,TRUE
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Lincoln,Male,1,9,2.07e-4,3.775510204081633e-5,TRUE,4e-5,8.673469387755103e-6,TRUE
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Lincoln,Male,10,19,7.440000000000001e-4,6.224489795918366e-5,TRUE,-1e-6,8.673469387755103e-6,TRUE
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Lincoln,Male,20,39,0.00293,5.543367346938776e-4,FALSE,5.6e-5,3.214285714285715e-5,TRUE
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Lincoln,Male,40,64,0.005426,5.724489795918367e-4,FALSE,5e-6,8.163265306122448e-6,FALSE
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Lincoln,Male,65,74,0.017908,0.0017096938775510205,FALSE,-1.5e-5,1.0459183673469388e-5,FALSE
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Lincoln,Male,75,84,0.049228999999999995,0.004641581632653062,FALSE,-1.5e-5,7.397959183673469e-6,FALSE
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Lincoln,Male,85,Inf,0.15788,0.017563265306122452,FALSE,-1.54e-4,6.173469387755103e-5,FALSE
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County,Sex,Min_Age,Max_Age,Death_Rate,Rate_SD,Imparted_Rate,Trend,Trend_SD,Imparted_Trend
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Wyoming,Female,0,0,0.005673999999999999,6.415816326530612e-4,FALSE,5e-6,6.122448979591837e-6,TRUE
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United States,Female,0,0,0.005039,2.3724489795918395e-5,FALSE,5e-6,6.122448979591837e-6,FALSE
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Laramie,Female,0,0,0.00635,0.0016502551020408163,FALSE,5e-6,6.122448979591837e-6,TRUE
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Albany,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Big Horn,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Campbell,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Carbon,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Converse,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Crook,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Fremont,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Goshen,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Hot Springs,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Johnson,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Lincoln,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Natrona,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Niobrara,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Park,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Platte,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Sheridan,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Sublette,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Sweetwater,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Teton,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Uinta,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Washakie,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Weston,Female,0,0,0.005673999999999999,6.415816326530612e-4,TRUE,5e-6,6.122448979591837e-6,TRUE
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Wyoming,Female,1,9,1.79e-4,3.64795918367347e-5,FALSE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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United States,Female,1,9,1.5800000000000002e-4,1.2755102040816327e-6,FALSE,3.7999999999999995e-5,9.693877551020408e-6,FALSE
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Albany,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Big Horn,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Campbell,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Carbon,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Converse,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Crook,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Fremont,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Goshen,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Hot Springs,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Johnson,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Laramie,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Lincoln,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Natrona,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Niobrara,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Park,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Platte,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Sheridan,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Sublette,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Sweetwater,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Teton,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Uinta,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Washakie,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Weston,Female,1,9,1.79e-4,3.64795918367347e-5,TRUE,3.7999999999999995e-5,9.693877551020408e-6,TRUE
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Wyoming,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,FALSE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
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United States,Female,10,19,2.2600000000000002e-4,1.5306122448979538e-6,FALSE,2.2000000000000003e-5,4.846938775510204e-6,FALSE
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Albany,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
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Big Horn,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
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Campbell,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
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Carbon,Female,10,19,2.9800000000000003e-4,4.132653061224491e-5,TRUE,2.2000000000000003e-5,4.846938775510204e-6,TRUE
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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
|
||||
|
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|
Can't render this file because it has a wrong number of fields in line 2.
|
@ -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()
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -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))
|
||||
85
Scripts/2A_Birth_Rate_Regression.r
Normal file
85
Scripts/2A_Birth_Rate_Regression.r
Normal file
@ -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)
|
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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)
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MAKE_REG_DATA(readRDS(DEMOGRAPHIC_KEM_LOC))
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REG_DATA
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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
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||||
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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
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DEMOGRAPHIC_DATA
|
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TEST <- readRDS(POPULATION_COUNTY_LOC)
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if(!("Births" %in% colnames(TEST)))
|
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"Deaths" %in% colnames(TEST)
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"Migration" %in% colnames(TEST)
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||||
"Migration" %in% colnames(TEST)
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|
||||
|
||||
readRDS(DEMOGRAPHIC_COUNTY_LOC)
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readRDS(POPULATION_COUNTY_LOC)
|
||||
COUNTY_REG_DATA <- MAKE_REG_DATA(readRDS(DEMOGRAPHIC_COUNTY_LOC),readRDS(POPULATION_COUNTY_LOC) )
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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()
|
||||
|
||||
@ -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)}
|
||||
Loading…
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Reference in New Issue
Block a user