Fixed some bugs, started run
This commit is contained in:
Alex Gebben Work
parent
f02fa324df
commit
9d0cf742d8
@ -16,20 +16,24 @@ source("Scripts/Load_Custom_Functions/Single_Age_Mortality_Trend_Simulation.r")
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source("Scripts/Load_Custom_Functions/Induced_Migration_Functions.r")
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#######Preliminary Model Inputs
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YEARS_AHEAD <- 43
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ST_YEAR <- 2023
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YEARS_AHEAD <- 41
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ST_YEAR <- 2025
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################################Load Data
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DEMO <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2023_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
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DEMO <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
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BIRTH_MOD <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Birth_Regression.Rds")
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#Must add region as a factor with multiple levels for predict to work. Seems to check for multiple levels although that is not needed econometrics.
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BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Data/Birth_Simulation_Key_Starting_Points.Rds") %>% mutate(Region=factor(Region)) %>% filter(KEM==1,Year==2023)
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BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Data/Birth_Simulation_Key_Starting_Points.Rds") %>% mutate(Region=factor(Region)) %>% filter(KEM==1,Year==2023) %>% mutate(Year=2024)
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MIGRATION_ARIMA <- readRDS("Data/Intermediate_Inputs/Migration_ARIMA_Models/Kemmerer_Diamondville_Net_Migration_ARIMA.Rds")
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MIGRATION_ODDS <- readRDS("Data/Intermediate_Inputs/Migration_Trends/Migration_Age_Probability_Zero_to_85.Rds")
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####
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OPERATORS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Operating_Worker_Related_Migration.Rds")
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CONSTRUCTION <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Construction_Related_Migration.Rds")
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OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
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INDUCED_MIGRATION_MULTIPLIERS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Induced_Jobs.Rds")
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##############
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@ -89,27 +93,24 @@ TOTAL_POP <- sum(DEMO)
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return(list(DEMO,BIRTH_DATA,c(TOTAL_POP,TOTAL_BIRTHS,TOTAL_DEATHS,TOTAL_MIGRATION)))
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}
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MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
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SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_TOTAL,CONSTRUCTION_TOTAL,MIGRATION_MULTIPLIERS ){
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MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
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#DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_TOTAL,CONSTRUCTION_TOTAL,MIGRATION_MULTIPLIERS
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CONSTRUCTION_MIGRATION <- CONSTRUCTION_LIN_MIGRATION
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MIGRATION_MULTIPLIERS <- INDUCED_MIGRATION_MULTIPLIERS
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OPERATOR_MIGRATION <- OPERATOR_LIN_MIGRATION
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SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_MIGRATION,CONSTRUCTION_MIGRATION,MIGRATION_MULTIPLIERS ){
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TERRA_POWER_EFFECT <- rep(0,YEARS_AHEAD)
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OPERATOR_MIGRATION <-OPERATOR_TOTAL%>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_MIGRATION <- CONSTRUCTION_TOTAL%>% pull("Construction_Emp_Migrated")
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OPERATOR_MIGRATION <- LOCAL_WORK_ADJ(OPERATOR_MIGRATION ,0.85) #Assume between 85%-100% operators live in Kemmerer
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CONSTRUCTION_MIGRATION <- LOCAL_WORK_ADJ(CONSTRUCTION_MIGRATION,0.41) #Assume between 41%-100% operators live in Kemmerer
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OPERATOR_MIGRATION <- OPERATOR_MIGRATION %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_MIGRATION <- CONSTRUCTION_MIGRATION %>% pull("Construction_Emp_Migrated")
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CONSTRUCTION_MIGRATION[7] <- CONSTRUCTION_MIGRATION[7] - sum(CONSTRUCTION_MIGRATION )
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CONSTRUCTION_POPULATION_ADDED <- cumsum(CONSTRUCTION_MIGRATION)
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PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,RES)+OPERATOR_MIGRATION
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PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,MIGRATION_MULTIPLIERS)+OPERATOR_MIGRATION
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###############NOTE NEED TO USE THIS AT END TO ADJUST THE RESULTS WHILE LEAVING THE DEMOGRAPHIC MATRIX
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TEMP_TERRAPOWER_MIGRATION_<- TERRA_POWER_EFFECT+CONSTRUCTION_MIGRATION
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MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL)+runif(1,-55,0))+PERMANENT_TERRAPOWER_MIGRATION)
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TERRA_POWER_EFFECT[1:7] <- TERRA_POWER_EFFECT[1:7]+CONSTRUCTION_MIGRATION
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MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL))+runif(1,-55,0)+TERRA_POWER_EFFECT)
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#The runif applies a downshift ranging from the historic decline rate all the way to the Lincoln rate applied in the model
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FINAL_REPORT_VALUES <- matrix(NA,ncol=6,nrow=YEARS_AHEAD)
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@ -122,6 +123,8 @@ SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL
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BIRTH_DATA <- C_RES[[2]]
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FINAL_REPORT_VALUES[i,-1] <- c(C_YEAR,C_RES[[3]])
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}
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FINAL_REPORT_VALUES[1:7,3] <- as.numeric(FINAL_REPORT_VALUES[1:7,3])+CONSTRUCTION_POPULATION_ADDED
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FINAL_REPORT_VALUES[1:7,6] <- as.numeric(FINAL_REPORT_VALUES[1:7,6]) +CONSTRUCTION_MIGRATION
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return(FINAL_REPORT_VALUES)
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}
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@ -135,14 +138,18 @@ SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL
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TOTAL_SIMULATIONS <- 10^6
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N_RUNS <-ceiling(TOTAL_SIMULATIONS/BATCH_SIZE )
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SIM_RES_FILE <- paste0(RES_SIM_DIR,"Kemmerer_2023_Simulation.csv")
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SIM_RES_FILE <- paste0(RES_SIM_DIR,"Kemmerer_2024_Simulation.csv")
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NEW_RES_FILE <- !file.exists(SIM_RES_FILE)
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OPERATOR_LIN_MIGRATION <- OPERATORS %>% pull("Operator_Emp_Migrated")
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CONSTRUCTION_LIN_MIGRATION <- CONSTRUCTION %>% pull("Construction_Emp_Migrated")
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for(i in 1:N_RUNS){
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# MIGRATION_MATRIX <- simulate(nsim=BATCH_SIZE,MIGRATION_ARIMA,n=YEARS_AHEAD)
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MIGRATION_MATRIX <- do.call(cbind, mclapply(1:BATCH_SIZE,function(x)(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA) )),mc.cores = detectCores()-1))
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# rownames(MIGRATION_MATRIX) <- ST_YEAR:(ST_YEAR+YEARS_AHEAD-1)
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# colnames(MIGRATION_MATRIX) <- 1:BATCH_SIZE
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try(RES <- do.call(rbind,mclapply(1:BATCH_SIZE,function(x){SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA)},mc.cores=NCORES)))
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#SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)
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try(RES <- do.call(rbind,mclapply(1:BATCH_SIZE,function(x){SINGLE_SIM(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA,OPERATOR_LIN_MIGRATION,CONSTRUCTION_LIN_MIGRATION,INDUCED_MIGRATION_MULTIPLIERS)},mc.cores=NCORES)))
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if(exists("RES")){
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RES <- as.data.frame(RES)
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RES[,-1] <- as.numeric(as.matrix(RES[,-1]))
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@ -1,72 +1,56 @@
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library(tidyverse)
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###Process the simulations and save the main percentile results by year
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RES <- read_csv("Results/Simulations/Kemmerer_2023_Simulation.csv")
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RES[,"Year"] <- RES[,"Year"]+1
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RES<- RES %>% filter(!is.na(Year))
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RES <- RES %>% filter(!is.na(Population))
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RES <- read_csv("Results/Simulations/Kemmerer_2024_Simulation.csv")
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RES[,"Year"] <- RES[,"Year"]
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RES<- RES %>% filter(!is.na(Year))
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RES <- RES %>% filter(!is.na(Population))
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HIST <- readRDS("Data/Cleaned_Data/Population_Data/RDS/Kemmerer_Diamondville_Population_Data.Rds") %>% filter(County=='Lincoln') %>% mutate(Percentile="Actual Population") %>% filter(Year>=1940)
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YEARS <- min(RES$Year,na.rm=TRUE):max(RES$Year,na.rm=TRUE)
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######Population
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START_POP <- HIST %>% filter(Year==2023) %>% pull(Population)
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GRAPH_DATA <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Population),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
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GRAPH_DATA <- rbind(GRAPH_DATA[1,],GRAPH_DATA)
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GRAPH_DATA[1,] <-t(c(rep(START_POP,ncol(GRAPH_DATA)-1),min(GRAPH_DATA$Year)-1))
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FAN_DATA <- GRAPH_DATA
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GRAPH_DATA <- GRAPH_DATA %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Population")
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GRAPH_DATA$Percentile <- factor(GRAPH_DATA$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','50%','60%','75%','90%','95%','97.5%')))
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START_POP <- HIST %>% filter(Year==2025) %>% pull(Population)
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MEDIAN_PRED <- GRAPH_DATA %>% filter(Percentile=='50%')
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######Migration
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GRAPH_DATA_MIGRATION <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Net_Migration),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
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FAN_DATA_MIGRATION <- GRAPH_DATA_MIGRATION
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GRAPH_DATA_MIGRATION <- GRAPH_DATA_MIGRATION %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Migration")
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GRAPH_DATA_MIGRATION$Percentile <- factor(GRAPH_DATA_MIGRATION$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%')))
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MEDIAN_PRED_MIGRATION <- GRAPH_DATA_MIGRATION %>% filter(Percentile=='50%')
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######Mortalities
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GRAPH_DATA_MORTALITY <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Deaths),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
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FAN_DATA_MORTALITY <- GRAPH_DATA_MORTALITY
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GRAPH_DATA_MORTALITY <- GRAPH_DATA_MORTALITY %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Deaths")
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GRAPH_DATA_MORTALITY$Percentile <- factor(GRAPH_DATA_MORTALITY$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%')))
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MEDIAN_PRED_MORTALITY<- GRAPH_DATA_MORTALITY %>% filter(Percentile=='50%')
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######Births
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START_BIRTHS <- HIST %>% filter(Year==2024) %>% pull(Births)
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GRAPH_DATA_BIRTHS <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Births),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
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GRAPH_DATA_BIRTHS <- rbind(GRAPH_DATA_BIRTHS[1,],GRAPH_DATA_BIRTHS)
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GRAPH_DATA_BIRTHS[1,] <-t(c(rep(START_BIRTHS,ncol(GRAPH_DATA_BIRTHS)-1),min(GRAPH_DATA_BIRTHS$Year)-1))
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GRAPH_DATA_BIRTHS$Year <- GRAPH_DATA_BIRTHS$Year+1
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####Fan New
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GET_DATA <- function(RES,COL_NUM){
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FAN_DATA_BIRTHS <- GRAPH_DATA_BIRTHS
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GRAPH_DATA_BIRTHS<- GRAPH_DATA_BIRTHS %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Births")
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GRAPH_DATA_BIRTHS$Percentile <- factor(GRAPH_DATA_BIRTHS$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%')))
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MEDIAN_PRED_BIRTHS<- GRAPH_DATA_BIRTHS %>% filter(Percentile=='50%')
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YEARS <- min(RES$Year,na.rm=TRUE):max(RES$Year,na.rm=TRUE)
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FAN_DATA <- do.call(rbind,lapply(YEARS,function(x){quantile(as.numeric(t((RES %>% filter(Year==x))[,COL_NUM])),seq(0.01,0.99,by=0.01))})) %>% as_tibble %>% mutate(Year=YEARS)
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FAN_DATA <- rbind(FAN_DATA[1,],FAN_DATA)
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START_VALUE <- (HIST %>% filter(Year==2024))[,COL_NUM+1] %>% as.numeric
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FAN_DATA <- FAN_DATA %>% pivot_longer(colnames(FAN_DATA %>% select(-Year)),names_to="Percentile") %>% filter(Year>2024) %>% unique
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NUM_YEARS <- length(unique(FAN_DATA$Year) )
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FAN_DATA$Group <- rep(c(1:49,0,rev(1:49)),NUM_YEARS)
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FAN_DATA <- FAN_DATA %>% group_by(Year,Group) %>% summarize(MIN=min(value),MAX=max(value))
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TEMP <- FAN_DATA %>% filter(Year==2025) %>% mutate(Year=2024) %>% ungroup
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TEMP[,3:4] <- START_VALUE
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FAN_DATA <- rbind(TEMP,FAN_DATA %>% ungroup) %>% as_tibble
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return(FAN_DATA)
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}
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RES %>% pull(Sim_UUID) %>% unique %>% length()
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MAKE_GRAPH <- function(GRAPH_DATA,ALPHA=0.03,COLOR='cadetblue',LINE_WIDTH=1){
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PLOT <- ggplot(data=GRAPH_DATA)
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for(i in 1:49){
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C_DATA <- GRAPH_DATA%>% filter(Group==i)
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PLOT <- PLOT +geom_ribbon(data=C_DATA,aes(x=Year,ymin=MIN,ymax=MAX),alpha=ALPHA,fill=COLOR)
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#write_csv(GRAPH_DATA,PERCENTILE_DATA)
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#Add historic
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MEDIAN_PRED <- GRAPH_DATA %>% filter(Percentile=='50%')
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GRAPH_DATA <- GRAPH_DATA %>% filter(Percentile!='50%')
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ALPHA=0.2
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COLOR <- 'black'
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#GRAPH <-
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nrow(RES)/40
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POP_PLOT <- ggplot(data=GRAPH_DATA)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Population,group=Percentile,color=Percentile))+geom_line(data=HIST,aes(x=Year,y=Population),color='black',linewidth=0.75)+geom_line(data=MEDIAN_PRED,aes(x=Year,y=Population),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2065, by = 5)))+ scale_y_continuous(breaks = seq(0, 35000, by = 250))+theme_bw()+ggtitle("Kemmerer & Diamondville, Population Forecast")+ expand_limits( y = 0)
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}
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CI_90 <- rbind(GRAPH_DATA%>% filter(Group==20) %>% mutate(Interval='80%'),GRAPH_DATA%>% filter(Group==5) %>% mutate(Interval='95%'),GRAPH_DATA%>% filter(Group==0) %>% mutate(Interval='Median Prediction'))
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PLOT <- PLOT+geom_line(aes(x=Year,y=MIN,linetype=Interval,color=Interval),linewidth=LINE_WIDTH, data=CI_90)+geom_line(aes(x=Year,y=MAX,group=Interval,linetype=Interval,color=Interval),linewidth=LINE_WIDTH ,data=CI_90)+scale_color_manual(values=c("grey50","grey80","black"))+scale_linetype_manual(values = c("solid","solid","dotdash"))
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return(PLOT)
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}
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POP_DATA <- GET_DATA(RES,3)
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POP_PLOT <- MAKE_GRAPH(POP_DATA)
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POP_PLOT <- POP_PLOT+geom_line(data=HIST,aes(x=Year,y=Population),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2060, by = 10),2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 500))+ggtitle("Kemmerer & Diamondville, Population Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Population")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
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POP_PLOT
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ggsave("Kemmerer_Population_Fan_Chart.png",POP_PLOT )
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MIGRATION_PLOT <- ggplot(data=GRAPH_DATA_MIGRATION)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Migration,group=Percentile,color=Percentile))+geom_line(data=HIST %>% filter(Year>=2009),aes(x=Year,y=Migration),color='black',linewidth=0.75) +geom_line(data=MEDIAN_PRED_MIGRATION,aes(x=Year,y=Migration),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2065, by = 5)))+ scale_y_continuous(breaks = seq(-350, 350, by = 10))+theme_bw()+ggtitle("Kemmerer & Diamondville, Migration Forecast")+ expand_limits( y = 0)
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BIRTH_DATA <- GET_DATA(RES,4)
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BIRTH_PLOT <- MAKE_GRAPH(BIRTH_DATA)
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BIRTH_PLOT <- BIRTH_PLOT+geom_line(data=HIST,aes(x=Year,y=Births),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 10),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 10))+ggtitle("Kemmerer & Diamondville, Birth Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Births")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
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BIRTH_PLOT
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DEATH_DATA <- GET_DATA(RES,5) %>% filter(!is.na(MIN))
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DEATH_PLOT <- MAKE_GRAPH(DEATH_DATA)
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DEATH_PLOT <- DEATH_PLOT+geom_line(data=HIST,aes(x=Year,y=Deaths),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 10),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(0, 35000, by = 10))+ggtitle("Kemmerer & Diamondville, Mortality Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Deaths")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
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DEATH_PLOT
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MIGRATION_DATA <- GET_DATA(RES,6) %>% filter(!is.na(MIN))
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MIGRATION_PLOT <- MAKE_GRAPH(MIGRATION_DATA)
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MIGRATION_PLOT <- MIGRATION_PLOT+geom_line(data=HIST,aes(x=Year,y=Migration),color='black',linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2010, 2060, by = 10),2065),limits=c(2009,2065))+ scale_y_continuous(breaks = seq(-1000, 1000, by = 50))+ggtitle("Kemmerer & Diamondville, Net Migration Forecast")+ expand_limits( y = 0)+labs(color = "Prediction Interval",linetype="Prediction Interval",y="Migration")+ theme_bw()+ theme(legend.position = "top",panel.grid.minor = element_blank())
|
||||
MIGRATION_PLOT
|
||||
ggsave("Kemmerer_Net_Migration_Fan_Chart.png",MIGRATION_PLOT)
|
||||
|
||||
|
||||
|
||||
DEATHS_PLOT <- ggplot(data=GRAPH_DATA_MORTALITY)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Deaths,group=Percentile,color=Percentile))+geom_line(data=HIST %>% filter(Year>=2009),aes(x=Year,y=Deaths),color='black',linewidth=0.75)+geom_line(data=MEDIAN_PRED_MORTALITY,aes(x=Year,y=Deaths),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2000, 2065, by = 5)))+ scale_y_continuous(breaks = seq(0, 50, by = 5))+theme_bw()+ggtitle("Kemmerer, Wyoming Death Forecast")+ expand_limits( y = 0)
|
||||
DEATHS_PLOT
|
||||
ggsave("Kemmerer_Mortality_Fan_Chart.png",DEATHS_PLOT )
|
||||
2025-1940
|
||||
|
||||
|
||||
BIRTH_PLOT <- ggplot(data=GRAPH_DATA_BIRTHS)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Births,group=Percentile,color=Percentile))+geom_line(data=HIST %>% filter(Year>=2009),aes(x=Year,y=Births),color='black',linewidth=0.75)+geom_line(data=MEDIAN_PRED_BIRTHS,aes(x=Year,y=Births),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2000, 2065, by = 5)))+ scale_y_continuous(breaks = seq(0, 200, by = 5))+theme_bw()+ggtitle("Kemmerer, Wyoming Birth Forecast")+ expand_limits( y = 0)
|
||||
ggsave("Kemmerer_Births_Fan_Chart.png",BIRTH_PLOT )
|
||||
|
||||
|
||||
@ -1,65 +0,0 @@
|
||||
library(tidyverse)
|
||||
###Process the simulations and save the main percentile results by year
|
||||
RES <- read_csv("Results/Simulations/Kemmerer_2016_Simulation.csv")
|
||||
HIST <- readRDS("Data/Cleaned_Data/Population_Data/RDS/Kemmerer_Diamondville_Population_Data.Rds") %>% filter(County=='Lincoln') %>% mutate(Percentile="Actual Population") %>% filter(Year>=1940)
|
||||
YEARS <- min(RES$Year):max(RES$Year)
|
||||
######Population
|
||||
START_POP <- HIST %>% filter(Year==2016) %>% pull(Population)
|
||||
GRAPH_DATA <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Population),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
|
||||
#Add 2016 as a starting value
|
||||
GRAPH_DATA <- rbind(GRAPH_DATA[1,],GRAPH_DATA)
|
||||
GRAPH_DATA[1,] <-t(c(rep(START_POP,ncol(GRAPH_DATA)-1),min(GRAPH_DATA$Year)-1))
|
||||
FAN_DATA <- GRAPH_DATA
|
||||
GRAPH_DATA <- GRAPH_DATA %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Population")
|
||||
GRAPH_DATA$Percentile <- factor(GRAPH_DATA$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','50%','60%','75%','90%','95%','97.5%')))
|
||||
START_POP <- HIST %>% filter(Year==2016) %>% pull(Population)
|
||||
GRAPH_DATA <- rbind(GRAPH_DATA,GRAPH_DATA %>% filter(Year==2017) %>% mutate(Population=START_POP,Year=2016))
|
||||
MEDIAN_PRED <- GRAPH_DATA %>% filter(Percentile=='50%')
|
||||
######Migration
|
||||
GRAPH_DATA_MIGRATION <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Net_Migration),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
|
||||
FAN_DATA_MIGRATION <- GRAPH_DATA_MIGRATION
|
||||
GRAPH_DATA_MIGRATION <- GRAPH_DATA_MIGRATION %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Migration")
|
||||
GRAPH_DATA_MIGRATION$Percentile <- factor(GRAPH_DATA_MIGRATION$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%')))
|
||||
MEDIAN_PRED_MIGRATION <- GRAPH_DATA_MIGRATION %>% filter(Percentile=='50%')
|
||||
######Mortalities
|
||||
GRAPH_DATA_MORTALITY <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Deaths),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
|
||||
FAN_DATA_MORTALITY <- GRAPH_DATA_MORTALITY
|
||||
GRAPH_DATA_MORTALITY <- GRAPH_DATA_MORTALITY %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Deaths")
|
||||
GRAPH_DATA_MORTALITY$Percentile <- factor(GRAPH_DATA_MORTALITY$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%')))
|
||||
MEDIAN_PRED_MORTALITY<- GRAPH_DATA_MORTALITY %>% filter(Percentile=='50%')
|
||||
######Births
|
||||
START_BIRTHS <- HIST %>% filter(Year==2016) %>% pull(Births)
|
||||
|
||||
GRAPH_DATA_BIRTHS <- do.call(rbind,lapply(YEARS,function(x){quantile(RES %>% filter(Year==x) %>% pull(Births),c(0.025,0.05,0.1,0.25,0.4,0.5,0.6,0.75,0.9,0.95,0.975))})) %>% as_tibble %>% mutate(Year=YEARS)
|
||||
GRAPH_DATA_BIRTHS <- rbind(GRAPH_DATA_BIRTHS[1,],GRAPH_DATA_BIRTHS)
|
||||
GRAPH_DATA_BIRTHS[1,] <-t(c(rep(START_BIRTHS,ncol(GRAPH_DATA_BIRTHS)-1),min(GRAPH_DATA_BIRTHS$Year)-1))
|
||||
|
||||
FAN_DATA_BIRTHS <- GRAPH_DATA_BIRTHS
|
||||
GRAPH_DATA_BIRTHS<- GRAPH_DATA_BIRTHS %>% pivot_longer(cols=!Year,names_to=c("Percentile"),values_to="Births")
|
||||
GRAPH_DATA_BIRTHS$Percentile <- factor(GRAPH_DATA_BIRTHS$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%')))
|
||||
MEDIAN_PRED_BIRTHS<- GRAPH_DATA_BIRTHS %>% filter(Percentile=='50%')
|
||||
|
||||
|
||||
|
||||
#write_csv(GRAPH_DATA,PERCENTILE_DATA)
|
||||
#Add historic
|
||||
MEDIAN_PRED <- GRAPH_DATA %>% filter(Percentile=='50%')
|
||||
GRAPH_DATA <- GRAPH_DATA %>% filter(Percentile!='50%')
|
||||
|
||||
ALPHA=0.2
|
||||
COLOR <- 'black'
|
||||
#GRAPH <-
|
||||
nrow(RES)/10
|
||||
ggplot(data=GRAPH_DATA)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Population,group=Percentile,color=Percentile))+geom_line(data=HIST,aes(x=Year,y=Population),color='black',linewidth=0.75)+geom_line(data=MEDIAN_PRED,aes(x=Year,y=Population),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2030, by = 5)))+ scale_y_continuous(breaks = seq(0, 35000, by = 250))+theme_bw()+ggtitle("Kemmerer & Diamondville, Population Forecast")+ expand_limits( y = 0)
|
||||
|
||||
ggplot(data=GRAPH_DATA_MIGRATION)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MIGRATION,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Migration,group=Percentile,color=Percentile))+geom_line(data=HIST %>% filter(Year>=2009),aes(x=Year,y=Migration),color='black',linewidth=0.75)
|
||||
+geom_line(data=MEDIAN_PRED_MIGRATION,aes(x=Year,y=Migration),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2030, by = 5)))+ scale_y_continuous(breaks = seq(0, 35000, by = 250))+theme_bw()+ggtitle("Kemmerer & Diamondville, Migration Forecast")+ expand_limits( y = 0)
|
||||
|
||||
|
||||
|
||||
|
||||
ggplot(data=GRAPH_DATA_MORTALITY)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_MORTALITY,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Deaths,group=Percentile,color=Percentile))+geom_line(data=HIST %>% filter(Year>=2009),aes(x=Year,y=Deaths),color='black',linewidth=0.75)+geom_line(data=MEDIAN_PRED_MORTALITY,aes(x=Year,y=Deaths),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2000, 2030, by = 5)))+ scale_y_continuous(breaks = seq(0, 50, by = 5))+theme_bw()+ggtitle("Kemmerer, Wyoming Death Forecast")+ expand_limits( y = 0)
|
||||
|
||||
|
||||
ggplot(data=GRAPH_DATA_BIRTHS)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA_BIRTHS,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Births,group=Percentile,color=Percentile))+geom_line(data=HIST %>% filter(Year>=2009),aes(x=Year,y=Births),color='black',linewidth=0.75)+geom_line(data=MEDIAN_PRED_BIRTHS,aes(x=Year,y=Births),color='black',linetype=4,linewidth=0.75)+ scale_x_continuous(breaks = c(seq(2000, 2030, by = 5)))+ scale_y_continuous(breaks = seq(0, 200, by = 5))+theme_bw()+ggtitle("Kemmerer, Wyoming Birth Forecast")+ expand_limits( y = 0)
|
||||
|
||||
@ -10,4 +10,4 @@ Data is manually gathered from CDC WONDER data queries.
|
||||
4) The world pandemic uncertainty index as collected from FRED which is used to account for pandemics in the regression, making the age time series stationary.
|
||||
|
||||
These are used to project mortality trends over time. In the case of the age adjusted data, this has local trends that can be compared to the national average. The single age-sex data is only at a national level but can be imparted to local levels as a general trend in the distribution of deaths
|
||||
--- Run Date: 2025-12-05 19:32:50 ---
|
||||
--- Run Date: 2025-12-08 13:10:13 ---
|
||||
|
||||
@ -13,8 +13,8 @@ Rscript "./Scripts/2A_Birth_Rate_Regression_and_Impart_Kemmerer_Births.r"
|
||||
Rscript "./Scripts/2B_Impart_Deaths_and_Migration_to_Subregions.r"
|
||||
Rscript "./Scripts/2C_Migration_by_Age_Regression.r"
|
||||
Rscript "./Scripts/2D_Overall_Migration_ARIMA.r"
|
||||
Rscript "./Scripts/2E_Move_Current_Demographic_Data_to_Current_Year.r"
|
||||
Rscript "./Scripts/2F_Create_Age_Sex_Specfic_Mortality_Trends.r"
|
||||
Rscript "./Scripts/2G_Single_Age_Sex_ARIMA_Models.r"
|
||||
Rscript "./Scripts/2E_Move_Current_Demographic_Data_to_Current_Year.r"
|
||||
#Produce final results for either the simulation, or information for the report, but not anything used in later stages of the simulation
|
||||
Rscript "./Scripts/3A_Population_Pyramid.r"
|
||||
|
||||
@ -57,8 +57,6 @@ rownames(OTHER_LIN_DEM_OLD_MAT ) <- 0:85
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
####Save results
|
||||
CSV_SAVE <- paste0(SAVE_DEMO_LOC,"/CSV")
|
||||
RDS_SAVE <- paste0(SAVE_DEMO_LOC,"/RDS")
|
||||
|
||||
@ -23,15 +23,16 @@ sink()
|
||||
|
||||
#Load data files which were generated using Wonder Data set manually
|
||||
LIN_1979 <- read_csv(paste0(DATA_LOC_RAW,"Lincoln_Age_Adjusted_1979-1998.csv")) %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`)%>% mutate(Region='Lincoln')
|
||||
|
||||
WY_1979 <- read_csv(paste0(DATA_LOC_RAW,"Wyoming_Age_Adjusted_1979-1998.csv")) %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`)%>% mutate(Region='Wyoming')
|
||||
US_1979 <- read_csv(paste0(DATA_LOC_RAW,"US_Age_Adjusted_1979-1998.csv")) %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`) %>% filter(!is.na(Sex),!is.na(Year)) %>% mutate(Region="US")%>% filter(Year<2018)
|
||||
LIN_1999 <- read_csv(paste0(DATA_LOC_RAW,"Lincoln_Age_Adjusted_1999-2020.csv")) %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`)%>% mutate(Region='Lincoln')
|
||||
WY_1999 <- read_csv(paste0(DATA_LOC_RAW,"Wyoming_Age_Adjusted_1999-2020.csv")) %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`)%>% mutate(Region='Wyoming') %>% filter(Year<2018)
|
||||
US_1999 <- read_csv(paste0(DATA_LOC_RAW,"US_Age_Adjusted_1999-2020.csv"))%>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`) %>% filter(!is.na(Sex),!is.na(Year)) %>% mutate(Region="US")%>% filter(Year<2018)
|
||||
|
||||
|
||||
WY_2018 <- read_csv(paste0(DATA_LOC_RAW,"Wyoming_Age_Adjusted_2018-2023.csv")) %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`) %>% mutate(Region='Wyoming')
|
||||
US_2018 <- read_csv(paste0(DATA_LOC_RAW,"US_Age_Adjusted_2018-2023.csv"))%>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`) %>% mutate(Region="US")
|
||||
|
||||
##No adjustment for later data allowed in WONDER, so applying an average value for use in the graphs as a reasonable assumption.
|
||||
LIN_2018<- read_csv(paste0(DATA_LOC_RAW,"Lincoln_Not_Age_Adjusted_2018-2023.csv")) %>% select(Year,Sex,Mort_Rate=`Crude Rate`)%>% mutate(Region='Lincoln')
|
||||
ADJUST_TERM <- LIN_2018 %>% rename(UNADJUSTED=Mort_Rate) %>% inner_join(LIN_1999) %>% filter(!is.na(Year)) %>% mutate(Ratio=Mort_Rate/UNADJUSTED) %>% group_by(Sex) %>% summarize(Ratio=mean(Ratio))
|
||||
@ -40,7 +41,6 @@ DF <- rbind(LIN_1999,LIN_2018,WY_1999,US_1999,US_2018,WY_2018,WY_1979,US_1979,LI
|
||||
DF <- DF %>% ungroup%>% group_by(Sex,Region) %>% arrange(Sex,Region,Year) %>% mutate(L_Mort_Rate=lag(Mort_Rate)) %>% ungroup
|
||||
#Make a regression table which includes lats of mortality rate for a stationary time series, and pivot such that each year is it's own row with each area of interest (US, Wyoming, Lincoln)
|
||||
REG_DATA <- DF %>% pivot_wider(values_from=c(Mort_Rate,L_Mort_Rate),names_from=Region)
|
||||
|
||||
##Pull the World Pandemic Index from FRED for use in the regression data
|
||||
PANDIMIC_INDEX <- read_csv("https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23ebf3fb&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1320&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=WUPI&scale=left&cosd=1996-01-01&coed=2025-07-01&line_color=%230073e6&link_values=false&line_style=solid&mark_type=none&mw=3&lw=3&ost=-99999&oet=99999&mma=0&fml=a&fq=Annual&fam=sum&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2025-11-20&revision_date=2025-11-20&nd=1996-01-01") %>% mutate(Year=year(observation_date)) %>% select(Year,WUPI)%>% mutate(L_WUPI=lag(WUPI),L_TWO_WUPI=lag(WUPI,2))
|
||||
REG_DATA <- REG_DATA %>% left_join(PANDIMIC_INDEX) %>% mutate(WUPI=ifelse(is.na(WUPI),0,WUPI),L_WUPI=ifelse(is.na(L_WUPI),0,L_WUPI))
|
||||
@ -59,7 +59,6 @@ write_csv(DF,paste0(DATA_SAVE_LOC_RDS,"Long_Mortality_Rate_Data.csv" ))
|
||||
#Data combined and cleaned for regression save
|
||||
saveRDS(REG_DATA,paste0(DATA_SAVE_LOC_RDS,"Mortality_Rate_and_Pandemic_Data_for_Regression.Rds" ))
|
||||
write_csv(REG_DATA,paste0(DATA_SAVE_LOC_CSV,"Mortality_Rate_and_Pandemic_Data_for_Regression.csv" ))
|
||||
|
||||
run_datetime <- format(Sys.time(), "%Y-%m-%d %H:%M:%S")
|
||||
sink(file=paste0(DATA_LOC_RAW,"README_MORTALITY_DATA.txt"),append=TRUE)
|
||||
cat(paste0("\n--- Run Date: ", run_datetime, " ---\n"))
|
||||
|
||||
@ -34,4 +34,3 @@ write_csv(DF,paste0(DATA_SAVE_LOC_RDS,"Single_Sex_Age_US_Mortality_Rate_Data_Lon
|
||||
|
||||
saveRDS(REG_DATA,paste0(DATA_SAVE_LOC_RDS,"Single_Sex_Age_US_Mortality_Rate_Data_Wide.Rds" ))
|
||||
write_csv(REG_DATA,paste0(DATA_SAVE_LOC_RDS,"Single_Sex_Age_US_Mortality_Rate_Data_Wide.csv" ))
|
||||
|
||||
|
||||
@ -80,3 +80,4 @@ RES*POP_WORK_RATIO #Total family included migration, converted per person (rathe
|
||||
RES$Job_Type <- c("Construction","Operator")
|
||||
RES <- RES[,c(3,1:2)]
|
||||
saveRDS(RES,paste0(SAVE_LOC,"Induced_Jobs.Rds"))
|
||||
|
||||
|
||||
@ -68,11 +68,10 @@ MOD_BIRTHS_1985 <- feols(log(Births)~log(Lag_Births)+log(Lag_Two_Births)+log(Min
|
||||
#Easier to read for a LaTex Table
|
||||
DICT <- c("log(Lag_Births)"="Births Last Years (log)","log(Lag_Two_Births)"="Births Two Years Ago (log)","LN"='Lincoln',"KEM"='Kemmerer Area','log(Births)'='Births (log)','log(Min_Birth_Group)'='Child Rearing Aged Adults (log)','Region'="County" )
|
||||
REG_VIEW_DATA <- REG_REDUCED_DATA %>% mutate(KEM=ifelse(Region=='Kemmerer & Diamondville',1,0),LN=ifelse(Region=='Kemmerer & Diamondville' |Region=='Lincoln' ,1,0),Region=ifelse(Region=='Kemmerer & Diamondville' ,'Lincoln',Region))
|
||||
MOD_VIEW_BIRTHS <- feols(log(Births)~log(Lag_Births)+log(Lag_Two_Births)+log(Min_Birth_Group)+KEM*Year+Year*Region|Region,cluster=~Year+Region, data=REG_VIEW_DATA ) #Higher AIC but worse acf
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MOD_VIEW_BIRTHS <- feols(log(Births)~log(Lag_Births)+log(Lag_Two_Births)+log(Min_Birth_Group)+KEM*Year+Year*Region|Region,cluster=~Year+Region, data=REG_VIEW_DATA ) #Higher AIC but worse acf
|
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REG_VIEW_DATA_2016 <- REG_VIEW_DATA %>% filter(Year<=2016)
|
||||
REG_DATA_2016 <- REG_REDUCED_DATA %>% filter(Year<=2016)
|
||||
MOD_VIEW_BIRTHS_2016 <- feols(log(Births)~log(Lag_Births)+log(Lag_Two_Births)+log(Min_Birth_Group)+KEM+LN*Year+Year*Region|Region,cluster=~Year+Region, data=REG_VIEW_DATA_2016 )
|
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MOD_VIEW_BIRTHS_2016
|
||||
REG_VIEW_DATA_1985 <- REG_VIEW_DATA%>% filter(Year<=1985)
|
||||
MOD_VIEW_BIRTHS_1985 <- feols(log(Births)~log(Lag_Births)+log(Lag_Two_Births)+log(Min_Birth_Group)+LN*Year+Year*Region|Region,cluster=~Year+Region, data=REG_VIEW_DATA_1985 )
|
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###Prelim information for fixest table
|
||||
@ -142,15 +141,14 @@ ST_REG_DATA <- REG_REDUCED_DATA %>% filter(Region=='Lincoln') %>% filter(Year==m
|
||||
write_csv(REG_REDUCED_DATA,paste0(SAVE_DATA_LOC,"/Birth_Regression_Input_Data.csv"))
|
||||
|
||||
##########Update existing demographic data with new births
|
||||
KEM_DATA_NEW <- REG_DATA %>% filter(Region=='Kemmerer & Diamondville') %>% select(Year,County,Region,Population,Births,Deaths,Migration)
|
||||
PRED_KEM_BIRTH_2024 <- REG_DATA %>% filter(Region=='Kemmerer & Diamondville',Year>=2023) %>% mutate(Min_Birth_Group=lag(Min_Birth_Group)) %>% filter(Year==2024)
|
||||
KEM_DATA_NEW <- REG_DATA %>% filter(KEM==1) %>% select(Year,County,Region,Population,Births,Deaths,Migration) %>% mutate(Region='Kemmerer & Diamondville')
|
||||
PRED_KEM_BIRTH_2024 <- REG_DATA %>% filter(KEM==1,Year>=2023) %>% mutate(Min_Birth_Group=lag(Min_Birth_Group)) %>% filter(Year==2024)
|
||||
KEM_DATA_NEW[KEM_DATA_NEW$Year==2024,"Births"] <- round(exp(predict(MOD_BIRTHS,PRED_KEM_BIRTH_2024)))
|
||||
|
||||
OTHER_DATA_NEW <- REG_DATA %>% filter(Region=='Lincoln_Other') %>% select(Year,County,Region,Population,Births,Deaths,Migration)
|
||||
PRED_OTHER_BIRTH_2024 <- REG_DATA %>% filter(Region=='Lincoln_Other',Year>=2023) %>% mutate(Min_Birth_Group=lag(Min_Birth_Group)) %>% filter(Year==2024)
|
||||
OTHER_DATA_NEW[OTHER_DATA_NEW$Year==2024,"Births"] <- round(exp(predict(MOD_BIRTHS,PRED_OTHER_BIRTH_2024)))
|
||||
|
||||
LIN_DATA_NEW <- REG_DATA %>% filter(Region=='Lincoln') %>% select(Year,County,Region,Population,Births,Deaths,Migration)
|
||||
LIN_DATA_NEW <- REG_DATA %>% filter(Region=='Lincoln',KEM==0) %>% select(Year,County,Region,Population,Births,Deaths,Migration)
|
||||
#
|
||||
if(!exists("POPULATION_SAVE_RDS")){POPULATION_SAVE_RDS <- "./Data/Cleaned_Data/Population_Data/RDS/"}
|
||||
dir.create(POPULATION_SAVE_RDS, recursive = TRUE, showWarnings = FALSE)
|
||||
@ -163,5 +161,5 @@ saveRDS(KEM_DATA_NEW,paste0(POPULATION_SAVE_RDS,"Kemmerer_Diamondville_Populatio
|
||||
write_csv(KEM_DATA_NEW,paste0(POPULATION_SAVE_CSV,"Kemmerer_Diamondville_Population_Data.csv"))
|
||||
saveRDS(OTHER_DATA_NEW,paste0(POPULATION_SAVE_RDS,"Other_Lincoln_Population_Data.Rds"))
|
||||
write_csv(OTHER_DATA_NEW,paste0(POPULATION_SAVE_CSV,"Other_Lincoln_Population_Data.csv"))
|
||||
|
||||
print("Done")
|
||||
|
||||
|
||||
@ -15,8 +15,7 @@ MORTALITY <- MORT
|
||||
PRED_DEATHS <- function(DATA,MORTALITY){
|
||||
MORTALITY <- MORTALITY%>% pivot_wider(values_from=Death_Rate,names_from=Sex) %>% group_by(County,Min_Age,Max_Age) %>% summarize(Rate_Male=mean(Male,na.rm=TRUE),Rate_Female=mean(Female,na.rm=TRUE)) %>% ungroup
|
||||
RES <- DATA[DATA$Age >= MORTALITY$Min_Age[1] & MORTALITY$Max_Age[1]>= DATA$Age,] %>% group_by(County,Region,Year,Age) %>% summarize(Num_Female=sum(Num_Female),Num_Male=sum(Num_Male)) %>% ungroup %>% mutate(Min_Age=MORTALITY$Min_Age[1],Max_Age= MORTALITY$Max_Age[1])
|
||||
for(i in 2:nrow(MORTALITY)){
|
||||
RES <- rbind(RES,DATA[DATA$Age >= MORTALITY$Min_Age[i] & MORTALITY$Max_Age[i]>= DATA$Age,] %>% group_by(County,Region,Year,Age) %>% summarize(Num_Female=sum(Num_Female),Num_Male=sum(Num_Male)) %>% ungroup %>% mutate(Min_Age=MORTALITY$Min_Age[i],Max_Age= MORTALITY$Max_Age[i]))
|
||||
for(i in 2:nrow(MORTALITY)){ RES <- rbind(RES,DATA[DATA$Age >= MORTALITY$Min_Age[i] & MORTALITY$Max_Age[i]>= DATA$Age,] %>% group_by(County,Region,Year,Age) %>% summarize(Num_Female=sum(Num_Female),Num_Male=sum(Num_Male)) %>% ungroup %>% mutate(Min_Age=MORTALITY$Min_Age[i],Max_Age= MORTALITY$Max_Age[i]))
|
||||
}
|
||||
RES <- RES%>% arrange(Year) %>% left_join(MORTALITY)%>% group_by(County,Region,Year) %>% summarize(Predicted_Deaths=sum(Rate_Male*Num_Male)+sum(Rate_Female*Num_Female) ) %>% ungroup %>% select(County,Region,Year,Predicted_Deaths)
|
||||
return(RES)
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
library(tidyverse)
|
||||
library(fixest)
|
||||
library(scales)
|
||||
#setwd("../")
|
||||
######Checking correlations with migration rates
|
||||
DEMOGRAPHIC_DATA <- readRDS("Data/Cleaned_Data/Demographic_Sex_Age_Data/RDS/All_Wyoming_Counties_Demographics.Rds")
|
||||
#Extract the population trend data to connect with demographics (Population,births,deaths)
|
||||
@ -108,3 +109,4 @@ dir.create(RES_SAVE_LOC , recursive = TRUE, showWarnings = FALSE)
|
||||
|
||||
write.csv(MIG_AGE_DIST ,paste0(RES_SAVE_LOC,"Migration_Age_Probability_Zero_to_85.csv"),row.names=FALSE)
|
||||
saveRDS(MIG_AGE_DIST ,paste0(RES_SAVE_LOC,"Migration_Age_Probability_Zero_to_85.Rds"))
|
||||
|
||||
|
||||
@ -59,7 +59,4 @@ KEM_DEMOGRAPHIC <- KEM_DEMOGRAPHIC - DEATH_MAT
|
||||
saveRDS(KEM_DEMOGRAPHIC,"Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
|
||||
##Perhaps I can be more clever. I could average the direct simulation estimate as done above, with the Kemmerer values found when estimating the other region, and then subtracting from the Lincoln Total.
|
||||
OTHER_DEMOGRAPHIC_NEW <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Lincoln_County_Demographics_Matrix.Rds")-KEM_DEMOGRAPHIC
|
||||
saveRDS(OTHER_DEMOGRAPHIC_NEW ,"Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
|
||||
|
||||
|
||||
|
||||
saveRDS(OTHER_DEMOGRAPHIC_NEW ,"Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Other_Lincoln_Demographics_Matrix.Rds")
|
||||
|
||||
@ -1,8 +1,11 @@
|
||||
#Takes the added jobs table and downshift for people living outside of Kemmerer but in Lincoln
|
||||
LOCAL_WORK_ADJ <- function(DF,MIN_LOCAL,MAX_LOCAL=1){
|
||||
#DF <- OPERATOR_MIGRATION
|
||||
#MIN_LOCAL=0.85
|
||||
#MAX_LOCAL=1
|
||||
|
||||
DF[,-1]<- runif(1,MIN_LOCAL,MAX_LOCAL)*DF[,-1]#Random range people choosing to live outside of Kemmerer assumed to be between 85% and 100%
|
||||
DF[,-1:-2] <-round(DF[,-1:-2])
|
||||
LOCAL_WORK_ADJ <- function(DF,MIN_LOCAL,MAX_LOCAL=1){
|
||||
DF <- runif(1,MIN_LOCAL,MAX_LOCAL)*DF#Random range people choosing to live outside of Kemmerer assumed to be between 85% and 100%
|
||||
DF <-round(DF)
|
||||
return(DF)
|
||||
}
|
||||
#Find the expected total of new induced jobs from TerraPower (Includes construction entering and leaving, and operators entering)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user