library(tidyverse)
###Process the simulations and save the main percentile results by year
	RES <- read_csv("Results/Simulations/Kemmerer_2024_Simulation.csv")
	RES[,"Year"] <- RES[,"Year"]
	RES<-	RES %>% filter(!is.na(Year))
	RES <-	RES %>% filter(!is.na(Population))
	HIST <-	readRDS("Data/Cleaned_Data/Population_Data/RDS/Kemmerer_Diamondville_Population_Data.Rds") %>% filter(County=='Lincoln') %>% mutate(Percentile="Actual Population") %>% filter(Year>=1940)
	######Population
	####Fan New
	GET_DATA <- function(RES,COL_NUM){ 

	YEARS <- min(RES$Year,na.rm=TRUE):max(RES$Year,na.rm=TRUE)
	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)
	FAN_DATA <- 	rbind(FAN_DATA[1,],FAN_DATA)
	START_VALUE <- (HIST %>% filter(Year==2024))[,COL_NUM+1] %>% as.numeric
	FAN_DATA <- 	FAN_DATA  %>% pivot_longer(colnames(FAN_DATA %>% select(-Year)),names_to="Percentile") %>% filter(Year>2024) %>% unique
	NUM_YEARS <- length(unique(FAN_DATA$Year) )
	FAN_DATA$Group <- rep(c(1:49,0,rev(1:49)),NUM_YEARS)
	FAN_DATA <- FAN_DATA %>% group_by(Year,Group)  %>% summarize(MIN=min(value),MAX=max(value))
	TEMP <- FAN_DATA %>% filter(Year==2025) %>% mutate(Year=2024) %>% ungroup
	TEMP[,3:4] <- START_VALUE 
	FAN_DATA <- rbind(TEMP,FAN_DATA %>% ungroup) %>% as_tibble
	return(FAN_DATA)
	}
	RES %>% pull(Sim_UUID) %>% unique %>% length()
MAKE_GRAPH <- function(GRAPH_DATA,ALPHA=0.03,COLOR='cadetblue',LINE_WIDTH=1){
	PLOT <- ggplot(data=GRAPH_DATA)
	for(i in 1:49){
	C_DATA <- GRAPH_DATA%>% filter(Group==i)
	     PLOT <- 	PLOT +geom_ribbon(data=C_DATA,aes(x=Year,ymin=MIN,ymax=MAX),alpha=ALPHA,fill=COLOR)

}
	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'))
	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"))
	return(PLOT)
}
POP_DATA <- GET_DATA(RES,3)
POP_PLOT <- MAKE_GRAPH(POP_DATA)
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())
POP_PLOT 

BIRTH_DATA <- GET_DATA(RES,4)
BIRTH_PLOT <- MAKE_GRAPH(BIRTH_DATA)
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())
BIRTH_PLOT 

DEATH_DATA <- GET_DATA(RES,5) %>% filter(!is.na(MIN))
DEATH_PLOT <- MAKE_GRAPH(DEATH_DATA)
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())
DEATH_PLOT 

MIGRATION_DATA <- GET_DATA(RES,6) %>% filter(!is.na(MIN))
MIGRATION_PLOT <- MAKE_GRAPH(MIGRATION_DATA)
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