##Extract Lincoln county ACS data including separate indicators for if a census tract is in Kemmerer, or any other part of the county. Population by Age-Sex in each year for each tract.
GET_ACS_LIN_DATA <- function(ACS_YEAR,ACS_CODES=CODES,CENSUS_TRACTS=PROJ_TRACTS){
	AGE_DATA <- 	get_acs(geography="tract",year=ACS_YEAR,variables=ACS_CODES$variable,state='WY',county='lincoln') %>% mutate(se=moe/1.64) %>% left_join(ACS_CODES %>% mutate(variable=gsub('E','',variable))) %>% select(-NAME,-moe) %>% left_join(CENSUS_TRACTS) %>% mutate(IN_KEM=ifelse(is.na(IN_KEM),0,1)) %>% rename(Population=estimate) %>% select(-variable,-GEOID) %>% select(Sex,Min_Age,Max_Age,Med_Age,IN_KEM,Population,se) %>% filter(!(Min_Age==0& Max_Age==Inf))
	AGE_DATA <- AGE_DATA %>% group_by(Sex,Min_Age,Max_Age,Med_Age,IN_KEM)%>% summarize(Population=sum(Population)) %>% ungroup
	#Add Descriptive age category to a clean graph
	AGE_DATA$Ages <- paste(AGE_DATA$Min_Age,"to",AGE_DATA$Max_Age)
	AGE_DATA[AGE_DATA$Max_Age==Inf,"Ages"] <- "85+"
	AGE_DATA[AGE_DATA$Med_Age==18,"Ages"] <- "18"
	AGE_DATA[AGE_DATA$Med_Age==20,"Ages"] <- "20"
	AGE_DATA[AGE_DATA$Med_Age==21,"Ages"] <- "21"
	AGE_DATA[AGE_DATA$Min_Age==0,"Ages"] <- "Under 5"
	#Turn the ages into factors to keep the correct order in graphs
	#Add the percent of total relative population in the region
	ORD <- AGE_DATA %>% select(Min_Age,Ages) %>% unique %>% arrange(Min_Age) %>% pull(Ages) %>% unique
	AGE_DATA$Ages <- factor(AGE_DATA$Ages,levels=ORD)
	AGE_DATA$Year <- ACS_YEAR
	AGE_DATA <-AGE_DATA %>% select(Year,Sex,Ages,IN_KEM,everything())
	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)
	AGE_DATA <- GET_ACS_LIN_DATA(ACS_YEAR)
	for(i in 1:nrow(AGE_DATA)){
		if(i==1 & exists("RES")){rm(RES)}
		C_DEMO <-	AGE_DATA[i,]
		C_SEX <- C_DEMO$Sex 
		if(C_DEMO$Min_Age==C_DEMO$Max_Age){C_AGE_YEARLY_DATA <- LIN_DEMOGRAPHICS %>% filter(Age==C_DEMO$Min_Age)}else{
			C_AGE_YEARLY_DATA <- LIN_DEMOGRAPHICS %>% filter(Age>=C_DEMO$Min_Age,Age<=C_DEMO$Max_Age)
		}
		#Extract only the current sex being applied
		if(C_SEX=='Female'){C_AGE_YEARLY_DATA  <- C_AGE_YEARLY_DATA %>% select(Age,Num_Female) %>% rename(POP=Num_Female)}else{C_AGE_YEARLY_DATA  <- C_AGE_YEARLY_DATA %>% select(Age,Num_Male) %>% rename(POP=Num_Male)}
		C_AGE_YEARLY_DATA$Sex <- C_SEX
		C_AGE_YEARLY_DATA$PER  <- C_AGE_YEARLY_DATA$POP/sum(C_AGE_YEARLY_DATA$POP)
		C_RES <- C_AGE_YEARLY_DATA%>% left_join(C_DEMO) %>% mutate(Population=PER*Population) %>% select(Age,Sex,IN_KEM,Population)
		if(i==1){RES <- C_RES}else{RES <- rbind(RES,C_RES)}
	}
	RES$Year <- ACS_YEAR
	RES$County <- "Lincoln"
	RES <-	RES %>% select(County,IN_KEM,Year,Age,Sex,Population)
	return(RES)
}