library(tidyverse)
GET_DATA <- function(){
	GET_FILE_US <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/US/US_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="US") }
	GET_FILE_WY <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/Wyoming/WY_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="WY") }
DATA <-rbind(lapply(2028:2035,GET_FILE_US) %>% bind_rows()%>% rename(Income=`Labor Income`)  %>% mutate(Income=parse_number(Income),`Value Added`=parse_number(`Value Added`),Output=parse_number(Output)) ,
lapply(2028:2035,GET_FILE_WY) %>% bind_rows()%>% rename(Income=`Labor Income`)  %>% mutate(Income=parse_number(Income),`Value Added`=parse_number(`Value Added`),Output=parse_number(Output)))

	DATA$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",DATA$Impact)))
	DATA <-  DATA %>% pivot_longer(-c(Impact,year,Region)) %>% rename(Type=Impact,Impact=name)
	DATA$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",DATA$Impact)))
return(DATA)
}

DATA <- GET_DATA()
TEMP <- DATA %>% filter(year==2035)
TEMP <- DATA %>% filter(year==2035)
RES <- TEMP %>% mutate(year=2036)
for(i in 2037:2060){
RES <- RES %>% rbind(TEMP %>% mutate(year=i))
}
DATA <- DATA %>% rbind(RES)
DATA$Type <-  factor(DATA$Type,levels=c("Direct","Indirect","Induced"))
DATA
EMP_WY <- DATA %>% filter(Impact=="Employment",Region=="WY")
DOL_WY <- DATA %>% filter(Impact!="Employment",Region=="WY")
ggplot(EMP_WY, aes(x = year, y = value, fill = Type)) +  geom_area(stat = "identity", position = "stack") +  theme_bw()+scale_x_continuous(breaks=2026:2060)+scale_y_continuous(breaks=seq(0,10000,by=250))+ylab("Wyoming Employment Additions\n(Full-time Equivalent)")
																	    ?scale_y_continuous
ggplot(DOL_WY, aes(x = year, y = value/10^9, fill = Type)) +  geom_bar(stat = "identity", position = "stack") +  theme_minimal()+facet_wrap(~Impact)