library(tidyverse)
library(scales)
library(janitor)
library(paletteer)
dir.create("Results", showWarnings = FALSE)
source("Scripts/Load_IMPLAN.r")
SINGLE <- TRUE 

	IMPLAN <- GET_IMPLAN_DATA(SINGLE)


IMPLAN_DOLLAR <- IMPLAN %>% filter(Impact!='Employment')

IMPLAN_DOLLAR <-  IMPLAN_DOLLAR  %>% group_by(Type,Year,Impact,Source) %>% mutate(value=ifelse(Region=='US',value-min(value),value) ) %>% ungroup
TEMP <- IMPLAN_DOLLAR %>% filter(Year==2028)  %>% mutate(value=0)
IMPLAN_DOLLAR <- rbind(IMPLAN_DOLLAR,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))
IMPLAN_DOLLAR$Impact <- factor(IMPLAN_DOLLAR$Impact,levels=c('Output','Value Added','Income','Federal','State','County'))
IMPLAN_DOLLAR$Region <- factor(ifelse(IMPLAN_DOLLAR$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
IMPLAN_DOLLAR_REGION <- IMPLAN_DOLLAR %>% filter(Impact %in% c('Income','Output','Value Added')) %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup

########################Plot of total impact in US and in Wyoming
IMPLAN_REGION_DOLLAR_PLOT <- ggplot(IMPLAN_DOLLAR_REGION ,aes(x=Year,y=value/10^9,fill=Region,group=Region))+geom_area()+facet_wrap(~Impact,ncol=1)+ylab("Billion Dollars")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=1))
ggsave( filename = "./Results/Single Phase: Dollar Values of Model.png",  plot = IMPLAN_REGION_DOLLAR_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
########################Taxes
IMPLAN_TAX <- IMPLAN_DOLLAR %>% filter(Impact %in% c("County","State","Federal"))
OLD <- IMPLAN_TAX 
IMPLAN_TAX <- IMPLAN_TAX %>% group_by(Type,Year,Impact,Source) %>% mutate(value=ifelse(Region=='Rest of United States',value-min(value),value) ) %>% ungroup
IMPLAN_TAX  %>% left_join(OLD %>% rename(OLD=value))

IMPLAN_TAX <- IMPLAN_TAX %>% mutate(Impact=ifelse(Region=='Rest of United States' & Impact!='Federal',"Other States",as.character(Impact)))
IMPLAN_TAX$Impact <- ifelse(IMPLAN_TAX$Impact =='State',"Wyoming State Taxes",IMPLAN_TAX$Impact)
IMPLAN_TAX$Impact <- ifelse(IMPLAN_TAX$Impact =='County',"Wyoming County Taxes",IMPLAN_TAX$Impact)
IMPLAN_TAX$Impact <- factor(IMPLAN_TAX$Impact,levels=c('Output','Value Added','Income','Federal','Other States','Wyoming State Taxes','Wyoming County Taxes'))

#IMPLAN_TAX <- IMPLAN_TAX[!(IMPLAN_TAX$Type=='Direct' & IMPLAN_TAX$Region=='Rest of United States'& IMPLAN_TAX$Impact!='Federal'),] 
#IMPLAN_TAX <-IMPLAN_TAX[!(IMPLAN_TAX$Type=='Direct' & IMPLAN_TAX$Region=='Wyoming'& IMPLAN_TAX$Impact=='Federal'),] 
IMPLAN_DOLLAR <- IMPLAN_DOLLAR %>% filter(!(Impact %in% c("County","State","Federal","Other States"))) %>% rbind(IMPLAN_TAX)
IMPLAN_TAX_FIG_DATA <- IMPLAN_TAX  %>% group_by(Year,Impact) %>% summarize(value=sum(value)) %>% ungroup

TAX_PLOT <- ggplot(IMPLAN_TAX_FIG_DATA, aes(x=Year,y=value/10^6,fill=Impact,group=Impact))+geom_area()+ylab("Million Dollars")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","firebrick","#FFC425","#492F24"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=seq(0,5000,by=50))

ggsave( filename = "./Results/Single Phase: Tax Model.png",  plot = TAX_PLOT,  width = 8.5,  height = 11*(3./4),  units = "in", dpi = 300)
#############Dollar values table
NPV <- IMPLAN_DOLLAR %>% mutate(value=value/10^9,Discount_0=value,Discount_2=value/(1+0.02)^(Year-2028),Discount_5=value/(1+0.05)^(Year-2028),Discount_10=value/(1+0.1)^(Year-2028)) %>% group_by(Impact,Region) %>% summarize(Discount_0=sum(Discount_0),Discount_2=sum(Discount_2),Discount_5=sum(Discount_5),Discount_10=sum(Discount_10)) %>% ungroup
NPV[,3:6] <-round(NPV[,3:6],2)
WY_NPV <- cbind(c("Discount","0%","2%","5%","10%"),NPV %>% filter(Region=='Wyoming') %>% select(-Region) %>% t) %>% as_tibble
colnames(WY_NPV ) <- WY_NPV[1,]
WY_NPV <- WY_NPV[-1,] 
WY_NPV <- WY_NPV %>%  mutate_if(is.character, str_trim)
WY_NPV[,-1] <- lapply(	lapply(WY_NPV[,-1] ,as.numeric) %>% as_tibble,dollar) %>% as_tibble
WY_TAX <- WY_NPV[,c(1,5:7)]
WY_NPV <- WY_NPV[,c(1:4)]

write.csv(WY_NPV,"./Results/Single Phase: Wyoming_NPV_Values.csv",row.names=FALSE)
write.csv(WY_TAX,"./Results/Single Phase: Wyoming_Tax_Values.csv",row.names=FALSE)

#US
US_NPV <- cbind(c("Discount","0%","2%","5%","10%"),NPV %>% filter(Region!='Wyoming') %>% select(-Region) %>% t) %>% as_tibble
colnames(US_NPV ) <- US_NPV[1,]
US_NPV <- US_NPV[-1,] 
US_NPV <- US_NPV %>%  mutate_if(is.character, str_trim)
US_NPV[,-1] <- lapply(	lapply(US_NPV[,-1] ,as.numeric) %>% as_tibble,dollar) %>% as_tibble
US_NPV
US_TAX <- US_NPV[,c(1,5:6)]
US_NPV <- US_NPV[,c(1:4)]

write.csv(US_NPV,"./Results/Single Phase: US_NPV_Values.csv",row.names=FALSE)
write.csv(US_TAX,"./Results/Single Phase: US_Tax_Values.csv",row.names=FALSE)


write.csv(NPV,"./Results/Single Phase: NPV_Values.csv",row.names=FALSE)

############Employment
IMPLAN_EMPLOY <- IMPLAN %>% filter(Impact=='Employment',Source=='IMPLAN') %>% group_by(Year,Region,Impact) %>% summarize(value=sum(value)) %>% ungroup

IMPLAN_EMPLOY <-  IMPLAN_EMPLOY  %>% group_by(Year,Impact) %>% mutate(value=ifelse(Region=='US',value-min(value),value) ) %>% ungroup
TEMP <- IMPLAN_EMPLOY %>% filter(Year==2028)  %>% mutate(value=0)
IMPLAN_EMPLOY <- rbind(IMPLAN_EMPLOY,rbind(TEMP %>% mutate(Year=2025),TEMP %>% mutate(Year=2026),TEMP %>% mutate(Year=2027)))


IMPLAN_EMPLOY$Region <- factor(ifelse(IMPLAN_EMPLOY$Region=="WY",'Wyoming',"Rest of United States"),levels=rev(c("Wyoming","Rest of United States")))
 IMPLAN_EMPLOYMENT_PLOT <- ggplot(IMPLAN_EMPLOY ,aes(x=Year,y=value,fill=Region,group=Region))+geom_area()+ylab("Employment (Full-Time Equivalent)")+theme_bw()+ theme(plot.title = element_text(size = 22, face = "bold"), axis.title = element_text(size = 18),axis.text  = element_text(size = 16),legend.text = element_text(size = 14),strip.text = element_text(size = 14),legend.position="top")+  theme(legend.title=element_blank())+scale_fill_manual(values=c("#3C3B6E","#FFC425"))+scale_x_continuous(breaks=seq(2025,2060,by=5))+scale_y_continuous(breaks=c(seq(0,50000,by=1000)),labels = label_comma())
IMPLAN_EMPLOYMENT_PLOT 
ggsave( filename = "./Results/Single Phase: Employment.png",  plot = IMPLAN_EMPLOYMENT_PLOT ,  width = 8.5,  height = 11*(2/4),  units = "in", dpi = 600)