library(tidyverse)
library(janitor)
#install.packages("paletteer")

library(scales)
library(paletteer)
DATA_DIR <- 'Results/REMI_Output/'
OUTPUT_DIR <- './Results/Tables_and_Figures/'
dir.create(OUTPUT_DIR,recursive=TRUE,showWarnings=FALSE)
######################Employments
EMPLOY <-	read_csv(paste0(DATA_DIR,"Direct, Indirect, and Induced -Employment.csv" ),skip=5)  %>% pivot_longer(c(-Category,-Units),names_to="Year")  %>% mutate(Year=parse_number(Year))
EMPLOY_NUM <-	EMPLOY %>% filter(Units=='Individuals (Jobs)')  %>% select(-Units,Employment=value)  %>% filter(Year>=2028)
EMPLOY_NUM <- rbind(EMPLOY_NUM  %>% filter(!(Year %in% c(2028,2029)))	,EMPLOY_NUM  %>% filter(Year==2028) %>% mutate(Year=2029.74),EMPLOY_NUM  %>% filter(Year==2029) %>% mutate(Year=2029.75))

EMPLOY_NUM$Category <- gsub(" Employment","",EMPLOY_NUM$Category)
EMPLOY_NUM$Category <- factor(EMPLOY_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total")))
EMPLOY_NUM  %>% filter(Employment=='Direct')

COLORS <- rev(paletteer_d("fishualize::Alosa_fallax",n=4,direction=1)[-2])
MAX_VAL <- round(max(EMPLOY_NUM$Employment))
MAX_VAL 
COOL_DOWN <- as.numeric(EMPLOY_NUM[5,3])
#EMPLOY_NUM  
#EMPLOY_NUM  %>% filter(Category=='Total') %>% arrange(Year)%>%  print(n=100)
df <- data.frame(Year=c(2032.5,2034.25,2040.25,2059.75),Employment=c(MAX_VAL+50,COOL_DOWN,687+20,596+20),text=c("Peak of 2,521","Stabalized at 878" ,"687 in 2040","Ends at 596"))
JOB_PLOT <- ggplot(EMPLOY_NUM %>% filter(Category!="Total"),aes(x=Year,y=Employment))+geom_area(position = "stack",aes(fill=Category,group=Category))+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_fill_manual(values=COLORS)+geom_line(data=EMPLOY_NUM %>% filter(Category=="Total"),linewidth=1)+geom_text(data=df,aes(label = text), vjust = "inward", hjust = "inward")+theme(legend.position = "top")+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=250))

 png(paste0(OUTPUT_DIR,"Job_plot.png"  ) , units = "in", width = 11, height = 8, res = 600)
	JOB_PLOT
dev.off()
#JOB_PLOT 
###################Output graph
OUTPUT <-	read_csv(paste0(DATA_DIR,"Direct, Indirect, and Induced -Output.csv" ),skip=5)  %>% pivot_longer(c(-Category,-Units),names_to="Year")  %>% mutate(Year=parse_number(Year))
OUTPUT_NUM <-	OUTPUT %>% select(-Units,Output=value)  %>% filter(Year>=2028) %>% mutate(Output=Output*1000)

OUTPUT_NUM <- rbind(OUTPUT_NUM  %>% filter(!(Year %in% c(2028,2029)))	,OUTPUT_NUM  %>% filter(Year==2028) %>% mutate(Year=2029.74),OUTPUT_NUM  %>% filter(Year==2029) %>% mutate(Year=2029.75))
OUTPUT_NUM$Category <- gsub(" Output","",OUTPUT_NUM$Category)
OUTPUT_NUM$Category <- factor(OUTPUT_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total")))

 OUTPUT_PLOT <- ggplot(OUTPUT_NUM %>% filter(Category!="Total"),aes(x=Year,y=Output))+geom_area(position = "stack",aes(fill=Category,group=Category))+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_fill_manual(values=COLORS)+geom_line(data=OUTPUT_NUM%>% filter(Category=="Total"),linewidth=1)+theme(legend.position = "top")+scale_y_continuous(labels = scales::comma,breaks=seq(0,600,by=25))+ylab("Economic Output (Million USD)")
OUTPUT_PLOT 

png(paste0(OUTPUT_DIR,"Output_plot.png"), units = "in", width = 10, height = 8, res = 600)
	OUTPUT_PLOT
dev.off()



####GDP and Output
GDP <-	read_csv(paste0(DATA_DIR,"Gross Domestic Product - By Region - GDP by Region.csv" ),skip=5)  %>% pivot_longer(c(-Region,-Units),names_to="Year",values_to="GDP")  %>% mutate(Year=parse_number(Year),GDP=GDP*1000) %>% select(-Units)
GDP <- GDP  %>% filter(Region !='All Regions')
GDP$Region <- gsub(" County","",GDP$Region)
KEY_REGIONS <- GDP %>% group_by(Region) %>% summarize(GDP = median(GDP)) %>% arrange(desc(GDP))  %>% filter(GDP>0) %>% pull(Region)
OTHER_GDP <- GDP %>% filter(!(Region %in% KEY_REGIONS)) %>% group_by(Year) %>% summarize(Region="Other Counties",GDP=sum(GDP)) %>% ungroup
GDP <- rbind(GDP %>% filter(Region %in% KEY_REGIONS),OTHER_GDP)
GDP$Region <- factor(GDP$Region,levels=rev(c("Other Counties",KEY_REGIONS)))
GDP <- GDP %>% filter(Year>=2029)

GDP_SUB_REGION_PLOT <- ggplot(GDP %>% filter(Region!='Lincoln'),aes(x=Year,y=GDP,fill=Region))+geom_area(position='stack')+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,30,by=1))+theme(legend.position = "top")+theme_bw()+ylab("GDP (Million USD)")+scale_fill_manual(values=paletteer_d("PNWColors::Shuksan2"))
png(paste0(OUTPUT_DIR,"GDP_Other_Counties.png"), units = "in", width = 10, height = 8, res = 600)
	GDP_SUB_REGION_PLOT 
dev.off()


GDP_REGION_SUMMARY <- GDP %>% mutate(Region=factor(ifelse(Region=='Lincoln','Lincoln','Rest of Wyoming'),levels=rev(c('Lincoln','Rest of Wyoming')))) %>% group_by(Region,Year) %>% summarize(GDP=sum(GDP)) %>% ungroup
GDP_PLOT <- ggplot(GDP_REGION_SUMMARY ,aes(x=Year,y=GDP,fill=Region))+geom_area(position='stack')+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,1000,by=10))+theme(legend.position = "top")+theme_bw()+ylab("GDP (Million USD)")+scale_fill_manual(values=c(paletteer_d("PNWColors::Shuksan2")[c(5)],"mediumpurple4"))+theme(legend.position = "top")

png(paste0(OUTPUT_DIR,"GDP_Region_Plot.png"), units = "in", width = 10, height = 8, res = 600)
	GDP_PLOT 
dev.off()




###################
INDUSTRY_JOBS <- read_csv(paste0(DATA_DIR,'Employment- By Industry - Employment by Industry.csv'),skip=5) %>% pivot_longer(c(-Industry,-Units),names_to="Year",values_to="Jobs") %>%mutate(Year=parse_number(Year)) %>% filter(Industry!='All Industries') %>% select(-Units) %>% filter(Year>=2029)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Data processing, hosting, and related services; Other information services',"Data processing",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Professional, scientific, and technical services',"Technical services",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Administrative and support services',"Administrative services",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='State and Local Government',"Government",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Food services and drinking places',"Restaurants",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Other transportation equipment manufacturing',"Equipment manufacturing",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Repair and maintenance',"Maintenance",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Utilities',"Energy Production",INDUSTRY_JOBS$Industry)
INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Ambulatory health care services',"Hospitals",INDUSTRY_JOBS$Industry)






INDUSTRY_JOBS <-  INDUSTRY_JOBS  %>% group_by(Year)  %>% mutate(Rank=rank(-Jobs)) %>% mutate(Rank=ifelse(Rank>10,11,Rank),Industry=ifelse(Rank==11,"Other",Industry)) %>% group_by(Year,Industry,Rank) %>% summarize(Jobs=sum(Jobs)) %>% ungroup(Industry,Rank)  %>% arrange(Year,Rank)  %>% ungroup
#INDUSTRY_JOBS  %>% pull(Industry) %>% unique

ORDER <- c(INDUSTRY_JOBS %>% filter(Industry!='Other')  %>% group_by(Industry) %>% summarize(MEAN=mean(Jobs)) %>% arrange(desc(MEAN)) %>% pull(Industry) %>% unique,"Other")
INDUSTRY_JOBS$Industry <- factor(INDUSTRY_JOBS$Industry,levels=ORDER)

JOB_TYPE_PLOT <- 	ggplot(INDUSTRY_JOBS ,aes(x=Year,y=Jobs,fill=Industry))+geom_bar(stat='identity')+ paletteer::scale_fill_paletteer_d("colorBlindness::Blue2DarkRed18Steps")+theme_bw()+theme(text = element_text(size = 20),legend.position = "top")+guides(fill=guide_legend(nrow=2,byrow=TRUE)) +scale_x_continuous(breaks=seq(2029,2060,by=1))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=250))
png(paste0(OUTPUT_DIR,"Job_Dist_Plot.png"), units = "in", width = 22, height = 16, res = 600)
	JOB_TYPE_PLOT 
dev.off()


JOB_FACET <- ggplot(INDUSTRY_JOBS %>% filter(Year %in% c(2032,2040)),aes(x=factor(Year),y=Jobs,fill=factor(Year)))+geom_bar(stat='identity',width=0.99)+facet_wrap(.~Industry)+theme_bw()+xlab("Year")+theme(text=element_text(size=16),legend.position="top",palette.colour.discrete=c("darkslategray1","darkorchid1" ))+scale_fill_discrete(name = "Year") +scale_y_continuous(labels = scales::comma,breaks=seq(0,1000,by=100))

png(paste0(OUTPUT_DIR,"Job_Facet_Plot.png"), units = "in", width = 11, height = 11, res = 600)
	JOB_FACET 
dev.off()
##################GDP
read_csv(paste0(DATA_DIR,'Gross Domestic Product - By Component - GDP Components.csv'),skip=5)
GDP_TOTAL <-      	read_csv(paste0(DATA_DIR,'Gross Domestic Product - By Component - GDP Components.csv'),skip=5)%>% select(-Units) %>% pivot_longer(c(-Category),names_to="Year") %>% mutate(value=value*1000,Year=parse_number(Year))  %>% filter(Year>=2029)

GDP_TOTAL <- GDP_TOTAL %>% filter(Category %in% c('Gross Domestic Product (GDP)','Consumption','Investment','Change in Private Inventories','Net Trade','Government Spending','Exogenous Final Demand'))
GDP_TOTAL <- GDP_TOTAL %>% filter(Category=='Gross Domestic Product (GDP)') %>% select(-Category) %>% rename(GDP=value)
GDP_PLOT <- ggplot(GDP_TOTAL ,aes(x=Year,y=GDP))+geom_line(linewidth=1,color='slateblue')+theme_bw()+theme(text=element_text(size=16),legend.position="top")+ylab("Wyoming GDP (Million USD)")+scale_x_continuous(breaks=c(seq(2030,2060,by=2)))+scale_y_continuous(breaks=seq(0,500,by=25))

png(paste0(OUTPUT_DIR,"GDP_Time_Plot.png"), units = "in", width = 11, height = 8, res = 600)
	GDP_PLOT	
dev.off()
################Personal Income
PERSONAL_INCOME <- read_csv(paste0(DATA_DIR,'Personal Income - By Region - Personal Income by Region.csv'),skip=5)%>% select(-Units) %>% pivot_longer(c(-Region),names_to="Year") %>% mutate(value=value*1000,Year=parse_number(Year))  %>% filter(Year>=2029) %>% rename(Income=value)

TOTAL_PERSONAL_INCOME <- PERSONAL_INCOME  %>% filter(Region=='All Regions') %>% select(-Region)
TOTAL_PERSONAL_INCOME 

TAXES <- read_csv("Results/Tax_PI/Revenues.csv",skip=5) %>% clean_names() %>% filter(!is.na(revenue))
colnames(TAXES) <- gsub("fy","",colnames(TAXES))
TAXES <- TAXES %>% pivot_longer(c(-revenue,-units),names_to='year',values_to='Taxes') %>% mutate(Taxes=Taxes*1000) %>% select(-units)
TAXES 
TAXES$revenue <- gsub('State Sales & Use Taxes - ','Sales Tax: ', TAXES$revenue)
TAXES <- TAXES %>% filter(year>2028,!is.na(revenue))
TAXES <- TAXES  %>% filter(Taxes>0)
TAXES$revenue[!grepl("Sales",TAXES$revenue)] <- 'Other Taxes'
TAXES <- TAXES %>% rename(Revenue='Taxes','Tax'=revenue,'Year'=year) %>% select(Year,Tax,Revenue)
TAXES <- TAXES %>% group_by(Year,Tax) %>% summarize(Revenue=sum(Revenue))
TOTAL_TAXES <- TAXES %>% group_by(Year) %>% summarize('Million (USD)'=sum(Revenue)/10^6,Metric='Wyoming Taxes') %>% mutate(Year=as.numeric(Year))







DOLLAR_VALUES <- rbind(OUTPUT_NUM %>% filter(Category=='Total') %>% select(-Category) %>% mutate(Metric='Economic Output') %>% rename('Million (USD)'=Output),
GDP_TOTAL %>% mutate(Metric='GDP') %>% rename('Million (USD)'=GDP),
TOTAL_TAXES,
TOTAL_PERSONAL_INCOME %>% mutate(Metric='Wages Paid') %>% rename('Million (USD)'=Income))

FACET_INDICATORS_PLOT <- ggplot(DOLLAR_VALUES, aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+facet_wrap(.~Metric,nrow=2)+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )
DOLLAR_VALUES
FACET_INDICATORS_PLOT <- ggplot(DOLLAR_VALUES %>% filter(!(Metric %in% c('Wyoming Taxes'))), aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+facet_wrap(.~Metric,nrow=3)+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )
FACET_INDICATORS_PLOT 



TAX_PLOT <- ggplot(DOLLAR_VALUES %>% filter(Metric %in% c('Wyoming Taxes')), aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+facet_wrap(.~Metric,nrow=2)+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=0.5))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus")[4] )
TAX_PLOT 

FACET_INDICATORS_PLOT 

png(paste0(OUTPUT_DIR,"Facet_Indicator_Plot.png"), units = "in", width = 1.5*11, height = 1.5*10, res = 600)
	FACET_INDICATORS_PLOT 
dev.off()



#############
Period_Indicator_Summary <- rbind(OUTPUT_NUM %>% filter(Category=='Total') %>% mutate(Period=ifelse(Year<=2033,"Construction","Operation")) %>% group_by(Period) %>% summarize(Metric='Economic Output','Average'=mean(Output),Max=max(Output)),GDP %>% mutate(Period=ifelse(Year<=2033,"Construction","Operation")) %>% group_by(Period) %>% summarize(Metric='GDP',Average=mean(GDP),Max=max(GDP)),EMPLOY_NUM %>% mutate(Period=ifelse(Year<=2033,"Construction","Operation")) %>% group_by(Period) %>% summarize(Metric='Employment',Average=mean(Employment),Max=max(Employment))) 
Period_Indicator_Summary[,c(3:4)] <- round(Period_Indicator_Summary[,c(3:4)],0)
Period_Indicator_Summary

write_csv(Period_Indicator_Summary,paste0(OUTPUT_DIR,"Economic_Summary_Indicator.csv"  ),col_names=TRUE)

GDP_SUMMARY <- GDP %>% group_by(Year) %>% summarize(GDP=sum(GDP),NPV_3=(GDP/((1+0.03)^(Year-2029))),NPV_5=(GDP/((1+0.05)^(Year-2029))),NPV_7=(GDP/((1+0.07)^(Year-2029)))) %>% ungroup
NPV_3 <- GDP_SUMMARY %>% pull(NPV_3) %>% sum
NPV_5 <-  GDP_SUMMARY %>% pull(NPV_5) %>% sum
NPV_7 <-  GDP_SUMMARY %>% pull(NPV_7) %>% sum
NPV_SUMMARY <- round(t(c(NPV_3,NPV_5,NPV_7)),0) %>% as_tibble
colnames(NPV_SUMMARY) <- c('3%','5%','7%')
write_csv(NPV_SUMMARY,paste0(OUTPUT_DIR,"GDP_Net_Present_Value.csv"  ))