library(tidyverse)
DATA_PATH <- "./Raw_Output/"
source("Scripts/Data_Proc_Script.r")
EVENT_DATA <- GET_EVENT_DATA(2025:2031)
  write_csv(EVENT_DATA,"./Results/Yearly_Detailed_Event_Data.csv")
DATA <- GET_SUMMARY_DATA()
  DATA[,c(-2:-6,-8:-10)] <- round(DATA[,c(-2:-6,-8:-10)])
  DATA[,c(3:6,8:10)] <- round(DATA[,c(3:6,8:10)],2)
  DATA[2,3:6] <- NA
  write_csv(DATA,"./Results/Yearly_Event_Summary.csv")


DETAILED_DATA <- GET_DETAIL_ECON_DATA()
write_csv(DETAILED_DATA,"./Results/Detailed_Economic_Indicators.csv")

SUMMARY_BY_YEAR <- round(GET_TOTAL_SUMMARY())
write_csv(SUMMARY_BY_YEAR,"./Results/Yearly_State_Totals.csv")

#Advanced Summary Tables
EMP_OUTPUTS_CONSTRUCTION <- DETAILED_DATA %>% filter(YEAR<=2029) %>% group_by(IND_DESC)  %>% summarize('Employment'=sum(EMP),'Income'=sum(EMP_COMP+PROP_INC),'Other Profits'=sum(OPI)) %>% arrange(desc(Employment))
TOP_20 <- EMP_OUTPUTS_CONSTRUCTION[1:20,] 
OTHERS <- TOP_20[1,]
OTHERS[1,1] <-"Other Industries"
OTHERS[,-1] <- t(colSums(EMP_OUTPUTS_CONSTRUCTION[-1-20,-1] ))
EMP_OUTPUTS_CONSTRUCTION <-rbind(TOP_20,OTHERS)
EMP_OUTPUTS_CONSTRUCTION[,-1] <- round(EMP_OUTPUTS_CONSTRUCTION[,-1])
write_csv(EMP_OUTPUTS_CONSTRUCTION,"./Results/Top_20_Employmnet_During_Development_(2029).csv")
EMP_OUTPUTS_CONSTRUCTION%>% print(n=100)

COUNTY_OUTPUT_CONSTRUCTION <- EVENT_DATA %>% filter(YEAR<=2029) %>% group_by(COUNTY) %>% summarize(EMP=sum(EMP),OUTPUT=sum(OUTPUT),VALUE_ADDED=sum(EMP_COM+PROP_INC+TOPI+OPI),COUNTY_TAX=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX),WY_TAX=sum(STATE_TAX)) %>% arrange(desc(EMP)) %>% print(n=200)
COUNTY_OUTPUT_CONSTRUCTION[,-1] <- round(COUNTY_OUTPUT_CONSTRUCTION[,-1])
write_csv(COUNTY_OUTPUT_CONSTRUCTION,"./Results/County_Outcomes_During_Development_(2029).csv")

EMP_OUTPUTS_OPERATING <- DETAILED_DATA %>% filter(YEAR==2030) %>% group_by(IND_DESC)  %>% summarize('Employment'=sum(EMP),'Income'=sum(EMP_COMP+PROP_INC),'Other Profits'=sum(OPI)) %>% arrange(desc(Employment)) %>% print(n=50)
TOP_20 <- EMP_OUTPUTS_OPERATING[1:20,] 
OTHERS <- TOP_20[1,]
OTHERS[1,1] <-"Other Industries"
OTHERS[,-1] <- t(colSums(EMP_OUTPUTS_OPERATING[-1-20,-1] ))
EMP_OUTPUTS_OPERATING <-rbind(TOP_20,OTHERS) 
EMP_OUTPUTS_OPERATING[,-1] <- round(EMP_OUTPUTS_OPERATING[,-1])
write_csv(EMP_OUTPUTS_OPERATING,"./Results/Top_20_Yearly_Employmnet_During_Operation_(2030+).csv")

COUNTY_OUTPUTS_OPERATING<- EVENT_DATA %>% filter(YEAR==2030) %>% group_by(COUNTY) %>% summarize(EMP=sum(EMP),OUTPUT=sum(OUTPUT),VALUE_ADDED=sum(EMP_COM+PROP_INC+TOPI+OPI),COUNTY_TAX=sum(SUBCOUNTY_TAX+SPECIAL_TAX+COUNTY_TAX),WY_TAX=sum(STATE_TAX)) %>% arrange(desc(EMP)) %>% print(n=200) %>% arrange(desc(EMP))
COUNTY_OUTPUTS_OPERATING[,-1] <- round(COUNTY_OUTPUTS_OPERATING[,-1])
write_csv(COUNTY_OUTPUTS_OPERATING,"./Results/County_Yearly_Outcomes_During_Operation_(2030+).csv")

#TAX_FILE <- FILES[grep("Taxes",FILES)]
ggplot(aes(x=YEAR,y=EMP,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
EVENT_DATA$MAJOR_EVENT <- ifelse(EVENT_DATA$MAJOR_EVENT=="Income","Royalties and income",EVENT_DATA$MAJOR_EVENT)
ORD <- EVENT_DATA$MAJOR_EVENT %>% unique
EVENT_DATA$MAJOR_EVENT <- factor(EVENT_DATA$MAJOR_EVENT,levels=ORD)
ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")

ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
    ylab("Wyoming Taxes (Million USD)")  + 
  xlab("Year")+
  labs(fill='Economic Event')+
  scale_x_continuous(breaks=2025:2031)+
  theme(legend.position = "top")+
  theme(text=element_text(size=20))
ggplot(aes(x=YEAR,y=EMP,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
  ylab("Wyoming Added Employment (Person-Years)")  + 
  xlab("Year")+
  labs(fill='Economic Event')+
  scale_x_continuous(breaks=2025:2031)+
  theme(legend.position = "top")+
  theme(text=element_text(size=20))

ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
  ylab("Wyoming Taxes (Million USD)")  + 
  xlab("Year")+
  labs(fill='Impact Type')+
  scale_x_continuous(breaks=2025:2031)+
  theme(legend.position = "top")+
  theme(text=element_text(size=20))

ggplot(aes(x=YEAR,y=EMP,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
  ylab("Wyoming Added Employment (Person-Years)")  + 
  xlab("Year")+
  labs(fill='Impact Type')+
  scale_x_continuous(breaks=2025:2031)+
  theme(legend.position = "top")+
  theme(text=element_text(size=20))