85 lines
4.6 KiB
R
85 lines
4.6 KiB
R
library(tidyverse)
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DATA_PATH <- "./Raw_Output/"
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source("Scripts/Data_Proc_Script.r")
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EVENT_DATA <- GET_EVENT_DATA(2025:2031)
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write_csv(EVENT_DATA,"./Results/Yearly_Detailed_Event_Data.csv")
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DATA <- GET_SUMMARY_DATA()
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DATA[,c(-2:-6,-8:-10)] <- round(DATA[,c(-2:-6,-8:-10)])
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DATA[,c(3:6,8:10)] <- round(DATA[,c(3:6,8:10)],2)
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DATA[2,3:6] <- NA
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write_csv(DATA,"./Results/Yearly_Event_Summary.csv")
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DETAILED_DATA <- GET_DETAIL_ECON_DATA()
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write_csv(DETAILED_DATA,"./Results/Detailed_Economic_Indicators.csv")
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SUMMARY_BY_YEAR <- round(GET_TOTAL_SUMMARY())
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write_csv(SUMMARY_BY_YEAR,"./Results/Yearly_State_Totals.csv")
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#Advanced Summary Tables
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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))
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TOP_20 <- EMP_OUTPUTS_CONSTRUCTION[1:20,]
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OTHERS <- TOP_20[1,]
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OTHERS[1,1] <-"Other Industries"
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OTHERS[,-1] <- t(colSums(EMP_OUTPUTS_CONSTRUCTION[-1-20,-1] ))
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EMP_OUTPUTS_CONSTRUCTION <-rbind(TOP_20,OTHERS)
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EMP_OUTPUTS_CONSTRUCTION[,-1] <- round(EMP_OUTPUTS_CONSTRUCTION[,-1])
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write_csv(EMP_OUTPUTS_CONSTRUCTION,"./Results/Top_20_Employmnet_During_Development_(2029).csv")
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EMP_OUTPUTS_CONSTRUCTION%>% print(n=100)
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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)
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COUNTY_OUTPUT_CONSTRUCTION[,-1] <- round(COUNTY_OUTPUT_CONSTRUCTION[,-1])
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write_csv(COUNTY_OUTPUT_CONSTRUCTION,"./Results/County_Outcomes_During_Development_(2029).csv")
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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)
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TOP_20 <- EMP_OUTPUTS_OPERATING[1:20,]
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OTHERS <- TOP_20[1,]
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OTHERS[1,1] <-"Other Industries"
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OTHERS[,-1] <- t(colSums(EMP_OUTPUTS_OPERATING[-1-20,-1] ))
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EMP_OUTPUTS_OPERATING <-rbind(TOP_20,OTHERS)
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EMP_OUTPUTS_OPERATING[,-1] <- round(EMP_OUTPUTS_OPERATING[,-1])
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write_csv(EMP_OUTPUTS_OPERATING,"./Results/Top_20_Yearly_Employmnet_During_Operation_(2030+).csv")
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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))
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COUNTY_OUTPUTS_OPERATING[,-1] <- round(COUNTY_OUTPUTS_OPERATING[,-1])
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write_csv(COUNTY_OUTPUTS_OPERATING,"./Results/County_Yearly_Outcomes_During_Operation_(2030+).csv")
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#TAX_FILE <- FILES[grep("Taxes",FILES)]
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ggplot(aes(x=YEAR,y=EMP,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
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ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
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EVENT_DATA$MAJOR_EVENT <- ifelse(EVENT_DATA$MAJOR_EVENT=="Income","Royalties and income",EVENT_DATA$MAJOR_EVENT)
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ORD <- EVENT_DATA$MAJOR_EVENT %>% unique
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EVENT_DATA$MAJOR_EVENT <- factor(EVENT_DATA$MAJOR_EVENT,levels=ORD)
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ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")
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ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
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ylab("Wyoming Taxes (Million USD)") +
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xlab("Year")+
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labs(fill='Economic Event')+
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scale_x_continuous(breaks=2025:2031)+
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theme(legend.position = "top")+
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theme(text=element_text(size=20))
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ggplot(aes(x=YEAR,y=EMP,group=MAJOR_EVENT,fill=MAJOR_EVENT),data=EVENT_DATA)+geom_bar(stat = "identity")+
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ylab("Wyoming Added Employment (Person-Years)") +
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xlab("Year")+
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labs(fill='Economic Event')+
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scale_x_continuous(breaks=2025:2031)+
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theme(legend.position = "top")+
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theme(text=element_text(size=20))
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ggplot(aes(x=YEAR,y=WY_TOTAL_TAX/10^6,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
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ylab("Wyoming Taxes (Million USD)") +
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xlab("Year")+
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labs(fill='Impact Type')+
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scale_x_continuous(breaks=2025:2031)+
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theme(legend.position = "top")+
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theme(text=element_text(size=20))
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ggplot(aes(x=YEAR,y=EMP,group=IMPACT_TYPE,fill=IMPACT_TYPE),data=EVENT_DATA)+geom_bar(stat = "identity")+
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ylab("Wyoming Added Employment (Person-Years)") +
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xlab("Year")+
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labs(fill='Impact Type')+
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scale_x_continuous(breaks=2025:2031)+
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theme(legend.position = "top")+
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theme(text=element_text(size=20))
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