Updating data
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alex
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514cf63d2d
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df37249806
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.gitignore
vendored
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.gitignore
vendored
@ -1,4 +1,5 @@
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# ---> R
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/Results
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*.lock
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# History files
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.Rhistory
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@ -1,17 +1,84 @@
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library(tidyverse)
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DATA_PATH <- "./Raw_Output/"
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source("Scripts/Data_Proc_Script.r")
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DAT <- GET_ALL_DATA(DATA_PATH)
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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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COUNTY_DATA <-DAT %>% group_by(Year,Major_Event,County_Name) %>% summarize(Employment =sum(Employment ),Labor_Income =sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output),Subcounty=sum(Subcounty),Special=sum(Special),County=sum(County),State=sum(State),STATE_TOTAL=sum(STATE_TOTAL),Federal=sum(Federal))
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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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STATE_EVENT_DATA <- DAT %>% group_by(Year,Major_Event) %>% summarize(Employment =sum(Employment ),Labor_Income =sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output),Subcounty=sum(Subcounty),Special=sum(Special),County=sum(County),State=sum(State),STATE_TOTAL=sum(STATE_TOTAL),Federal=sum(Federal))
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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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STATE_TYPE_DATA <- DAT %>% group_by(Year,Impact) %>% summarize(Employment =sum(Employment ),Labor_Income =sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output),Subcounty=sum(Subcounty),Special=sum(Special),County=sum(County),State=sum(State),STATE_TOTAL=sum(STATE_TOTAL),Federal=sum(Federal))
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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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ggplot(aes(x=Year,y=Employment,group-Impact,fill=Impact),data=STATE_TYPE_DATA)+geom_bar(stat = "identity")
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ggplot(aes(x=Year,y=STATE_TOTAL/10^6,group=Impact,fill=Impact),data=STATE_TYPE_DATA)+geom_bar(stat = "identity")+ylab("Wyoming Taxes (Million USD)")
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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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ggplot(aes(x=Year,y=Employment,group-Major_Event,fill=Major_Event),data=STATE_EVENT_DATA)+geom_bar(stat = "identity") + scale_fill_discrete(name = "Economic Event")
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ggplot(aes(x=Year,y=STATE_TOTAL/10^6,group=Major_Event,fill=Major_Event),data=STATE_EVENT_DATA)+geom_bar(stat = "identity") + scale_fill_discrete(name = "Economic Event")+ylab("Wyoming Taxes (Million USD)")
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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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@ -28,6 +28,5 @@ GET_INCOME_ADJ <- function(YEAR){
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PROFIT_ADJ <- TOTAL_PROFIT-REPORTED_PROFIT
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return(PROFIT_ADJ)
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}
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CYEAR <- 2030
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GET_INCOME_ADJ(CYEAR)
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GET_TRANSPORT_ADJ(CYEAR)
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#New scripts
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GET_YEAR_DATA <- function(C_YEAR,DATA_PATH){
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DATA_PATH="./Raw_Output/Final_Output/"
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C_YEAR=2025
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FILES <- list.files(DATA_PATH)
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FILES <- paste0(DATA_PATH,FILES[grep(paste0(C_YEAR,"-"),FILES)])
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GET_DATA_ECON_YEAR <- function(YEAR){
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DAT_YEAR <- ifelse(YEAR>2030,2030,YEAR)
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HEADER <- paste0('./Raw_Output/Final_Output/')
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FILES <- list.files(HEADER)
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FILES <- paste0(HEADER,FILES[grep(paste0(DAT_YEAR,"-"),FILES)])
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ECON_FILE <- FILES[grep("Economic",FILES)]
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TAX_FILE <- FILES[grep("Tax",FILES)]
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ECON <- read_csv(ECON_FILE)
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TAX <- read_csv(TAX_FILE)
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FILE_LIST <- list.files(paste0(DATA_PATH,C_YEAR))
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TAX_FILE <- paste0(DATA_PATH,C_YEAR,"/",FILE_LIST[grep("Taxes",FILE_LIST)])
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ECON_FILE <- paste0(DATA_PATH,C_YEAR,"/",FILE_LIST[grep("Economic",FILE_LIST)])
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DAT <- read_csv(ECON_FILE)[,-1] %>% select(-TagName)
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colnames(DAT) <- c("Event","County_Name","Impact","Employment","Labor_Income","Value_Added","Output")
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DAT <- DAT[!is.na(DAT[,1]),]
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TAX <- read_csv(TAX_FILE)[,-1] %>% select(-TagName)
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colnames(TAX) <- c("Event","County_Name","Impact","Subcounty","Special","County","State","Federal","Total")
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TAX <- TAX[!is.na(TAX[,1]),]
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TAX$STATE_TOTAL <- rowSums(TAX[,4:7])
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DAT <- full_join(DAT,TAX)
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#rm(TAX)
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DAT$County_Name <- gsub(" County, WY \\(2023\\)","",DAT$County_Name)
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DAT$Impact <- gsub(" - ","",gsub("1","",gsub("2","",gsub("3","",DAT$Impact))))
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DAT$Group <- NA
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DAT[grep('Skid',DAT$Event),"Group"] <- "Skid"
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DAT[grep('Purchase',DAT$Event),"Group"] <- "Beet Purchase"
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DAT[grep('Truck',DAT$Event),"Group"] <- "Transportation"
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DAT[DAT$Event=="Gas Production (Campbell)","Group"] <- "Gas Production"
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DAT[is.na(DAT$Group),"Group"] <- "Processing Facilities"
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#Remove the direct transportation effects induced by by beet purchases, by adding in a negative impact of rail and truck equal to the direct effect found in the first run.
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DAT[grep('Adjustment',DAT$Event),"Group"] <- "Beet Purchase"
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DAT <- DAT %>% select(Minor_Event=Event,Major_Event=Group,everything())
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DAT$Year <- C_YEAR
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DAT <-DAT %>% select(Year,everything())
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return(DAT)
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ECON <- ECON %>% rename(COUNTY=DestinationRegion,EVENT=EventName,IMPACT_TYPE=ImpactType,EVENT=EventName) %>% rename(TOPI=TaxesOnProductionAndImports,OUTPUT=Output,EMP=Employment,EMP_COMP=EmployeeCompensation,PROP_INC=ProprietorIncome,WAGE_EMP=WageAndSalaryEmployment,PROP_EMP=ProprietorEmployment,OPI=OtherPropertyIncome,IND_CODE=IndustryCode,IND_DESC=IndustryDescription) %>% select(-OriginRegion,-TagName)
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ECON$COUNTY <- gsub(" County, WY \\(2023\\)","",ECON$COUNTY )
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ECON <- ECON %>% filter(COUNTY!="Totals")
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ECON$MAJOR_EVENT <- NA
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ECON$MAJOR_EVENT %>% unique
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ECON$MAJOR_EVENT <- ifelse(ECON$EVENT %in% c("Royalties EnWyo (Albany)","Royalties UW (Albany)","Total Profits (Campbell)" ),"Income", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT <- ifelse(ECON$EVENT %in% c("Well Equipment (Campbell)","Well Field Support (Campbell)","Well Road Work (Campbell)" ),"Skid development", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT <- ifelse(ECON$EVENT=="Natural Gas Prod (Campbell)" ,"Natural gas prod.", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT <- ifelse(ECON$EVENT %in% c("Transportation (Goshen)","Transportation (Weston)","Transportation (Washakie)" ),"Transportation", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT <- ifelse(ECON$EVENT %in% c("Beet Proc. Captital (Washakie)","Beet Proc. Captital (Goshen)"),"Facility construction", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT <- ifelse(ECON$EVENT %in% c("Proc. Op Cost (Goshen)","Proc. Op Cost (Washakie)"),"Beet processing", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT <- ifelse(ECON$EVENT %in% c("Beet Purchase (Washakie)","SBS purchase (Washakie)","Beet Purchase (Goshen)"),"Beet purchases", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT <- ifelse(is.na(ECON$MAJOR_EVENT),"Beet purchases", ECON$MAJOR_EVENT )
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ECON$MAJOR_EVENT %>% unique
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ECON$YEAR <- YEAR
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ECON <- ECON %>% select(YEAR,COUNTY,MAJOR_EVENT,everything())
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return(ECON)
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}
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GET_ALL_DATA <- function(DATA_PATH){
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YEARS_OF_DATA <- list.files(DATA_PATH)
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for(i in 1:length(YEARS_OF_DATA)){
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if(!exists("RES")){
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RES<- GET_YEAR_DATA(YEARS_OF_DATA[i],DATA_PATH)
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}else{
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RES<- rbind(RES,GET_YEAR_DATA(YEARS_OF_DATA[i],DATA_PATH)) %>% as_tibble
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}
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}
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return(RES)
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GET_DATA_TAX_YEAR <- function(YEAR){
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DAT_YEAR <- ifelse(YEAR>2030,2030,YEAR)
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HEADER <- paste0('./Raw_Output/Final_Output/')
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FILES <- list.files(HEADER)
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FILES <- paste0(HEADER,FILES[grep(paste0(DAT_YEAR,"-"),FILES)])
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TAX_FILE <- FILES[grep("Tax",FILES)]
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TAX <- read_csv(TAX_FILE)
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TAX <- TAX %>% rename(COUNTY_TAX=County,STATE_TAX=State,FEDERAL_TAX=Federal,SUBCOUNTY_TAX=SubCountyGeneral,SPECIAL_TAX=SubCountySpecialDistricts,COUNTY=ModelName,TOTAL_TAX=Total,IMPACT_TYPE=Impact,EVENT=EventName) %>% select(-GroupName,-TagName)
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TAX <- TAX %>% mutate(WY_TOTAL_TAX=TOTAL_TAX-FEDERAL_TAX)
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TAX$COUNTY <- gsub(" County, WY \\(2023\\)","",TAX$COUNTY )
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TAX <- TAX %>% filter(COUNTY!="Totals")
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TAX$IMPACT_TYPE <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",TAX$IMPACT_TYPE)))
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TAX$MAJOR_EVENT <- NA
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TAX$MAJOR_EVENT %>% unique
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TAX$MAJOR_EVENT <- ifelse(TAX$EVENT %in% c("Royalties EnWyo (Albany)","Royalties UW (Albany)","Total Profits (Campbell)" ),"Income", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT <- ifelse(TAX$EVENT %in% c("Well Equipment (Campbell)","Well Field Support (Campbell)","Well Road Work (Campbell)" ),"Skid development", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT <- ifelse(TAX$EVENT=="Natural Gas Prod (Campbell)" ,"Natural gas prod.", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT <- ifelse(TAX$EVENT %in% c("Transportation (Goshen)","Transportation (Weston)","Transportation (Washakie)" ),"Transportation", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT <- ifelse(TAX$EVENT %in% c("Beet Proc. Captital (Washakie)","Beet Proc. Captital (Goshen)"),"Facility construction", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT <- ifelse(TAX$EVENT %in% c("Proc. Op Cost (Goshen)","Proc. Op Cost (Washakie)"),"Beet processing", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT <- ifelse(TAX$EVENT %in% c("Beet Purchase (Washakie)","SBS purchase (Washakie)","Beet Purchase (Goshen)"),"Beet purchases", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT <- ifelse(is.na(TAX$MAJOR_EVENT),"Beet purchases", TAX$MAJOR_EVENT )
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TAX$MAJOR_EVENT %>% unique
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TAX$YEAR <- YEAR
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TAX <- TAX %>% select(YEAR,COUNTY,MAJOR_EVENT,everything())
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return(TAX)
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}
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GET_EVENT_DATA_YEAR <- function(DAT_YEAR){
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ECON_EVENT_LEVEL <- GET_DATA_ECON_YEAR(DAT_YEAR) %>% group_by(YEAR,COUNTY,MAJOR_EVENT,EVENT,IMPACT_TYPE) %>% summarize(OUTPUT=sum(OUTPUT),EMP=sum(EMP),WAGE_EMP=sum(WAGE_EMP),PROP_EMP=sum(PROP_EMP),EMP_COM=sum(EMP_COMP),PROP_INC=sum(PROP_INC),TOPI=sum(TOPI),OPI=sum(OPI))
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RES <- ECON_EVENT_LEVEL %>% full_join(GET_DATA_TAX_YEAR(DAT_YEAR))
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RES[is.na(RES)] <- 0
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return(RES)
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}
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GET_MAJOR_EVENT_DATA_YEAR <- function(DAT_YEAR){
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ECON_EVENT_LEVEL <- GET_DATA_ECON_YEAR(DAT_YEAR) %>% group_by(YEAR,COUNTY,MAJOR_EVENT,IMPACT_TYPE) %>% summarize(OUTPUT=sum(OUTPUT),EMP=sum(EMP),WAGE_EMP=sum(WAGE_EMP),PROP_EMP=sum(PROP_EMP),EMP_COM=sum(EMP_COMP),PROP_INC=sum(PROP_INC),TOPI=sum(TOPI),OPI=sum(OPI))
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TAX_EVENT_LEVEL <- GET_DATA_TAX_YEAR(DAT_YEAR)%>% group_by(YEAR,COUNTY,MAJOR_EVENT,IMPACT_TYPE) %>% summarize(SUBCOUNTY_TAX=sum(SUBCOUNTY_TAX),SPECIAL_TAX=sum(SPECIAL_TAX),COUNTY_TAX=sum(COUNTY_TAX),STATE_TAX=sum(STATE_TAX),TOTAL_TAX=sum(TOTAL_TAX),WY_TOTAL_TAX=sum(WY_TOTAL_TAX))
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RES <- ECON_EVENT_LEVEL %>% full_join(TAX_EVENT_LEVEL)
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RES[is.na(RES)] <- 0
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return(RES)
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}
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GET_TOTAL_YEAR <- function(DAT_YEAR){
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ECON_EVENT_LEVEL <- GET_DATA_ECON_YEAR(DAT_YEAR) %>% group_by(YEAR) %>% summarize(OUTPUT=sum(OUTPUT),EMP=sum(EMP),WAGE_EMP=sum(WAGE_EMP),PROP_EMP=sum(PROP_EMP),EMP_COM=sum(EMP_COMP),PROP_INC=sum(PROP_INC),TOPI=sum(TOPI),OPI=sum(OPI))
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TAX_EVENT_LEVEL <- GET_DATA_TAX_YEAR(DAT_YEAR)%>% group_by(YEAR) %>% summarize(SUBCOUNTY_TAX=sum(SUBCOUNTY_TAX),SPECIAL_TAX=sum(SPECIAL_TAX),COUNTY_TAX=sum(COUNTY_TAX),STATE_TAX=sum(STATE_TAX),TOTAL_TAX=sum(TOTAL_TAX),WY_TOTAL_TAX=sum(WY_TOTAL_TAX))
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RES <- ECON_EVENT_LEVEL %>% full_join(TAX_EVENT_LEVEL)
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RES[is.na(RES)] <- 0
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return(RES)
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}
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GET_EVENT_SUMMARY_YEAR <- function(DAT_YEAR){
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RES <- GET_MAJOR_EVENT_DATA_YEAR(DAT_YEAR) %>% group_by(YEAR,MAJOR_EVENT) %>% summarize(TAX_DIR_PER=sum(ifelse(IMPACT_TYPE=="Direct",WY_TOTAL_TAX,0))/sum(WY_TOTAL_TAX),EMP_DIR_PER=sum(ifelse(IMPACT_TYPE=="Direct",EMP,0))/sum(EMP),TAX_INDIR_PER=sum(ifelse(IMPACT_TYPE=="Indirect",WY_TOTAL_TAX,0))/sum(WY_TOTAL_TAX),EMP_INDIR_PER=sum(ifelse(IMPACT_TYPE=="Indirect",EMP,0))/sum(EMP),OUTPUT=sum(OUTPUT),EMP=sum(EMP), WAGE_EMP=sum(WAGE_EMP),PROP_EMP=sum(PROP_EMP), EMP_COM=sum(EMP_COM),PROP_INC=sum(PROP_INC),TOPI=sum(TOPI),OPI=sum(OPI), SUBCOUNTY_TAX=sum(SUBCOUNTY_TAX), SPECIAL_TAX=sum(SPECIAL_TAX),COUNTY_TAX=sum(COUNTY_TAX), STATE_TAX=sum(STATE_TAX), TOTAL_TAX=sum(TOTAL_TAX), WY_TOTAL_TAX=sum(WY_TOTAL_TAX))
|
||||
return(RES)
|
||||
}
|
||||
GET_SUMMARY_DATA <- function(YEARS=2025:2034){
|
||||
RES <- GET_EVENT_SUMMARY_YEAR(YEARS[1])
|
||||
for(i in 2:length(YEARS)){
|
||||
RES <- rbind(RES,GET_EVENT_SUMMARY_YEAR(YEARS[i]))
|
||||
}
|
||||
return(RES)
|
||||
}
|
||||
GET_EVENT_SUMMARY_YEAR <- function(DAT_YEAR){
|
||||
RES <- GET_MAJOR_EVENT_DATA_YEAR(DAT_YEAR) %>% group_by(YEAR,MAJOR_EVENT) %>% summarize(TAX_DIR_PER=sum(ifelse(IMPACT_TYPE=="Direct",WY_TOTAL_TAX,0))/max(sum(WY_TOTAL_TAX),0.001),EMP_DIR_PER=sum(ifelse(IMPACT_TYPE=="Direct",EMP,0))/max(sum(EMP),0.001),TAX_INDIR_PER=sum(ifelse(IMPACT_TYPE=="Indirect",WY_TOTAL_TAX,0))/max(sum(WY_TOTAL_TAX),0.001),EMP_INDIR_PER=sum(ifelse(IMPACT_TYPE=="Indirect",EMP,0))/max(sum(EMP),0.001),,OUTPUT=sum(OUTPUT),EMP=sum(EMP), WAGE_EMP=sum(WAGE_EMP),PROP_EMP=sum(PROP_EMP), EMP_COM=sum(EMP_COM),PROP_INC=sum(PROP_INC),TOPI=sum(TOPI),OPI=sum(OPI), SUBCOUNTY_TAX=sum(SUBCOUNTY_TAX), SPECIAL_TAX=sum(SPECIAL_TAX),COUNTY_TAX=sum(COUNTY_TAX), STATE_TAX=sum(STATE_TAX), TOTAL_TAX=sum(TOTAL_TAX), WY_TOTAL_TAX=sum(WY_TOTAL_TAX))
|
||||
return(RES)
|
||||
}
|
||||
|
||||
GET_EVENT_DATA <- function(YEARS=2025:2034){
|
||||
RES <- GET_EVENT_DATA_YEAR(YEARS[1])
|
||||
for(i in 2:length(YEARS)){
|
||||
RES <- rbind(RES,GET_EVENT_DATA_YEAR(YEARS[i]))
|
||||
}
|
||||
return(RES)
|
||||
}
|
||||
GET_DETAIL_ECON_DATA <- function(YEARS=2025:2034){
|
||||
RES <- GET_DATA_ECON_YEAR(YEARS[1])
|
||||
for(i in 2:length(YEARS)){
|
||||
RES <- rbind(RES,GET_DATA_ECON_YEAR(YEARS[i]))
|
||||
}
|
||||
return(RES)
|
||||
}
|
||||
GET_TOTAL_SUMMARY<- function(YEARS=2025:2034){
|
||||
RES <- GET_TOTAL_YEAR(YEARS[1])
|
||||
for(i in 2:length(YEARS)){
|
||||
RES <- rbind(RES,GET_TOTAL_YEAR(YEARS[i]))
|
||||
}
|
||||
RES <- RES %>% mutate(VALUE_ADDED=PROP_INC+EMP_COM+TOPI+OPI)
|
||||
return(RES)
|
||||
}
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user