210 lines
16 KiB
R
210 lines
16 KiB
R
library(tidyverse)
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library(janitor)
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#install.packages("paletteer")
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library(scales)
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library(paletteer)
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DATA_DIR <- 'Results/REMI_Output/'
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OUTPUT_DIR <- './Results/Tables_and_Figures/'
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dir.create(OUTPUT_DIR,recursive=TRUE,showWarnings=FALSE)
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######################Employments
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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))
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EMPLOY_NUM <- EMPLOY %>% filter(Units=='Individuals (Jobs)') %>% select(-Units,Employment=value) %>% filter(Year>=2028)
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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))
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EMPLOY_NUM$Category <- gsub(" Employment","",EMPLOY_NUM$Category)
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EMPLOY_NUM$Category <- factor(EMPLOY_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total")))
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EMPLOY_NUM %>% filter(Employment=='Direct')
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EMPLOY_NUM
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EMPLOY_NUM %>% filter(Category=='Total',Employment!=0)%>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period) %>% summarize(Employment=mean(Employment))
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EMPLOY_NUM %>% filter(Category=='Total')%>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period) %>% summarize(Employment=mean(Employment))
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EMPLOY_NUM %>% filter(Category=='Total') %>% print(n=100)
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COLORS <- rev(paletteer_d("fishualize::Alosa_fallax",n=4,direction=1)[-2])
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MAX_VAL <- round(max(EMPLOY_NUM$Employment))
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MAX_VAL
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COOL_DOWN <- as.numeric(EMPLOY_NUM[5,3])
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#EMPLOY_NUM
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#EMPLOY_NUM %>% filter(Category=='Total') %>% arrange(Year)%>% print(n=100)
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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"))
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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))
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png(paste0(OUTPUT_DIR,"Job_plot.png" ) , units = "in", width = 11, height = 8, res = 600)
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JOB_PLOT
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dev.off()
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#JOB_PLOT
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###################Output graph
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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))
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OUTPUT_NUM <- OUTPUT %>% select(-Units,Output=value) %>% filter(Year>=2028) %>% mutate(Output=Output*1000)
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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))
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OUTPUT_NUM$Category <- gsub(" Output","",OUTPUT_NUM$Category)
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OUTPUT_NUM$Category <- factor(OUTPUT_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total")))
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OUTPUT_PLOT <- ggplot(OUTPUT_NUM %>% filter(Category!="Total"),aes(x=Year,y=Output))+geom_area(position = "stack",aes(fill=Category,group=Category))+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_fill_manual(values=COLORS)+geom_line(data=OUTPUT_NUM%>% filter(Category=="Total"),linewidth=1)+theme(legend.position = "top")+scale_y_continuous(labels = scales::comma,breaks=seq(0,600,by=25))+ylab("Economic Output (Million USD)")
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OUTPUT_PLOT
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png(paste0(OUTPUT_DIR,"Output_plot.png"), units = "in", width = 10, height = 8, res = 600)
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OUTPUT_PLOT
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dev.off()
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####GDP and Output
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GDP <- read_csv(paste0(DATA_DIR,"Gross Domestic Product - By Region - GDP by Region.csv" ),skip=5) %>% pivot_longer(c(-Region,-Units),names_to="Year",values_to="GDP") %>% mutate(Year=parse_number(Year),GDP=GDP*1000) %>% select(-Units)
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GDP <- GDP %>% filter(Region !='All Regions')
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GDP$Region <- gsub(" County","",GDP$Region)
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KEY_REGIONS <- GDP %>% group_by(Region) %>% summarize(GDP = median(GDP)) %>% arrange(desc(GDP)) %>% filter(GDP>0) %>% pull(Region)
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OTHER_GDP <- GDP %>% filter(!(Region %in% KEY_REGIONS)) %>% group_by(Year) %>% summarize(Region="Other Counties",GDP=sum(GDP)) %>% ungroup
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GDP <- rbind(GDP %>% filter(Region %in% KEY_REGIONS),OTHER_GDP)
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GDP$Region <- factor(GDP$Region,levels=rev(c("Other Counties",KEY_REGIONS)))
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GDP <- GDP %>% filter(Year>=2029)
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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"))
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png(paste0(OUTPUT_DIR,"GDP_Other_Counties.png"), units = "in", width = 10, height = 8, res = 600)
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GDP_SUB_REGION_PLOT
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dev.off()
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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
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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")
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png(paste0(OUTPUT_DIR,"GDP_Region_Plot.png"), units = "in", width = 10, height = 8, res = 600)
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GDP_PLOT
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dev.off()
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###################
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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)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Data processing, hosting, and related services; Other information services',"Data processing",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Professional, scientific, and technical services',"Technical services",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Administrative and support services',"Administrative services",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='State and Local Government',"Government",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Food services and drinking places',"Restaurants",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Other transportation equipment manufacturing',"Equipment manufacturing",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Repair and maintenance',"Maintenance",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Utilities',"Energy Production",INDUSTRY_JOBS$Industry)
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INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Ambulatory health care services',"Hospitals",INDUSTRY_JOBS$Industry)
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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
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#INDUSTRY_JOBS %>% pull(Industry) %>% unique
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ORDER <- c(INDUSTRY_JOBS %>% filter(Industry!='Other') %>% group_by(Industry) %>% summarize(MEAN=mean(Jobs)) %>% arrange(desc(MEAN)) %>% pull(Industry) %>% unique,"Other")
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INDUSTRY_JOBS$Industry <- factor(INDUSTRY_JOBS$Industry,levels=ORDER)
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INDUSTRY_JOBS
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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))
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png(paste0(OUTPUT_DIR,"Job_Dist_Plot.png"), units = "in", width = 22, height = 16, res = 600)
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JOB_TYPE_PLOT
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dev.off()
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JOB_FACET_DATA <- INDUSTRY_JOBS %>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period,Industry) %>% summarize(Jobs=mean(Jobs)) %>% ungroup
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JOB_FACET <- ggplot(JOB_FACET_DATA,aes(x=Period,y=Jobs,fill=factor(Period)))+geom_bar(stat='identity',width=0.99)+facet_wrap(.~Industry,nrow=5)+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))
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png(paste0(OUTPUT_DIR,"Job_Facet_Plot.png"), units = "in", width = 11, height = 11, res = 600)
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JOB_FACET
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dev.off()
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##################GDP
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read_csv(paste0(DATA_DIR,'Gross Domestic Product - By Component - GDP Components.csv'),skip=5)
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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)
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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'))
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GDP_TOTAL <- GDP_TOTAL %>% filter(Category=='Gross Domestic Product (GDP)') %>% select(-Category) %>% rename(GDP=value)
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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))
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png(paste0(OUTPUT_DIR,"GDP_Time_Plot.png"), units = "in", width = 11, height = 8, res = 600)
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GDP_PLOT
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dev.off()
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################Personal Income
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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)
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TOTAL_PERSONAL_INCOME <- PERSONAL_INCOME %>% filter(Region=='All Regions') %>% select(-Region)
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TOTAL_PERSONAL_INCOME
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TAXES <- read_csv("Results/Tax_PI/Revenues.csv",skip=5) %>% clean_names() %>% filter(!is.na(revenue))
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colnames(TAXES) <- gsub("fy","",colnames(TAXES))
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TAXES <- TAXES %>% pivot_longer(c(-revenue,-units),names_to='year',values_to='Taxes') %>% mutate(Taxes=Taxes*1000) %>% select(-units)
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TAXES
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TAXES$revenue <- gsub('State Sales & Use Taxes - ','Sales Tax: ', TAXES$revenue)
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TAXES <- TAXES %>% filter(year>2028,!is.na(revenue))
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TAXES <- TAXES %>% filter(Taxes>0)
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TAXES$revenue[!grepl("Sales",TAXES$revenue)] <- 'Other Taxes'
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TAXES <- TAXES %>% rename(Revenue='Taxes','Tax'=revenue,'Year'=year) %>% select(Year,Tax,Revenue)
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TAXES <- TAXES %>% group_by(Year,Tax) %>% summarize(Revenue=sum(Revenue))
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TOTAL_TAXES <- TAXES %>% group_by(Year) %>% summarize('Million (USD)'=sum(Revenue)/10^6,Metric='Wyoming Taxes') %>% mutate(Year=as.numeric(Year))
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TOTAL_TAXES
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DOLLAR_VALUES <- rbind(OUTPUT_NUM %>% filter(Category=='Total') %>% select(-Category) %>% mutate(Metric='Economic Output') %>% rename('Million (USD)'=Output),
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GDP_TOTAL %>% mutate(Metric='GDP') %>% rename('Million (USD)'=GDP),
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TOTAL_TAXES,
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TOTAL_PERSONAL_INCOME %>% mutate(Metric='Wages Paid') %>% rename('Million (USD)'=Income))
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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") )
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DOLLAR_VALUES
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DIRECT_TAX <- c(10.408,22.355,24.647,58.221,55.835,53.449,51.062,48.676,46.290,43.904,41.518,39.132,37.318,35.766,34.214,32.662,31.110,29.557,28.005,26.453,24.901,23.349,21.797,20.244,18.692,17.140,15.588,14.036,12.484,10.931,9.379)
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DIRECT_TAX <- cbind(2030:2060,DIRECT_TAX ) %>% as_tibble
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colnames(DIRECT_TAX) <- c("Year","Tax")
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DOLLAR_VALUES
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DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] <- DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] + c(0,DIRECT_TAX$Tax)
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TAX_PLOT <- ggplot(DOLLAR_VALUES %>% filter(Metric %in% c('Wyoming Taxes')), aes(x=Year,y=`Million (USD)`,fill=Metric))+geom_area()+theme_bw()+scale_x_continuous(breaks=c(2029,seq(2025,2060,by=2)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=5))+theme(legend.position = "top",text=element_text(size=16))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus")[4] )
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png(paste0(OUTPUT_DIR,"Total_Tax_Plot.png"), units = "in", width = 1.5*11, height = 1.5*10, res = 600)
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TAX_PLOT
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dev.off()
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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") )
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FACET_INDICATORS_PLOT
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FACET_INDICATORS_PLOT
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png(paste0(OUTPUT_DIR,"Facet_Indicator_Plot.png"), units = "in", width = 1.5*11, height = 1.5*10, res = 600)
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FACET_INDICATORS_PLOT
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dev.off()
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#############
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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)))
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Period_Indicator_Summary[,c(3:4)] <- round(Period_Indicator_Summary[,c(3:4)],0)
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Period_Indicator_Summary
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write_csv(Period_Indicator_Summary,paste0(OUTPUT_DIR,"Economic_Summary_Indicator.csv" ),col_names=TRUE)
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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
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NPV_3 <- GDP_SUMMARY %>% pull(NPV_3) %>% sum
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NPV_5 <- GDP_SUMMARY %>% pull(NPV_5) %>% sum
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NPV_7 <- GDP_SUMMARY %>% pull(NPV_7) %>% sum
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NPV_SUMMARY <- round(t(c(NPV_3,NPV_5,NPV_7)),0) %>% as_tibble
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NPV_SUMMARY
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colnames(NPV_SUMMARY) <- c('3%','5%','7%')
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write_csv(NPV_SUMMARY,paste0(OUTPUT_DIR,"GDP_Net_Present_Value.csv" ))
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#######################
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TAX <- DOLLAR_VALUES %>% filter(Metric=='Wyoming Taxes') %>% select(Year,Tax=`Million (USD)`)
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TAX %>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period) %>% summarize(Mean_Tax=mean(Tax),Max_Tax=max(Tax))
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TAX_SUMMARY <- TAX %>% group_by(Year) %>% summarize(Tax=sum(Tax),NPV_3=(Tax/((1+0.03)^(Year-2029))),NPV_5=(Tax/((1+0.05)^(Year-2029))),NPV_7=(Tax/((1+0.07)^(Year-2029)))) %>% ungroup
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NPV_3 <- TAX_SUMMARY %>% pull(NPV_3) %>% sum
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NPV_5 <- TAX_SUMMARY %>% pull(NPV_5) %>% sum
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NPV_7 <- TAX_SUMMARY %>% pull(NPV_7) %>% sum
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NPV_TAX_SUMMARY <- round(t(c(NPV_3,NPV_5,NPV_7)),0) %>% as_tibble
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NPV_TAX_SUMMARY
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colnames(NPV_TAX_SUMMARY) <- c('3%','5%','7%')
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write_csv(NPV_TAX_SUMMARY,paste0(OUTPUT_DIR,"Tax_Net_Present_Value.csv" ))
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