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Chnaged all dates to start in 2029

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Alex 2026-05-01 15:07:32 -06:00
parent 32ae8c79d3
commit b32be3c23c
1 changed files with 9 additions and 24 deletions
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Visuals.r
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@ -10,13 +10,13 @@ dir.create(OUTPUT_DIR,recursive=TRUE,showWarnings=FALSE)
######################Employments ######################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 <- 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 <- 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[EMPLOY_NUM$Year==2028,]$Year <-2029
#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$Category <- gsub(" Employment","",EMPLOY_NUM$Category)
EMPLOY_NUM$Category <- factor(EMPLOY_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total"))) EMPLOY_NUM$Category <- factor(EMPLOY_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total")))
EMPLOY_NUM %>% filter(Employment=='Direct') #EMPLOY_NUM %>% filter(Category=='Total',Employment!=0)%>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period) %>% summarize(Employment=mean(Employment))
EMPLOY_NUM
EMPLOY_NUM %>% filter(Category=='Total',Employment!=0)%>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period) %>% summarize(Employment=mean(Employment))
EMPLOY_NUM %>% filter(Category=='Total')%>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period) %>% summarize(Employment=mean(Employment)) EMPLOY_NUM %>% filter(Category=='Total')%>% mutate(Period=ifelse(Year>2033,"Operating","Construction")) %>% group_by(Period) %>% summarize(Employment=mean(Employment))
EMPLOY_NUM %>% filter(Category=='Total') %>% print(n=100) EMPLOY_NUM %>% filter(Category=='Total') %>% print(n=100)
@ -25,20 +25,21 @@ COLORS <- rev(paletteer_d("fishualize::Alosa_fallax",n=4,direction=1)[-2])
MAX_VAL <- round(max(EMPLOY_NUM$Employment)) MAX_VAL <- round(max(EMPLOY_NUM$Employment))
MAX_VAL MAX_VAL
COOL_DOWN <- as.numeric(EMPLOY_NUM[5,3]) COOL_DOWN <- as.numeric(EMPLOY_NUM[5,3])
COOL_DOWN
#EMPLOY_NUM #EMPLOY_NUM
#EMPLOY_NUM %>% filter(Category=='Total') %>% arrange(Year)%>% print(n=100) #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+125,COOL_DOWN,687+20,596+20),text=c("Peak of 2,521","Stabalized at 878" ,"687 in 2040","Ends at 596")) df <- data.frame(Year=c(2032.5,2034.25,2040.25,2059.75),Employment=c(MAX_VAL+125,900,687+20,596+20),text=c("Peak of 2,521","Stabilized 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(2035,2060,by=5)))+scale_fill_manual(values=COLORS)+geom_line(data=EMPLOY_NUM %>% filter(Category=="Total"),linewidth=1)+geom_text(data=df,aes(label = text),size=6, vjust = "inward", hjust = "inward")+theme(text = element_text(size = 20),legend.position = "top")+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=250)) 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(2035,2060,by=5)))+scale_fill_manual(values=COLORS)+geom_line(data=EMPLOY_NUM %>% filter(Category=="Total"),linewidth=1)+geom_text(data=df,aes(label = text),size=6, vjust = "inward", hjust = "inward")+theme(text = element_text(size = 20),legend.position = "top")+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=250))
JOB_PLOT
png(paste0(OUTPUT_DIR,"Job_plot.png" ) , units = "in", width = 11, height = 8, res = 600) png(paste0(OUTPUT_DIR,"Job_plot.png" ) , units = "in", width = 11, height = 8, res = 600)
JOB_PLOT JOB_PLOT
dev.off() dev.off()
#JOB_PLOT #JOB_PLOT
###################Output graph ###################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 <- 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 <- 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 <- 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$Category <- gsub(" Output","",OUTPUT_NUM$Category)
OUTPUT_NUM$Category <- factor(OUTPUT_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total"))) OUTPUT_NUM$Category <- factor(OUTPUT_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total")))
@ -92,14 +93,11 @@ INDUSTRY_JOBS$Industry <- ifelse(INDUSTRY_JOBS$Industry=='Ambulatory health care
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 <- 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 #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") 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) INDUSTRY_JOBS$Industry <- factor(INDUSTRY_JOBS$Industry,levels=ORDER)
INDUSTRY_JOBS
JOB_TYPE_PLOT <- ggplot(INDUSTRY_JOBS ,aes(x=Year,y=Jobs,fill=Industry))+geom_bar(stat='identity')+ paletteer::scale_fill_paletteer_d("colorBlindness::Blue2DarkRed18Steps",name="")+theme_bw()+theme(text = element_text(size = 20),legend.position = "top")+guides(fill=guide_legend(nrow=4,byrow=TRUE)) +scale_x_continuous(breaks=c(2029,seq(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=500)) JOB_TYPE_PLOT <- ggplot(INDUSTRY_JOBS ,aes(x=Year,y=Jobs,fill=Industry))+geom_bar(stat='identity')+ paletteer::scale_fill_paletteer_d("colorBlindness::Blue2DarkRed18Steps",name="")+theme_bw()+theme(text = element_text(size = 20),legend.position = "top")+guides(fill=guide_legend(nrow=4,byrow=TRUE)) +scale_x_continuous(breaks=c(2029,seq(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=500))
png(paste0(OUTPUT_DIR,"Job_Dist_Plot.png"), units = "in", width = 11, height = 8, res = 600) png(paste0(OUTPUT_DIR,"Job_Dist_Plot.png"), units = "in", width = 11, height = 8, res = 600)
@ -127,12 +125,10 @@ dev.off()
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) 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 <- 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)) TAXES <- read_csv("Results/Tax_PI/Revenues.csv",skip=5) %>% clean_names() %>% filter(!is.na(revenue))
colnames(TAXES) <- gsub("fy","",colnames(TAXES)) 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 %>% 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$revenue <- gsub('State Sales & Use Taxes - ','Sales Tax: ', TAXES$revenue)
TAXES <- TAXES %>% filter(year>2028,!is.na(revenue)) TAXES <- TAXES %>% filter(year>2028,!is.na(revenue))
TAXES <- TAXES %>% filter(Taxes>0) TAXES <- TAXES %>% filter(Taxes>0)
@ -140,12 +136,6 @@ TAXES$revenue[!grepl("Sales",TAXES$revenue)] <- 'Other Taxes'
TAXES <- TAXES %>% rename(Revenue='Taxes','Tax'=revenue,'Year'=year) %>% select(Year,Tax,Revenue) TAXES <- TAXES %>% rename(Revenue='Taxes','Tax'=revenue,'Year'=year) %>% select(Year,Tax,Revenue)
TAXES <- TAXES %>% group_by(Year,Tax) %>% summarize(Revenue=sum(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)) TOTAL_TAXES <- TAXES %>% group_by(Year) %>% summarize('Million (USD)'=sum(Revenue)/10^6,Metric='Wyoming Taxes') %>% mutate(Year=as.numeric(Year))
TOTAL_TAXES
DOLLAR_VALUES <- rbind(OUTPUT_NUM %>% filter(Category=='Total') %>% select(-Category) %>% mutate(Metric='Economic Output') %>% rename('Million (USD)'=Output), DOLLAR_VALUES <- rbind(OUTPUT_NUM %>% filter(Category=='Total') %>% select(-Category) %>% mutate(Metric='Economic Output') %>% rename('Million (USD)'=Output),
@ -153,24 +143,19 @@ GDP_TOTAL %>% mutate(Metric='GDP') %>% rename('Million (USD)'=GDP),
TOTAL_TAXES, TOTAL_TAXES,
TOTAL_PERSONAL_INCOME %>% mutate(Metric='Wages Paid') %>% rename('Million (USD)'=Income)) 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(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+theme(legend.position = "top",text=element_text(size=20))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") ) 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(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+theme(legend.position = "top",text=element_text(size=20))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )
DOLLAR_VALUES
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) 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)
DIRECT_TAX <- cbind(2030:2060,DIRECT_TAX ) %>% as_tibble DIRECT_TAX <- cbind(2030:2060,DIRECT_TAX ) %>% as_tibble
colnames(DIRECT_TAX) <- c("Year","Tax") colnames(DIRECT_TAX) <- c("Year","Tax")
DOLLAR_VALUES
DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] <- DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] + c(0,DIRECT_TAX$Tax) DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] <- DOLLAR_VALUES[DOLLAR_VALUES$Metric=='Wyoming Taxes','Million (USD)'] + c(0,DIRECT_TAX$Tax)
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(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=5))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus")[4],name="" )+theme(legend.position = "top",text=element_text(size=20))+ guides(fill = "none") 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(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=5))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus")[4],name="" )+theme(legend.position = "top",text=element_text(size=20))+ guides(fill = "none")
TAX_PLOT
png(paste0(OUTPUT_DIR,"Total_Tax_Plot.png"), units = "in", width = 11, height = 8, res = 600) png(paste0(OUTPUT_DIR,"Total_Tax_Plot.png"), units = "in", width = 11, height = 8, res = 600)
TAX_PLOT TAX_PLOT
dev.off() dev.off()
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(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )+theme(legend.position = "top",text=element_text(size=20)) 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(2035,2060,by=5)))+scale_y_continuous(labels = scales::comma,breaks=seq(0,3000,by=50))+scale_fill_manual(values=paletteer::paletteer_d("calecopal::lupinus") )+theme(legend.position = "top",text=element_text(size=20))
FACET_INDICATORS_PLOT