51 lines
3.4 KiB
R
51 lines
3.4 KiB
R
library(tidyverse)
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library(scales)
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GET_DATA <- function(){
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GET_FILE_US <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/US/US_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="US") }
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GET_FILE_WY <- function(YEAR){read_csv(paste0("Model_Outputs/IMPLAN/Wyoming/WY_",YEAR,"/economic_indicators_by_impact.csv")) %>% mutate(year=YEAR) %>% filter(!is.na(Impact))%>% mutate(Region="WY") }
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DATA <-rbind(lapply(2028:2035,GET_FILE_US) %>% bind_rows()%>% rename(Income=`Labor Income`) %>% mutate(Income=parse_number(Income),`Value Added`=parse_number(`Value Added`),Output=parse_number(Output)) ,
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lapply(2028:2035,GET_FILE_WY) %>% bind_rows()%>% rename(Income=`Labor Income`) %>% mutate(Income=parse_number(Income),`Value Added`=parse_number(`Value Added`),Output=parse_number(Output)))
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DATA$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",DATA$Impact)))
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DATA <- DATA %>% pivot_longer(-c(Impact,year,Region)) %>% rename(Type=Impact,Impact=name)
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DATA$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",DATA$Impact)))
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return(DATA)
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}
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DATA <- GET_DATA()
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TEMP <- DATA %>% filter(year==2035)
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TEMP <- DATA %>% filter(year==2035)
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RES <- TEMP %>% mutate(year=2036)
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for(i in 2037:2060){
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RES <- RES %>% rbind(TEMP %>% mutate(year=i))
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}
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DATA <- DATA %>% rbind(RES)
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DATA$Type <- factor(DATA$Type,levels=c("Direct","Indirect","Induced"))
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DATA
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EMP_WY <- DATA %>% filter(Impact=="Employment",Region=="WY")
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DOL_WY <- DATA %>% filter(Impact!="Employment",Region=="WY")
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EMP <- DATA %>% filter(Impact=="Employment")
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ALL_EMP <- EMP %>% group_by(Region,year) %>% summarize(value=sum(value)) %>% ungroup()
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ALL_EMP <- ALL_EMP %>% group_by(year) %>% mutate(value=ifelse(Region=='US',value-min(value),value))
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DOLLAR <-DATA %>% filter(Impact!="Employment")
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ALL_DOLLAR <-DOLLAR %>% group_by(Region,year,Impact) %>% summarize(value=sum(value))
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ALL_DOLLAR <- ALL_DOLLAR %>% group_by(year,Impact) %>% mutate(value=ifelse(Region=='US',value-min(value),value))
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ggplot(EMP, aes(x = year, y = value, fill = Type)) + geom_area(stat = "identity", position = "stack") + theme_bw()+scale_x_continuous(breaks=2026:2060)+scale_y_continuous(breaks=seq(0,10000,by=250))+ylab("Wyoming Employment Additions\n(Full-time Equivalent)")+facet_wrap(~Region)
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WY_US_EMP_PLOT <- ggplot(ALL_EMP, aes(x = year, y = value, fill = Region)) + geom_area(stat = "identity", position = "stack") + theme_bw()+scale_x_continuous(breaks=seq(2026,2060,by=2))+scale_y_continuous(breaks=seq(0,100000,by=1000),labels = label_comma())+ylab("Employment Additions (FTE)")+scale_fill_manual(values=c("#0A3161","#FFC425"))+theme(legend.position = "top",text = element_text(size = 20))
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ggsave(filename = "Employment_Plot.png", plot = WY_US_EMP_PLOT, width = 2*6.5, height = 2*4.0, units = "in", dpi = 600 )
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ALL_DOLLAR$Impact <- factor(ALL_DOLLAR$Impact,levels=c("Output","Value Added","Income"))
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WY_US_VALUE_ADD <- ggplot(ALL_DOLLAR, aes(x = year, y = value/10^9, fill = Region)) + geom_area(stat = "identity", position = "stack") + theme_bw()+scale_x_continuous(breaks=seq(2026,2060,by=2))+scale_y_continuous(breaks=seq(0,10000,by=0.5),labels = label_comma())+ylab("Billion Dollars (2026 USD)")+scale_fill_manual(values=c("#0A3161","#FFC425"))+theme(legend.position = "top",text = element_text(size = 20))+facet_wrap(~Impact,ncol=1)
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ggsave(filename = "Impact_Plot.png", plot = WY_US_VALUE_ADD, width = 2*6.5, height = 6*4.0, units = "in", dpi = 600 )
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