31 lines
1.9 KiB
R
31 lines
1.9 KiB
R
library(tidyverse)
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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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ggplot(EMP_WY, 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)")
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?scale_y_continuous
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ggplot(DOL_WY, aes(x = year, y = value/10^9, fill = Type)) + geom_bar(stat = "identity", position = "stack") + theme_minimal()+facet_wrap(~Impact)
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