From 8c9d56870229c17be1c50cc97255203cc14ae9f7 Mon Sep 17 00:00:00 2001 From: alex Date: Fri, 2 Oct 2026 19:09:42 -0600 Subject: [PATCH] Working on Employment table --- Visuals.r | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/Visuals.r b/Visuals.r index 8dc27d8..60cfd62 100644 --- a/Visuals.r +++ b/Visuals.r @@ -94,4 +94,42 @@ ggsave( filename = paste0("./Results/",HEADER,"Employment.png"), plot = IMPLAN_ OUTPUT_RES(TRUE) OUTPUT_RES(FALSE) +######### +if(exists("RES")){rm(RES)} + for(YEAR in 2028:2036){ +TEMP <- read_csv(paste0("Model_Outputs/IMPLAN/Wyoming/WY_",YEAR,"/occupation_impacts_table.csv"))[,c(2:3,5)] +colnames(TEMP) <- c("Occupation","Employment","Wages") +TEMP$Wages <- parse_number(TEMP$Wages) +TEMP <- TEMP %>% mutate(Year=YEAR) +if(!exists("RES")){RES <- TEMP}else{RES <- rbind(RES,TEMP)} +rm(TEMP) + } +if(exists("RES_US")){rm(RES_US)} + for(YEAR in 2028:2036){ +TEMP <- read_csv(paste0("Model_Outputs/IMPLAN/US/US_",YEAR,"/occupation_impacts_table.csv"))[,c(2:3,5)] +colnames(TEMP) <- c("Occupation","Employment","Wages") +TEMP$Wages <- parse_number(TEMP$Wages) +TEMP <- TEMP %>% mutate(Year=YEAR) +if(!exists("RES_US")){RES_US <- TEMP}else{RES_US <- rbind(RES_US,TEMP)} +rm(TEMP) + } +JOBS <- rbind(RES %>% filter(Year<2036) %>% group_by(Occupation) %>% summarize(Peak_Employment=max(Employment),Employment=mean(Employment),Wages=mean(Wages)) %>% ungroup %>% arrange(desc(Employment)) %>% mutate(Region='Wyoming',Period='Construction'), +RES %>% filter(Year==2036) %>% arrange(desc(Employment)) %>% mutate(Peak_Employment=Employment,Region='Wyoming',Period='Operations')%>% select(-Year), +RES_US %>% filter(Year<2036) %>% group_by(Occupation) %>% summarize(Peak_Employment=max(Employment),Employment=mean(Employment),Wages=mean(Wages)) %>% ungroup %>% arrange(desc(Employment)) %>% mutate(Region='United States',Period='Construction'), +RES_US %>% filter(Year==2036) %>% arrange(desc(Employment)) %>% mutate(Peak_Employment=Employment,Region='United States',Period='Operations')%>% select(-Year)) +### + +WY_JOBS <- JOBS %>% filter(Region=='Wyoming') %>% rename(Peak_Wy_Employment=Peak_Employment,Wy_Employment=Employment,Wy_Wages=Wages) %>% select(-Region) +US_JOBS <- JOBS %>% filter(Region!='Wyoming') %>% rename(Peak_US_Employment=Peak_Employment,US_Employment=Employment,US_Wages=Wages) %>% select(-Region) +JOINED <- WY_JOBS %>% left_join(US_JOBS) %>% mutate(US_Employment=US_Employment-Wy_Employment,Peak_US_Employment=Peak_Wy_Employment,US_Wages=US_Wages-Wy_Wages) +JOINED$US_Employment <- ifelse(JOINED$US_Employment<0,0,JOINED$US_Employment) +JOINED$US_Wages <- ifelse(JOINED$US_Employment==0,0,JOINED$US_Wages) +JOINED$Peak_US_Employment <- ifelse(JOINED$US_Employment==0,0,JOINED$Peak_US_Employment) +EMP_SUMMARY <- JOINED %>% mutate(Total_Employment=US_Employment+Wy_Employment,Total_Wages=US_Wages+Wy_Wages) %>% group_by(Period) %>% mutate(Rank=rank(-rank(Total_Employment))) %>% arrange(Period,Rank) + + + +TOTAL_JOBS <- JOBS %>% group_by(Occupation,Period) %>% summarize(Total_Employment=sum(Employment,na.rm=TRUE),Total_Wages=sum(Wages,na.rm=TRUE)) %>% ungroup %>% group_by(Period) %>% mutate(Rank=rank(-rank(Total_Employment))) %>% arrange(Period,Rank) + +JOBS %>% group_by(Occupation,Period) %>% summarize(Total_Employment=sum(Employment),Total_Wages=sum(Wages))