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Working on Employment table

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alex 2026-10-02 19:09:42 -06:00
parent d489b2c0e3
commit 8c9d568702
1 changed files with 38 additions and 0 deletions
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@ -94,4 +94,42 @@ ggsave( filename = paste0("./Results/",HEADER,"Employment.png"), plot = IMPLAN_
OUTPUT_RES(TRUE) OUTPUT_RES(TRUE)
OUTPUT_RES(FALSE) 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))