Working on TerraPower numbers
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@ -9,10 +9,14 @@ for(YEAR in 2026:(2026+NUM_YEARS)){
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PERM$Date <- DATES[1:nrow(PERM)]
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PERM$Date <- DATES[1:nrow(PERM)]
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PERM$Year <- year(PERM$Date)
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PERM$Year <- year(PERM$Date)
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PERM$Add_Emp <- c(0,diff(PERM$In_Migration))
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PERM$Add_Emp <- c(0,diff(PERM$In_Migration))
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PERM <- PERM %>% group_by(Year) %>% summarize(Perm_Emp_Migration=sum(Add_Emp),Total_Wages=sum(Average_Wages_Present_USD*In_Migration),Current_Perm_Workers=round(mean(In_Migration) ))
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PERM %>% group_by(Year) %>% mutate(Total_Wages=Average_Wages_Present_USD*In_Migration) %>% group_by(Year) %>% summarize(Total_Wages=sum(Total_Wages),Total_Wages=sum(ifelse(Year==2031,Total_Wages*3,Total_Wages)))
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%>% summarize(Perm_Emp_Migration=sum(Add_Emp),Total_Wages=sum(Average_Wages_Present_USD*In_Migration),Current_Perm_Workers=round(mean(In_Migration) ))
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PERM[7,3]<-PERM[7,3]*3 #4 months (1/3) year only seen in sample
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PERM[7,3]<-PERM[7,3]*3 #4 months (1/3) year only seen in sample
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PERM[,"Total_Migration"] <- round(PERM[,2]+PERM[,2]*0.8*3.05) #TerraPower assumes 80% have families and the average family is 3.2 people, but in wyoming the average is stated as 3.05
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PERM[,"Total_Migration"] <- round(PERM[,2]+PERM[,2]*0.8*3.05) #TerraPower assumes 80% have families and the average family is 3.2 people, but in wyoming the average is stated as 3.05
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PERM
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TEST <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Operations_Workforce.csv")
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TEST[59,]*12
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TEMP <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Construction_Workforce.csv")
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TEMP <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Construction_Workforce.csv")
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TEMP[,1] <- TEMP[,1]*0.41 #TerraPower assumes 41% migrate into Lincoln
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TEMP[,1] <- TEMP[,1]*0.41 #TerraPower assumes 41% migrate into Lincoln
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