24 lines
1.4 KiB
R
24 lines
1.4 KiB
R
library(tidyverse)
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library(scales)
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library(janitor)
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library(paletteer)
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source("Scripts/Return_Data_Center_Model_Inputs.r")
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DATA_CENTER <- DATA_CENTER_INPUTS()
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COMP_PURCHASE <- read_csv("Model_Outputs/IMPLAN/Data_Center/Wyoming/Computer_Purchase_Mill_Dollars/economic_indicators_by_impact.csv") %>% rename(Income=`Labor Income`) %>% mutate(Income=parse_number(Income),`Value Added`=parse_number(`Value Added`),Output=parse_number(Output)) %>% mutate(Region='Wyoming',Event='COMPUTERS')
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COMP_PURCHASE$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",COMP_PURCHASE$Impact)))
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COMP_PURCHASE <- COMP_PURCHASE %>% pivot_longer(-c(Impact,Region,Event)) %>% rename(Type=Impact,Impact=name)
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COMP_PURCHASE$Impact <- gsub("3 - ","",gsub("2 - ","",gsub("1 - ","",COMP_PURCHASE$Impact)))
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COMP_PURCHASE <- COMP_PURCHASE %>% mutate(Source='IMPLAN') %>% select(Type,Region,Impact,Event,Source,value)
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COMP_PURCHASE <- COMP_PURCHASE %>% filter(!is.na(Type))
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COMP_PURCHASE$value <- COMP_PURCHASE$value/10^6 #shift to impact per dollar from impact per million dollar
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COMP_PURCHASE <- COMP_PURCHASE %>% rename('marginal'=value)
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COMP_PURCHASE <- COMP_PURCHASE %>% left_join(DATA_CENTER) %>% mutate(value=marginal*total)
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COMP_PURCHASE <- COMP_PURCHASE %>% group_by(Type,Year,Region,Impact,Source) %>% summarize(value=sum(value)) %>% ungroup
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COMP_PURCHASE
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EMP <-COMP_PURCHASE %>% filter(Impact=='Employment')
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EMP
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ggplot(EMP ,aes(x=Year,y=value,fill=Type)) +geom_area()
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