17 lines
1.6 KiB
R
17 lines
1.6 KiB
R
library(tidyverse)
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DATA_PATH <- "./Raw_Output/"
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source("Scripts/Data_Proc_Script.r")
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DAT <- GET_ALL_DATA(DATA_PATH)
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COUNTY_DATA <-DAT %>% group_by(Year,Major_Event,County_Name) %>% summarize(Employment =sum(Employment ),Labor_Income =sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output),Subcounty=sum(Subcounty),Special=sum(Special),County=sum(County),State=sum(State),STATE_TOTAL=sum(STATE_TOTAL),Federal=sum(Federal))
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STATE_EVENT_DATA <- DAT %>% group_by(Year,Major_Event) %>% summarize(Employment =sum(Employment ),Labor_Income =sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output),Subcounty=sum(Subcounty),Special=sum(Special),County=sum(County),State=sum(State),STATE_TOTAL=sum(STATE_TOTAL),Federal=sum(Federal))
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STATE_TYPE_DATA <- DAT %>% group_by(Year,Impact) %>% summarize(Employment =sum(Employment ),Labor_Income =sum(Labor_Income),Value_Added=sum(Value_Added),Output=sum(Output),Subcounty=sum(Subcounty),Special=sum(Special),County=sum(County),State=sum(State),STATE_TOTAL=sum(STATE_TOTAL),Federal=sum(Federal))
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ggplot(aes(x=Year,y=Employment,group-Impact,fill=Impact),data=STATE_TYPE_DATA)+geom_bar(stat = "identity")
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ggplot(aes(x=Year,y=STATE_TOTAL/10^6,group=Impact,fill=Impact),data=STATE_TYPE_DATA)+geom_bar(stat = "identity")+ylab("Wyoming Taxes (Million USD)")
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ggplot(aes(x=Year,y=Employment,group-Major_Event,fill=Major_Event),data=STATE_EVENT_DATA)+geom_bar(stat = "identity") + scale_fill_discrete(name = "Economic Event")
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ggplot(aes(x=Year,y=STATE_TOTAL/10^6,group=Major_Event,fill=Major_Event),data=STATE_EVENT_DATA)+geom_bar(stat = "identity") + scale_fill_discrete(name = "Economic Event")+ylab("Wyoming Taxes (Million USD)") |