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Alex 2025-05-02 20:19:42 -06:00
parent 231ea31899
commit 8c2e16f3e7
2 changed files with 18 additions and 18 deletions
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34
Data_Analysis.r
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@ -1,17 +1,17 @@
library(tidyverse) library(tidyverse)
DATA_PATH <- "./Raw_Output/" DATA_PATH <- "./Raw_Output/"
source("Scripts/Data_Proc_Script.r") source("Scripts/Data_Proc_Script.r")
DAT <- GET_ALL_DATA(DATA_PATH) DAT <- GET_ALL_DATA(DATA_PATH)
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)) 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))
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)) 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))
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)) 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))
ggplot(aes(x=Year,y=Employment,group-Impact,fill=Impact),data=STATE_TYPE_DATA)+geom_bar(stat = "identity") ggplot(aes(x=Year,y=Employment,group-Impact,fill=Impact),data=STATE_TYPE_DATA)+geom_bar(stat = "identity")
ggplot(aes(x=Year,y=STATE_TOTAL/10^6,group-Impact,fill=Impact),data=STATE_TYPE_DATA)+geom_bar(stat = "identity") 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)")
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") 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")
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)")

2
Scripts/Data_Proc_Script.r
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@ -19,7 +19,7 @@ GET_YEAR_DATA <- function(C_YEAR,DATA_PATH){
DAT[grep('Skid',DAT$Event),"Group"] <- "Skid" DAT[grep('Skid',DAT$Event),"Group"] <- "Skid"
DAT[grep('Purchase',DAT$Event),"Group"] <- "Beet Purchase" DAT[grep('Purchase',DAT$Event),"Group"] <- "Beet Purchase"
DAT[grep('Truck',DAT$Event),"Group"] <- "Transportation" DAT[grep('Truck',DAT$Event),"Group"] <- "Transportation"
DAT[DAT$Event=="Gas Production (Campbell)","Group"] <- "Gas Produciton" DAT[DAT$Event=="Gas Production (Campbell)","Group"] <- "Gas Production"
DAT[is.na(DAT$Group),"Group"] <- "Processing Facilities" DAT[is.na(DAT$Group),"Group"] <- "Processing Facilities"
#Remove the direct transportation effects induced by by beet purchases, by adding in a negative impact of rail and truck equal to the direct effect found in the first run. #Remove the direct transportation effects induced by by beet purchases, by adding in a negative impact of rail and truck equal to the direct effect found in the first run.
DAT[grep('Adjustment',DAT$Event),"Group"] <- "Beet Purchase" DAT[grep('Adjustment',DAT$Event),"Group"] <- "Beet Purchase"