Visuals.r

@@ -1,15 +1,15 @@
 library(tidyverse)
 #install.packages("paletteer")
-library(scales)
 library(paletteer)
-DATA_DIR <- 'Results/REMI_Output/'
+DATA_DIR <- 'Results/REMI_Outputs'
-OUTPUT_DIR <- './Results/Figures/'
+OUTPUT_DIR <- './Results/Figures'
 dir.create(OUTPUT_DIR,recursive=TRUE,showWarnings=FALSE)
 ######################Employments
-EMPLOY <-	read_csv(paste0(DATA_DIR,"Direct, Indirect, and Induced -Employment.csv" ),skip=5)  %>% pivot_longer(c(-Category,-Units),names_to="Year")  %>% mutate(value=parse_number(value),Year=parse_number(Year))
+EMPLOY <-	read_csv("Results/Direct, Indirect, and Induced -Employment.csv",skip=5)  %>% pivot_longer(c(-Category,-Units),names_to="Year")  %>% mutate(value=parse_number(value),Year=parse_number(Year))
 EMPLOY_NUM <-	EMPLOY %>% filter(Units=='Individuals (Jobs)')  %>% select(-Units,Employment=value)  %>% filter(Year>=2028)
 EMPLOY_NUM <- rbind(EMPLOY_NUM  %>% filter(!(Year %in% c(2028,2029)))	,EMPLOY_NUM  %>% filter(Year==2028) %>% mutate(Year=2029.74),EMPLOY_NUM  %>% filter(Year==2029) %>% mutate(Year=2029.75))
 
+
 EMPLOY_NUM$Category <- gsub(" Employment","",EMPLOY_NUM$Category)
 EMPLOY_NUM$Category <- factor(EMPLOY_NUM$Category,levels=rev(c("Direct","Indirect","Induced","Total")))
 
@@ -19,12 +19,13 @@ COOL_DOWN <- as.numeric(EMPLOY_NUM[5,3])
 df <- data.frame(Year=c(2032.5,2034.25,2046,2059.8),Employment=c(MAX_VAL,COOL_DOWN,791+20,743+20),text=c("Peak of 2,530","Stablized at 961","Midpoint of 791","Ends at 743"))
 JOB_PLOT <- ggplot(EMPLOY_NUM %>% filter(Category!="Total"),aes(x=Year,y=Employment))+geom_area(position = "stack",aes(fill=Category,group=Category))+theme_bw()+scale_x_continuous(breaks=c(2028,seq(2025,2060,by=5)))+scale_fill_manual(values=COLORS)+geom_line(data=EMPLOY_NUM %>% filter(Category=="Total"),size=1)+geom_text(data=df,aes(label = text), vjust = "inward", hjust = "inward")+theme(legend.position = "top")
 
-    png(paste0(OUTPUT_DIR,"Job_plot.png"  ) , units = "in", width = 10, height = 8, res = 600)
+    png("Job_plot.png", units = "in", width = 10, height = 8, res = 600)
 	JOB_PLOT
 dev.off()
-#JOB_PLOT 
+JOB_PLOT 
-###################Output graph
+###################
-OUTPUT <-	read_csv(paste0(DATA_DIR,"Direct, Indirect, and Induced -Output.csv" ),skip=5)  %>% pivot_longer(c(-Category,-Units),names_to="Year")  %>% mutate(value=parse_number(value),Year=parse_number(Year))
+OUTPUT <-	read_csv("Results/Direct, Indirect, and Induced -Output.csv",skip=5)  %>% pivot_longer(c(-Category,-Units),names_to="Year")  %>% mutate(value=parse_number(value),Year=parse_number(Year))
+OUTPUT 
 OUTPUT_NUM <-	OUTPUT %>% filter(Units=='Thousands of Fixed (2025) Dollars')  %>% select(-Units,Output=value)  %>% filter(Year>=2028) %>% mutate(Output=Output/1000)
 OUTPUT_NUM <- rbind(OUTPUT_NUM  %>% filter(!(Year %in% c(2028,2029)))	,OUTPUT_NUM  %>% filter(Year==2028) %>% mutate(Year=2029.74),OUTPUT_NUM  %>% filter(Year==2029) %>% mutate(Year=2029.75))
 
@@ -43,25 +44,9 @@ COOL_DOWN <- as.numeric(OUTPUT_NUM[5,3])
 df <- data.frame(Year=c(2032.5,2034.25,2059.8),Output=c(MAX_VAL,COOL_DOWN,337),text=c("Peak of $658 Million","Stablized at $273 Million ","Ends at $337 Million"))
 #OUTPUT_PLOT <- 
 OUTPUT_NUM  %>% filter(Year==2033)
-	OUTPUT_PLOT <- ggplot(OUTPUT_NUM %>% filter(Category!="Total"),aes(x=Year,y=Output))+geom_area(position = "stack",aes(fill=Category,group=Category))+theme_bw()+scale_x_continuous(breaks=c(2028,seq(2025,2060,by=5)))+scale_fill_manual(values=COLORS)+geom_line(data=OUTPUT_NUM %>% filter(Category=="Total"),size=1)
+	ggplot(OUTPUT_NUM %>% filter(Category!="Total"),aes(x=Year,y=Output))+geom_area(position = "stack",aes(fill=Category,group=Category))+theme_bw()+scale_x_continuous(breaks=c(2028,seq(2025,2060,by=5)))+scale_fill_manual(values=COLORS)+geom_line(data=OUTPUT_NUM %>% filter(Category=="Total"),size=1)
-#+geom_text(data=df,aes(label = text), vjust = "inward", hjust = "inward")+theme(legend.position = "top")
++geom_text(data=df,aes(label = text), vjust = "inward", hjust = "inward")+theme(legend.position = "top")
-    png(paste0(OUTPUT_DIR,"Output_plot.png"), units = "in", width = 10, height = 8, res = 600)
+    png("Output_plot.png", units = "in", width = 10, height = 8, res = 600)
 OUTPUT_PLOT
 dev.off()
-###################
-INDUSTRY_JOBS <- read_csv(paste0(DATA_DIR,'Employment- By Industry - Employment by Industry.csv'),skip=5)  %>% pivot_longer(c(-Industry,-Units),names_to="Year")  %>% mutate(Jobs=parse_number(value),Year=parse_number(Year)) %>% select(-value) %>% filter(Industry!='All Industries') %>% select(-Units) %>% filter(Year>=2029)
-
-
-INDUSTRY_JOBS <-  INDUSTRY_JOBS  %>% group_by(Year)  %>% mutate(Rank=rank(-Jobs)) %>% mutate(Rank=ifelse(Rank>10,11,Rank),Industry=ifelse(Rank==11,"Other",Industry)) %>% group_by(Year,Industry,Rank) %>% summarize(Jobs=sum(Jobs)) %>% ungroup(Industry,Rank)  %>% arrange(Year,Rank)  
-
-ORDER <- c(INDUSTRY_JOBS %>% filter(Industry!='Other')  %>% group_by(Industry) %>% summarize(MEAN=mean(Jobs)) %>% arrange(desc(MEAN)) %>% pull(Industry) %>% unique,"Other")
-INDUSTRY_JOBS$Industry <- factor(INDUSTRY_JOBS$Industry,levels=ORDER)
-
-JOB_TYPE_PLOT <- ggplot(INDUSTRY_JOBS ,aes(x=Year,y=Jobs,fill=Industry))+geom_bar(stat='identity')+ paletteer::scale_fill_paletteer_d("colorBlindness::Blue2DarkRed18Steps")
-png(paste0(OUTPUT_DIR,"Job_Dist_Plot.png"), units = "in", width = 20, height = 8, res = 600)
-JOB_TYPE_PLOT 
-dev.off()
-
-
-ggplot(INDUSTRY_JOBS %>% filter(Year %in% c(2032,2045)),aes(x=as.factor(Year),y=Jobs,fill=Industry))+geom_bar(stat='identity')+ paletteer::scale_fill_paletteer_d("colorBlindness::Blue2DarkRed18Steps")