FP Maps

Maps

#figure maps

#load packages

library(dplyr)

library(readr)

library(ggplot2)

library(RColorBrewer)

library(ggmap)

library(maptools)

library(gtools)

#read map data

mapdata <- read_csv("~/Desktop/map.csv")

#make map 1

map1 <- ggplot() + theme_nothing(legend=TRUE) + 
  geom_polygon(data=mapdata, aes(x=long,y=lat,group=group),fill='white',color='black')


#read csv

ipums <- read_csv('~/Desktop/usa_00028.csv')




#create integer map data

newmap <- mapdata %>% mutate(STATEI=as.integer(STATEFIP))


#women only and over 22

women_only <- ipums%>% filter(SEX==2 & AGE>=22)

#set SLWT for 1950

#calculate total num of women
numwomen_region <- women_only %>% group_by(YEAR,BPL) %>% summarise(Number=sum(PERWT))

#filter to only women with college degrees
women_educ <- women_only %>% filter (EDUCD>=101 & EDUCD<=116)

#calculate number of women w/ college degrees
numwomen_region_educ <- women_educ %>% group_by(YEAR,BPL) %>% summarise(Number=sum(PERWT))

#join the two
joined <- left_join(numwomen_region,numwomen_region_educ,by=c('YEAR'='YEAR',"BPL"="BPL"))

#calculate percent
percentages <- joined %>% mutate(pct=Number.y/Number.x*100)

#rename BPL statefip for map
percentages <- percentages %>% rename(STATEFIP=BPL)

#create integer

newmap <- mapdata %>% mutate(STATEI=as.integer(STATEFIP))





#create categories for percents

pct_cats <- percentages %>% mutate(Percent=factor(ifelse(pct<0,1,
                                                     ifelse(pct<0.5,2,
                                                            ifelse(pct<2,3,
                                                                   ifelse(pct<5,4,
                                                                          ifelse(pct<10,5,
                                                                                 ifelse(pct<15,6,
                                                                                        ifelse(pct<20,6,
                                                                                               ifelse(pct<25,7,
                                                                                                      ifelse(pct<30,8,9)))))))))))
levels(pct_cats$Percent) <- c('Under 0.50%','0.50%-1.99%','2.00%-4.99%','5.00%-9.99%','10%-14.99%','15.00%-19.99%','20.00%-24.99%','25.00%-29.99%','30% or Greater')


#make percent map join
pct_map <- left_join(pct_cats,newmap,by=c('STATEFIP'='STATEI'))

pct_map <- pct_map %>% arrange(order)




#make map 3



#make map 4 unique map and print

unique(pct_map$YEAR)

for (year in unique(pct_map$YEAR)) {
  
  map4 <- map1 + scale_fill_brewer(palette='blues',drop=F) +
    theme_bw(base_size = 24) +
    geom_polygon(data=filter(pct_map,YEAR==year),aes(x=long,y=lat,group=group,fill=Percent),color='black') +
    labs(title=paste('Percent of Women Holding College Degrees by Birth State,',year,sep=' '))
  png(paste('~/Desktop/map_',year,'.png',sep=''),width=1500,height=1000)
  print(map4)
  dev.off()
}