FP Figure 2

Figure 2

#FP Figure 2

#load packages

library(readr)

library(dplyr)

library(ggplot2)


library(RColorBrewer)

library(scales)



#read csv 
csv <- read_csv("~/Desktop/usa_00028.csv")
#only women and older than 22 
csv_new <- csv %>% filter(SEX==2 & AGE>=22)



#recode race
race_grouped <- csv_new %>% mutate(Race_2=factor(ifelse(HISPAN>0,1,
                                                         ifelse(RACESING==1,2,
                                                                ifelse(RACESING==2,3,
                                                                       ifelse(RACESING==3,4,
                                                                              ifelse(RACESING==4,5,6)))))))
levels(race_grouped$Race_2) <- c('Hispanic','White','Black','Native American','Asian','Other')

#filter out 'other' category
race_grouped2 <- race_grouped %>% filter(Race_2!='Other')

#make marriage variable 

mstatus <- race_grouped2 %>% mutate(MStatus=ifelse(MARST==6,'Unmarried','Married'))

#make degree/no degree variable
educ <- mstatus %>% mutate(EducLev=ifelse(EDUCD>=101 & EDUCD<=116, 'Degree', 'No Degree'))


#calculate number with degrees
num <- educ %>% group_by(YEAR, EducLev, MStatus, Race_2) %>% summarise(num=sum(PERWT))


#calculate total number

total_num <- educ %>% group_by(YEAR, MStatus, Race_2) %>% summarise(tnum=sum(PERWT))


#join

joined <- left_join(total_num,num)


#calc percent
percentages <- joined %>% mutate(pct=num/tnum*100)

# plot the percent with a college degree

percentages <- percentages %>% filter(EducLev=='Degree')



#plot it, line
ggplot(percentages,aes(x=YEAR,y=pct,color=Race_2)) +
  geom_line() + geom_point() +
  facet_grid(MStatus~.) +
  labs(x='Year',y='Percent',fill='Sex',title='Percent of Women over 22 with a College Degree by Race, 1940-2000') +
  scale_y_continuous(labels=scales::comma) +
  scale_x_continuous(breaks=c(1940,1960,1980,2000))