FP - Occ

Occ

#open libraries

library(ggplot2)
library(dplyr)
library(readr)

library(RColorBrewer)
#read csv 
csv <- read_csv('~/Desktopusa_00028.csv')


#only women over 22
women <- csv_new %>% filter(SEX==2 & AGE>=22)


# Create Occupation Variable
Occupations <- women %>% mutate(Occupation=factor(ifelse(OCC1950>=979 & OCC1950<=999,1,
                                                         ifelse(OCC1950>=100 & OCC1950<=123 | OCC1950>=810 & OCC1950<=840, 2,
                                                                ifelse(OCC1950>=500 & OCC1950<=690 | OCC1950>=910 & OCC1950<=970,3,
                                                                       ifelse(OCC1950>=200 & OCC1950<=490,4,
                                                                              ifelse(OCC1950>=700 & OCC1950<=790,5,
                                                                                     ifelse(OCC1950>=000 & OCC1950<=99,6,0)))))),
                                                  labels=c('None','Farmers and Farm Laborers','Craftsmen/Operatives/Laborers',
                                                           'Managerial/Clerical/Sales','Service',
                                                           'Professional')))
#recode sex
sex <- Occupations %>% mutate(Sex=ifelse(SEX==2,'female'))

#group race
race_grouped <- sex %>% mutate(Race=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) <- c('Hispanic','White','Black','Native American','Asian','Other')
#select only needed variables
select <- race_grouped %>% select(YEAR,PERWT,Occupation,Sex, Race)

#make graph groupings - Year, Sex, Region, and Occupation
group_2 <- select %>% group_by(YEAR, Sex, Race, Occupation) %>% summarise(Number=sum(PERWT))


#graphing figure 4
ggplot(data=group_2,aes(x=YEAR, y=Number, fill=Occupation)) + 
  geom_bar(stat='identity', position='fill') +
  labs(x='Year',y='Percent of Population',fill='Occupation', title='Occupation of Women Over 20 by Race and Year, 1940-2000') +
  scale_y_continuous(labels=scales::percent) +
  scale_x_continuous(breaks=c(1940,1960,1980,2000)) +
  scale_fill_brewer(palette='Set1') +
  facet_grid(~Race) + 
  theme(legend.position='bottom')