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#line graph for race for single women | |
#load libraries | |
library(readr) | |
library(dplyr) | |
library(ggplot2) | |
library(RColorBrewer) | |
library(scales) | |
#load data | |
a <- read_csv('data/usa_00013.csv',col_types=cols(PERWT=col_double())) | |
#filter out those under 16 and over 65 | |
b <- a %>% filter(AGE>= 16 & AGE<= 65) | |
#Remove Alaska and Hawaii | |
bb <- b %>% filter(YEAR>=1960 | !(STATEFIP %in% c(2,15))) | |
#assign sex | |
c <- bb %>%mutate(Sex=factor(SEX, labels=c('Male','Female'))) | |
#Remove Men | |
d <- c %>% filter(Sex=='Female') | |
#Remove married women | |
e <- d %>% filter(MARST>=3) | |
#Create Race catagories | |
f <- e %>% mutate(Race=factor(ifelse(RACE==1,1, | |
ifelse(RACE==2,2, | |
ifelse(RACE==3,3,4))))) | |
g <- f %>% mutate(Race=factor(Race,labels=c('White','Black','Native American','Asian'))) | |
#Creat Occ avriable | |
h <- g %>% mutate(Occ=factor(ifelse(OCC1950>=980,1,2))) | |
#Split into employed/unemployed | |
i <- h %>% mutate(Occ=factor(Occ,labels=c('Not Employed','Employed'))) | |
#Group to calculate total single women of each race for each year | |
j <- i %>% group_by(YEAR,Race) %>% summarise(Total=sum(PERWT)) | |
#group to calculate total percent employed of single women for each year | |
k <- i %>% group_by(YEAR,Race,Occ) %>% summarise(Number=sum(PERWT)) | |
#link so we can calculate percent | |
l <- left_join(k,j,by=c('YEAR','Race')) | |
#Remove unemployed | |
m <- l %>% filter(Occ=='Employed') | |
#Calculate percentages | |
n <- m %>% mutate(Percent=Number*100/Total) | |
#Graph | |
ggplot(data=n, aes(x=YEAR, y=Percent, group=Race, colour=Race)) + | |
geom_line() + | |
labs(title='Percent Employment of Single Women by Race from 1920-1970',x='Year', colour='Race of Woman') + | |
scale_y_continuous(limits=c(0,100), breaks=c(0,25,50,75,100), | |
labels=c('0%','25%','50%','75%','100%')) + | |
ggsave('Fig2.pdf',width=10, height=7.5) |
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