Created
November 14, 2016 16:12
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FP - Figure 3
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#FP Figure 3 | |
#load packages | |
library(readr) | |
library(dplyr) | |
library(ggplot2) | |
library(RColorBrewer) | |
library(scales) | |
#read csv | |
csv <- read_csv("~/Desktop/usa_00028.csv") | |
#only women 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') | |
#create marriage status variable | |
edu_status <- race_grouped %>% mutate(Status=ifelse(MARST==6,'Unmarried','Married')) | |
#total number | |
total_num <- edu_status %>% group_by(YEAR, Race_2) %>% summarise(tnum=sum(PERWT)) | |
#number married | |
married_number <- edu_status %>% group_by(YEAR, Race_2, Status) %>% summarise(mnum=sum(PERWT)) | |
#join the two | |
joined <- left_join(total_num,married_number,by=c('YEAR'='YEAR','Race_2'='Race_2')) | |
#calculate percent | |
percentages <- joined %>% mutate(pct=mnum/tnum) | |
#graph | |
ggplot(data=percentages,aes(x=YEAR, y=pct, fill=Status)) + | |
geom_bar(stat='identity') + | |
labs(x='Year',y='Percent',fill='Marital Status', title='Marriage Rate by Race for Women over 20, 1940-2000') + | |
scale_y_continuous(labels=scales::percent) + | |
theme_bw(base_size=22) + | |
scale_fill_brewer(palette='Set2',guide=guide_legend(reverse=TRUE)) + | |
facet_wrap(~Race_2,ncol=6)+ | |
scale_x_continuous(breaks=c(1950,1970,1990)) | |
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