family_project.R

setwd('/users/JakeEsse/Desktop/HIST 90')

library(readr)
library(dplyr)
library(ggplot2)
library(scales)
library(grid)

family_master_data <- read_csv('usa_00012.csv')

a <- family_master_data

b <- mutate(a, sex=factor(SEX, labels = c('male','female')))
head(b)

c <- filter(b, AGE>14, MARST<6, NCHILD>0)
head(c)

d <- mutate(c, marst=factor(ifelse(MARST==2, 1, MARST), labels = c('Married','Separated', 'Divorced', 'Widowed')))
head(d)

ee <- mutate(d, weight=ifelse(YEAR==1950, SLWT, PERWT))
head(ee)

e <- mutate(ee, income=ifelse(INCWAGE==999999, 0, INCWAGE))
head(e)

f <- mutate(e, nchild=factor(ifelse(NCHILD>5, 6, NCHILD), labels = c('1', '2', '3', '4', '5', '6+')))
head(f)

g <- filter(f, EMPSTAT==1)
head(g)

h <- summarize(group_by(g, YEAR, sex, marst), inc=median(rep(income, times=weight)))
head(h)

years <- c(1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010)
index <- c(15.58, 9.05, 7.37, 5.62, 2.65, 1.67, 1.27, 1)
cpi <- as.data.frame(cbind(years,index))
print(cpi)

i <- merge(h, cpi, by.x='YEAR', by.y='years')
head(i)

j <- ggplot(i, aes(x=YEAR, y=inc*index, color=marst))+geom_line()+geom_point()+facet_grid(.~sex)+labs(title='Median Wage Income, Inflation Adjusted', x='year', y='dollars')
print(j)
                          
k <- filter(g, MARST<5 & MARST!=3)

l <- summarize(group_by(k, YEAR, sex, marst, nchild), inc=median(rep(income, times=weight)))
head(l)

m <- merge(l, cpi, by.x='YEAR', by.y='years')
head(m)

n <- ggplot(m, aes(x=YEAR, y=inc*index, color=marst))+geom_line()+geom_point()+facet_grid(nchild~.~sex)+labs(title='Median Wage Income, Inflation Adjusted', x='year', y='dollars')
print(n)

o <- summarize(group_by(k, YEAR, sex, marst, nchild), NUMBER=sum(weight))
head(o)

p <- ggplot(o, aes(x=YEAR, y=NUMBER, fill=nchild)) + geom_bar(stat='identity', position = 'fill')+facet_grid(sex~.~marst) + scale_fill_brewer(palette = 'Set3') + guides(fill=guide_legend(reverse=TRUE,title='Children')) + labs(title='Number of Children by Sex and Marital Status', x='Year', y='Number') + scale_y_continuous(labels=scales::percent)
print(p)

q <- filter(k, NCHILD>3)

r <- summarize(group_by(q, YEAR, sex, marst, nchild), NUMBER=sum(weight))
head(r)

s <- ggplot(r, aes(x=YEAR, y=NUMBER, fill=nchild)) + geom_bar(stat='identity', position = 'fill')+facet_grid(sex~.~marst) + scale_fill_brewer(palette = 'Set3') + guides(fill=guide_legend(reverse=TRUE,title='Children')) + labs(title='Number of Children by Sex and Marital Status', x='Year', y='Number') + scale_y_continuous(labels=scales::percent)
print(s)

ll <- summarize(group_by(q, YEAR, sex, marst, nchild), inc=median(rep(income, times=weight)))
head(ll)

mm <- merge(ll, cpi, by.x='YEAR', by.y='years')
head(mm)

nn <- ggplot(mm, aes(x=YEAR, y=inc*index, color=marst))+geom_line()+geom_point()+facet_grid(nchild~.~sex)+labs(title='Median Wage Income, Inflation Adjusted', x='year', y='dollars')
print(nn)