chicago_missing_men.R

master_data = read.csv('usa_00005.csv')
library(readr)
library(dplyr)
library(ggplot2)
library(scales)
library(grid)

a <- filter(master_data, SEX<2)
usa_men <- a
e <- select(usa_men, YEAR, AGE, PERWT)
f <- mutate(e, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
head(f)
agec <- c('0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+')
g <- mutate(f, AGECAT=factor(AGECAT, labels = agec))
head(g)
h <- summarise(group_by(g, YEAR, AGECAT), NUMBER = sum(PERWT))
i <- ggplot(h, aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='US Men Population Density by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

j <- i + facet_grid(YEAR~.) + coord_flip()
print(j)




a <- filter(master_data, SEX<2)
b <- filter(master_data, RACE==2)
usa_black_men <- b
e <- select(b, YEAR, AGE, PERWT)
f <- mutate(e, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
head(f)
agec <- c('0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+')
g <- mutate(f, AGECAT=factor(AGECAT, labels = agec))
head(g)
h <- summarise(group_by(g, YEAR, AGECAT), NUMBER = sum(PERWT))
i <- ggplot(h, aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='US Black Men Population Density by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

j <- i + facet_grid(YEAR~.) + coord_flip()
print(j)





chicago_black_men <- filter(usa_black_men, METAREA==160)
l <- select(chicago_black_men, YEAR, AGE, PERWT)
m <- mutate(l, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
n <- mutate(m, AGECAT=factor(AGECAT, labels = agec))
o <- summarise(group_by(n, YEAR, AGECAT), NUMBER = sum(PERWT))
p <- ggplot(o, aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='Chicago Black Men Population Density by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

q <- p + facet_grid(YEAR~.)+coord_flip()
print(q)

chicago_black_men_2 <- filter(usa_black_men, METAREA==160, YEAR>1970)
l <- select(chicago_black_men_2, YEAR, AGE, PERWT)
m <- mutate(l, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
n <- mutate(m, AGECAT=factor(AGECAT, labels = agec))
o <- summarise(group_by(n, YEAR, AGECAT), NUMBER = sum(PERWT))
p <- ggplot(o, aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='Chicago Black Men Population by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

q <- p + facet_grid(YEAR~.)+coord_flip()
print(q)

usa_black_men_2 <- usa_black_men
e <- select(b, YEAR, AGE, PERWT)
f <- mutate(e, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
head(f)
agec <- c('0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+')
g <- mutate(f, AGECAT=factor(AGECAT, labels = agec))
head(g)
h <- summarise(group_by(g, YEAR, AGECAT), NUMBER = sum(PERWT))
i <- ggplot(filter(h, YEAR>1960), aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='US Black Men Population Density by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

j <- i + facet_grid(YEAR~.) + coord_flip()
print(j)


usa_men_2 <- usa_men
e <- select(usa_men_2, YEAR, AGE, PERWT)
f <- mutate(e, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
head(f)
agec <- c('0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+')
g <- mutate(f, AGECAT=factor(AGECAT, labels = agec))
head(g)
h <- summarise(group_by(g, YEAR, AGECAT), NUMBER = sum(PERWT))
i <- ggplot(filter(h, YEAR>1960), aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='US Men Population Density by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

j <- i + facet_grid(YEAR~.) + coord_flip()
print(j)

usa_men <- a
chicago_men <- filter(usa_men, METAREA==160)
e <- select(chicago_men, YEAR, AGE, PERWT)
f <- mutate(e, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
head(f)
agec <- c('0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+')
g <- mutate(f, AGECAT=factor(AGECAT, labels = agec))
head(g)
h <- summarise(group_by(g, YEAR, AGECAT), NUMBER = sum(PERWT))
i <- ggplot(h, aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='Chicago Men Population Density by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

j <- i + facet_grid(YEAR~.) + coord_flip()
print(j)


l <- select(chicago_men, YEAR, AGE, PERWT)
m <- mutate(l, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
n <- mutate(m, AGECAT=factor(AGECAT, labels = agec))
o <- summarise(group_by(n, YEAR, AGECAT), NUMBER = sum(PERWT))
p <- ggplot(filter(o, YEAR>1960), aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='Chicago Black Men Population by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)

q <- p + facet_grid(YEAR~.)+coord_flip()
print(q)

chicago_men <- filter(master_data, SEX<2, METAREA==160)
chi1 <- select(chicago_men, YEAR, AGE, PERWT)
chi2 <- mutate(chi1, AGECAT=ifelse(AGE>=90,9,floor(AGE/10)))
head(chi2)
agec <- c('0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+')
chi3 <- mutate(chi2, AGECAT=factor(AGECAT, labels = agec))
head(chi3)
chi4 <- summarise(group_by(chi3, YEAR, AGECAT), NUMBER = sum(PERWT))
chi5 <- ggplot(chi4, aes(x=AGECAT, y=NUMBER)) + geom_bar(stat = 'identity')+labs(title='Chicago Men Population Density by Year', x='Age Category',y="Population")+scale_y_continuous(labels=comma)
chi6 <- chi5 + facet_grid(YEAR~.) + coord_flip()
print(chi6)