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living_at_home.R
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living_at_home.R
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# Replicating/expanding on Pew work: http://www.pewsocialtrends.org/2016/05/24/for-first-time-in-modern-era-living-with-parents-edges-out-other-living-arrangements-for-18-to-34-year-olds/
library(magrittr)
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(RSQLite)
ACS_db <- src_sqlite("ACS_db.sqlite3", create = T)
working <- read_csv("home.csv")
partners <- working %>%
filter(RELATED == 1114) %>%
select(SERIAL, YEAR)
working$partner <- 0
working$partner[(working$SERIAL %in% partners$SERIAL) & working$YEAR == partners$YEAR] <- 1
rm(partners)
gc(verbose = T)
younghome <- copy_to(ACS_db, working,
name = "young_home_data", temporary = FALSE)
rm(working)
# Connect to DB
ACS_db <- src_sqlite("ACS_db.sqlite3", create = F)
# Create a table of all the 2014 data. This won't actually pull it into memory.
younghome <- tbl(ACS_db, sql("SELECT * FROM young_home_data"))
younghome <- younghome %>%
filter(AGE >= 18, AGE < 35, YEAR >= 1960) %>%
select(SERIAL, YEAR, HHWT, GQ, GQTYPE, HHINCOME, PERWT, NCHILD, RELATE, RELATED, SEX, AGE, MARST, RACE, HISPAN,
SCHOOL, EDUC, EDUCD, EMPSTAT, WKSWORK2, UHRSWORK, INCTOT)
partners <- younghome %>%
filter(RELATED == 1114) %>%
select(SERIAL, YEAR) %>%
collect()
partners$partner <- 1
working <- younghome %>%
collect()
working <- left_join(working, partners, by = c("SERIAL", "YEAR"))
rm(partners)
working <- working %>%
mutate(partner = ifelse(is.na(partner), 0, 1),
home = ifelse(RELATE== 3, "Home",
ifelse(RELATE == 1 & MARST %in% c(1,2), "Married",
ifelse(RELATE == 2, "Married",
ifelse(RELATED == 1114, "Married",
ifelse(RELATE == 1 & partner == 1, "Married",
ifelse(RELATE == 1, "Alone", "Other")))))))
save.image()
table <- working %>%
filter(YEAR >= 1960) %>%
mutate(agegroup = cut(AGE, breaks = c(0, 24, 100), labels = c("Younger", "Older"))) %>%
group_by(YEAR, home, agegroup) %>%
summarize(count = sum(PERWT))
# Replicate Pew chart
table %>%
filter(YEAR %in% c(1960, 1970, 1980, 1990, 2000, 2010, 2014)) %>%
group_by(YEAR, home) %>%
summarize(count = sum(count)) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(YEAR, Home, Married) %>%
gather(home, value, -YEAR) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line()
working %>%
filter(YEAR %in% c(1960, 1970, 1980, 1990, 2000, 2010, 2014)) %>%
group_by(YEAR, home, SEX) %>%
summarize(count = sum(PERWT)) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(YEAR, Home, Married, SEX) %>%
gather(home, value, -YEAR, -SEX) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line() + facet_grid(~SEX)
# Split by age groups
table %>%
filter(YEAR %in% c(1960, 1970, 1980, 1990, 2000, 2010, 2014)) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(YEAR, Home, Married, agegroup) %>%
gather(home, value, -YEAR, -agegroup) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line() + facet_grid(~agegroup)
table %>%
filter(YEAR >= 1960) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(YEAR, Home, agegroup) %>%
gather(home, value, -YEAR, -agegroup) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line() + facet_grid(~agegroup)
table %>%
filter(YEAR >= 2000) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(YEAR, Home, Married, agegroup) %>%
gather(home, value, -YEAR, -agegroup) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line() + facet_grid(~agegroup)
# Demographics
working %>%
filter(YEAR >= 1960) %>%
mutate(EDUC = as.numeric(EDUC),
agegroup = cut(AGE, breaks = c(0, 24, 100), labels = c("Younger", "Older")),
college = cut(EDUC, breaks = c(0, 6, 9, 11), labels = c("HS or less", "Some college", "BA+"))) %>%
group_by(YEAR, home, agegroup, college) %>%
summarize(count = sum(PERWT)) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(-total, -Alone, -Other) %>%
filter(!is.na(college), agegroup == "Older") %>%
gather(home, value, -YEAR, -agegroup, -college) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line() + facet_grid(~college)
working %>%
filter(YEAR >= 1960) %>%
mutate(EMPSTAT = factor(EMPSTAT, labels = c("Employed", "Unemployed", "Not in labor force")),
SEX = factor(SEX, labels = c("Men", "Women")),
agegroup = cut(AGE, breaks = c(0, 24, 100), labels = c("Younger", "Older"))) %>%
group_by(YEAR, home, agegroup, EMPSTAT, SEX) %>%
summarize(count = sum(PERWT)) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(-total, -Alone, -Other) %>%
filter(agegroup == "Younger") %>%
gather(home, value, -YEAR, -agegroup, -EMPSTAT, -SEX) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line() + facet_wrap(~SEX + EMPSTAT)
working %>%
filter(YEAR >= 1980) %>%
mutate(job = ifelse(EMPSTAT == 2, "Unemployed",
ifelse(EMPSTAT == 3, "NILF",
ifelse(EMPSTAT == 1 & WKSWORK2 == 6 & UHRSWORK >=35, "FT", "PT"))),
SEX = factor(SEX, labels = c("Men", "Women")),
agegroup = cut(AGE, breaks = c(0, 24, 100), labels = c("Younger", "Older"))) %>%
group_by(YEAR, home, job, SEX) %>%
summarize(count = sum(PERWT)) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(-total, -Alone, -Other) %>%
gather(home, value, -YEAR, -job, -SEX) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line() + facet_wrap(~SEX + job)
# Share of 25-year-olds living at home
working %>%
filter(AGE == 25) %>%
mutate(mom = ifelse(home == "Home", PERWT, 0)) %>%
group_by(YEAR) %>%
summarize(home = sum(mom)/sum(PERWT))
working %>%
filter(AGE == 25) %>%
mutate(mom = ifelse(home == "Home", PERWT, 0),
SEX = factor(SEX, labels = c("Men", "Women"))) %>%
group_by(YEAR, SEX) %>%
summarize(home = sum(mom)/sum(PERWT)) %>%
spread(SEX, home)
working %>%
filter(AGE == 25) %>%
mutate(mom = ifelse(home == "Home", PERWT, 0),
SEX = factor(SEX, labels = c("Men", "Women"))) %>%
group_by(YEAR, SEX) %>%
summarize(home = sum(mom)/sum(PERWT)) %>%
ggplot(., aes(YEAR, home, colour = SEX)) + geom_line()
table %>%
group_by(YEAR, home) %>%
summarize(count = sum(count)) %>%
spread(home, count) %>%
mutate(total = Home + Married + Alone + Other,
Home = Home/total, Married = Married/total, Alone = Alone/total, Other = Other/total) %>%
select(-total) %>%
gather(home, value, -YEAR) %>%
ggplot(., aes(YEAR, value, colour = home)) + geom_line()
working %>%
filter(YEAR >= 1980) %>%
group_by(YEAR, home) %>%
summarize(count = sum(PERWT)) %>%
spread(home, count) %>%
mutate(total = Home + Partner + Married + Head + Other,
Home = Home/total, Married = (Married + Partner)/total, Head = Head/total, Other = Other/total) %>%
select(-total, -Partner)
working %>%
filter(YEAR == 2014) %>%
mutate(home = ifelse(RELATE== 3, "Home",
ifelse(RELATE == 1 & MARST %in% c(1), "Married",
ifelse(RELATE == 2, "Married",
ifelse(RELATED == 1114, "Married",
ifelse(RELATE == 1 & partner == 1, "Married",
ifelse(RELATE == 1, "Alone", "Other"))))))) %>%
group_by(home) %>%
summarize(count = sum(PERWT)) %>%
mutate(count = count/sum(count))