Project3_CarolineAllan.R

## Caroline Allan
## U.S. History Through Census Data
## Project 3
## Female Labor Force Participation 1880-2000
## February 11, 2016

rm(list = ls())

# load packages
library(readr)
library(dplyr)
library(ggplot2)
library(scales)
library(ggthemes)
library(tidyr)

## Set Working Directory
setwd("~/Desktop/HistThroughCensusData/Project3")

## Read In Data
data_raw <- read_csv('usa_00008.csv')

## Clean Data
a <- data_raw %>% 
  filter(SEX == 2, AGE >=18 & AGE < 65) %>% 
  select(YEAR, PERWT, SLWT, AGE, RACE, LABFORCE, INCWAGE, MARST) %>% 
  mutate(weight = ifelse(YEAR == 1950, SLWT, PERWT)) %>% 
  mutate(race = factor(ifelse(RACE == 1, 1, 0), labels = c('nonwhite', 'white'))) %>% 
  mutate(income = ifelse(INCWAGE == 999999, 0, INCWAGE)) %>% 
  mutate(married = factor(ifelse(MARST == 1 | MARST == 2, 1, 0), labels = c('Not Married', 'Married')))

a$InLab <- ifelse(a$LABFORCE == 2, 1, 0)
a$NotInLab <- ifelse(a$LABFORCE == 1, 1, 0)

## Labor force participation over time by race 
b <- a %>% 
  group_by(YEAR, race, married) %>% 
  summarise(InLab = sum(InLab*PERWT), NotInLab = sum(NotInLab*PERWT)) %>% 
  mutate(rate = InLab/(InLab + NotInLab))

## Plot 1
plot1 <- ggplot(b, aes(x = YEAR, y = rate, color = race)) +
  geom_line() + 
  geom_point() +
  theme_few() +
  labs(x = "Year",
       y = "Labor Force Participation Rate",
       title = "Female Labor Force Participation Rate by Race 1880-2000") +
  scale_x_continuous(breaks = c(1880, 1890, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 
                                1980, 1990, 2000)) +
  scale_y_continuous(labels = percent_format()) +
  facet_grid(married~.) +
  scale_color_manual(values=c("darkmagenta", "dodgerblue"), 
                     name  ="Race",
                     breaks=c("nonwhite", "white"),
                     labels=c("Non-White", "White"))
ggsave(file="participation.png",dpi=300)
plot1

## Median Income in each race in each year
c <- a %>% 
  filter(YEAR >= 1940) %>% 
  select(YEAR, race, married, income, weight, InLab)

d <- summarise(group_by(c, YEAR, race, married), inc = median(rep(as.numeric(income), times = weight)))

## Prices not adjusted
plot2 <- ggplot(d, aes(x = YEAR, y = inc, color = race)) +
  geom_line() + 
  geom_point() +
  theme_few() +
  labs(x = "Year",
       y = "Median Income",
       title = "Median Female Income by Race 1940-2000") +
  scale_x_continuous(breaks = c(1940, 1950, 1960, 1970, 
                                1980, 1990, 2000)) +
  facet_grid(married~.) +
  scale_color_manual(values=c("deeppink", "lightseagreen"), 
                     name  ="Race",
                     breaks=c("nonwhite", "white"),
                     labels=c("Non-White", "White")) +
  scale_y_continuous(labels = comma)
ggsave(file="income.png",dpi=300)
plot2

## Price adjustment
YEAR <- c(1940, 1950, 1960, 1970, 1980, 1990, 2000)
index <- c(11.986, 7, 5.725, 4.54, 2.295, 1.344, 1)

cpi <- as.data.frame(cbind(YEAR, index))

e <- merge(d, cpi, by = "YEAR", all.x = TRUE)

## Plot 3
plot3 <- ggplot(e, aes(x = YEAR, y = inc*index, color = race)) +
  geom_line() + 
  geom_point() +
  theme_few() +
  labs(x = "Year",
       y = "Adjusted Median Income",
       title = "Price-Adjusted Median Female Income by Race 1940-2000") +
  scale_x_continuous(breaks = c(1940, 1950, 1960, 1970, 
                                1980, 1990, 2000)) +
  facet_grid(married~.) +
  scale_color_manual(values=c("deeppink", "lightseagreen"), 
                     name  ="Race",
                     breaks=c("nonwhite", "white"),
                     labels=c("Non-White", "White")) +
  scale_y_continuous(labels = comma)
ggsave(file="adjincome.png",dpi=300)
plot3



## Median Income in each race in each year
f <- a %>% 
  filter(YEAR >= 1940) %>% 
  select(YEAR, race, married, income, weight, InLab) %>% 
  filter(InLab == 1)

g <- summarise(group_by(f, YEAR, race, married), inc = median(rep(as.numeric(income), times = weight)))

## Price adjustment
YEAR <- c(1940, 1950, 1960, 1970, 1980, 1990, 2000)
index <- c(11.986, 7, 5.725, 4.54, 2.295, 1.344, 1)

cpi <- as.data.frame(cbind(YEAR, index))

h <- merge(g, cpi, by = "YEAR", all.x = TRUE)

## Plot 4
plot4 <- ggplot(h, aes(x = YEAR, y = inc*index, color = race)) +
  geom_line() + 
  geom_point() +
  theme_few() +
  labs(x = "Year",
       y = "Adjusted Median Income",
       title = "Price-Adjusted Median Female Income In Labor Force by Race 1940-2000") +
  scale_x_continuous(breaks = c(1940, 1950, 1960, 1970, 
                                1980, 1990, 2000)) +
  facet_grid(married~.) +
  scale_color_manual(values=c("deeppink", "lightseagreen"), 
                     name  ="Race",
                     breaks=c("nonwhite", "white"),
                     labels=c("Non-White", "White")) +
  scale_y_continuous(labels = comma)
ggsave(file="inLabadjincome.png",dpi=300)
plot4