###########################################################################
# PUBLG100: Introduction to Quantitative Methods
#
# Week 4 Solutions: Bivariate linear regression models
#
#

## ----message = FALSE-----------------------------------------------------
library(texreg)
library(Zelig)
library(dplyr)

## ----eval = FALSE--------------------------------------------------------
## # Change your working directory
## setwd("N:/PUBLG100")
## 
## # Check your working directory
## getwd()
## 
## # clear the environment
## rm(list = ls())

## ------------------------------------------------------------------------
communities <- read.csv("http://uclspp.github.io/PUBLG100/data/communities.csv")
communities_employment <- read.csv("http://uclspp.github.io/PUBLG100/data/communities_employment.csv")
communities_immigration <- read.csv("http://uclspp.github.io/PUBLG100/data/communities_immig.csv")

communities <- merge(communities, communities_employment, by = c("state", "communityname"))
communities <- merge(communities, communities_immigration, by = c("state", "communityname"))

## ------------------------------------------------------------------------
communities <- select(communities, 
                      state, 
                      Community = communityname, 
                      UnemploymentRate = PctUnemployed, 
                      NoHighSchool = PctNotHSGrad,
                      White = racePctWhite,
                      RecentImmigration5 = PctImmigRec5)

## ------------------------------------------------------------------------
model_immigration <- lm(UnemploymentRate ~ RecentImmigration5, data = communities)

## ------------------------------------------------------------------------
plot(communities$RecentImmigration5, communities$UnemploymentRate,
     xlab = "Recent Immigration",
     ylab = "Unemployment Rate")

abline(model_immigration, col = "red")

## ------------------------------------------------------------------------
model_education <- lm(UnemploymentRate ~ NoHighSchool, data = communities)

communities$Minority <- 1 - communities$White
model_minority <- lm(UnemploymentRate ~ Minority, data = communities)

screenreg(list(model_education, model_minority, model_immigration))

## ------------------------------------------------------------------------
htmlreg(list(model_education, model_minority, model_immigration), file = "solutions4_model_comparison.doc")

## ------------------------------------------------------------------------
z.out <- zelig(UnemploymentRate ~ RecentImmigration5, data = communities, model = "ls")
x.out <- setx(z.out, RecentImmigration5 = seq(0, 1, 0.1))
s.out <- sim(z.out, x = x.out)

plot(s.out, qi = "pv", xlab = "Recent Immigration (last 5 years)")

## ----eval = FALSE--------------------------------------------------------
## png(filename = "solutions4_model_immigration.png")
## plot(communities$RecentImmigration5, communities$UnemploymentRate,
##      xlab = "Recent Immigration",
##      ylab = "Unemployment Rate")
## abline(model_immigration, col = "red")
## dev.off()

## ----eval = FALSE--------------------------------------------------------
## png(filename = "solutions4_conf_interval.png")
## plot(s.out, qi = "pv", xlab = "Recent Immigration")
## dev.off()