Plotting uninsurance and health policy attitudes.

uninsurance_and_attitudes.R

# load packages
library(readr)  # for reading data
library(ggplot2)  # for graphics
library(dplyr)  # for data manipulation
library(gridExtra)  # for arranging plots in grids
library(texreg)  # for printing output
library(arm)  # for rescale() function

# load data
health_data <- read_csv("https://raw.githubusercontent.com/carlislerainey/intro-methods/gh-pages/data/health.csv")

# convert ideology to factor
health_data <- mutate(health_data, ideology_cat = cut(ideology, 
                  breaks = c(-Inf, quantile(ideology, c(1/3, 2/3)), Inf),
                  labels = c("Liberal", "Moderate", "Conservative")))

# plot 1: support expansion
gg1 <- ggplot(health_data, aes(x = percent_uninsured, 
                              y = percent_supporting_expansion)) + 
  geom_point(method = "rlm") +
  geom_smooth(method = "rlm") +
  geom_text(aes(label = state_abbr), hjust = -0.3, size = 3) +
  labs(x = "Percent Uninsured in 2012",
     y = "Percent Supporting Medicaid Expansion in 2012",
     title = "Attitudes Toward the Medicaid Expansion") +
  theme_bw()
plot(gg)

# plot 2: support aca
gg2 <- gg1 + aes(y = percent_favorable_aca) +
  labs(y = "Percent Favorable to ACA in 2012",
       title = "Attitudes Toward the ACA")
  plot(gg2)

# combine plots
pdf("~/Desktop/health1.pdf", height = 6, width = 15)
grid.arrange(gg2, gg1, ncol = 2)
dev.off()

# break down plot 1 by color
gg3 <- gg1 + aes(color = ideology_cat) + 
  labs(color = "Ideology") + 
  theme(legend.position = "bottom")
plot(gg3)

# break down plot 2 by color
gg4 <- gg3 + aes(y = percent_favorable_aca) +
  labs(y = "Percent Favorable to ACA in 2012",
       title = "Attitudes Toward the ACA")
plot(gg4)

# combine plots
pdf("~/Desktop/health2.pdf", height = 7, width = 15)
grid.arrange(gg4, gg3, ncol = 2)
dev.off()