Code for analyzing public opinion and healthcare CBO impact numbers for the AHCA

AHCA_Opinion.R

## R Code for analysing AHCA CBO outlook and congress data
## Written by G. Elliott Morris
## @gelliottmorris | elliott@thecrosstab.com | TheCrosstab.com
## please do not replicate the analysis without permission.
##
## Additional info:
## DOWNLOAD DATA AT http://bit.ly/2oa0RPa
## Github Gist lives at: http://bit.ly/2nI5bZu

# libraries ####
rm(list = ls()) #reset the environment
library(arm)
library(foreign)
library(tidyverse)
library(stargazer)
library(USAboundaries)
source('~/Desktop/theme_elliott.R')

# load data ####
house <- read.csv("house_and_ahca.csv")
house$state <- substr(house$Code,1,2)
house <- house %>% mutate (Net_Support = Total.Support - Total.Oppose,
                           GOP_Margin = X2016.GOP - X2016.Dem,
                           Total_Loss_Nonelderly = as.numeric(Total_Loss_Nonelderly),
                           Loss_Hundreds = Total_Loss_Nonelderly/100)

house <- house[house$GOP_Margin > -100 & house$GOP_Margin < 100,] # only look at districts from 2016 that had competition
house$state_name <- sub(" .*", "", house$District)
house$cd114fp <- sub('.*\\-', '', house$Code)
house$cd114fp <- sub('AL', '01', house$cd114fp)

# analyze ####
stargazer(lm(Net_Support ~ GOP_Margin, house),
          lm(Net_Support ~ Loss_Hundreds,house),
          lm(Net_Support ~ GOP_Margin + Loss_Hundreds,house),
          type = "text",
          title = "Support for the AHCA by Congressional District")

mean(house[house$Party == "D",]$Total_Loss_Nonelderly,na.rm = TRUE)
mean(house[house$Party == "R",]$Total_Loss_Nonelderly,na.rm = TRUE)

mean(house[house$Party == "D",]$Net_Support,na.rm = TRUE)
mean(house[house$Party == "R",]$Net_Support,na.rm = TRUE)

median(house[house$Net_Support >=0,]$GOP_Margin,na.rm = TRUE)


# graphics ####

#map
library(USAboundaries)
library(USAboundariesData)
library(sp)
state_names <- as.list(state.name)
us_congress <- us_congressional(resolution = "low", states = as.character(state_names[!state_names %in% c("Hawaii","Alaska")]))


us_congress@data <- left_join(us_congress@data,house,by=c("state_name","cd114fp"))

#ggplot(us_congress) + 
  aes(long,lat,group=group) + 
  geom_polygon(aes(fill=data$Net_Support)) +
  geom_path(color="black") +
  coord_equal()+
  theme_void()



# margin & support - label
gg <- ggplot(data = house, aes(x=GOP_Margin, 
                               y = Net_Support)) +
  geom_hline(yintercept=0,linetype=2) +
  #geom_point()+
  geom_label(aes(label = Code,col = state)) +
  geom_smooth(method = "lm",se=FALSE) +
  theme_elliott() + 
  labs(title = "Support for A.H.C.A. by Congressional District",
       subtitle = "Net support as a function of gop margin of victory in each district",
       x = "GOP Margin of Victory (%)",
       y = "Net Support (%)",
       caption = "Data: http://bit.ly/2oa0RPa") +
  theme(legend.position = "none")


grid.arrange(gg,my_g,heights=c(9, .5))
dev.copy(png,"graphics/AHCA_GOP_Label.png",width=8,height=6,unit="in",res=350)
dev.off()


# margin and support - point

gg <- ggplot(data = house, aes(x=GOP_Margin, 
                               y = Net_Support)) +
  geom_hline(yintercept=0,linetype=2) +
  geom_point(shape = 1, aes(col = state))+
  geom_smooth(method = "lm",se=FALSE) +
  theme_elliott() + 
  labs(title = "Support for A.H.C.A. by Congressional District",
       subtitle = "Net support as a function of gop margin of victory in each district",
       x = "GOP Margin of Victory (%)",
       y = "Net Support (%)",
       caption = "Data: http://bit.ly/2oa0RPa") +
  theme(legend.position = "none")


grid.arrange(gg,my_g,heights=c(9, .5))
dev.copy(png,"graphics/AHCA_GOP_Point.png",width=8,height=6,unit="in",res=350)
dev.off()


# loss and support - label

gg <- ggplot(data = house, aes(x=Total_Loss_Nonelderly, 
                               y = Net_Support)) +
  geom_hline(yintercept=0,linetype=2) +
  #geom_point(shape = 1, aes(col = state))+
  geom_label(aes(label = Code,col = state)) +
  geom_smooth(method = "lm",se=FALSE) +
  theme_elliott() + 
  labs(title = "Support for A.H.C.A. by Congressional District",
       subtitle = "Net support as a function of total non-elederly health insurance loss per district",
       x = "Total Insurance Loss (Nonelderly)",
       y = "Net Support (%)",
       caption = "Data: http://bit.ly/2oa0RPa") +
  theme(legend.position = "none")


grid.arrange(gg,my_g,heights=c(9, .5))
dev.copy(png,"graphics/AHCA_Loss_Label.png",width=8,height=6,unit="in",res=350)
dev.off()

# loss and support - point

gg <- ggplot(data = house, aes(x=Total_Loss_Nonelderly, 
                               y = Net_Support)) +
  geom_hline(yintercept=0,linetype=2) +
  geom_point(shape = 1, aes(col = state))+
  #geom_label(aes(label = Code,col = state)) +
  geom_smooth(method = "lm",se=FALSE) +
  theme_elliott() + 
  labs(title = "Support for A.H.C.A. by Congressional District",
       subtitle = "Net support as a function of total non-elederly health insurance loss per district",
       x = "Total Insurance Loss (Nonelderly)",
       y = "Net Support (%)",
       caption = "Data: http://bit.ly/2oa0RPa") +
  theme(legend.position = "none")


grid.arrange(gg,my_g,heights=c(9, .5))
dev.copy(png,"graphics/AHCA_Loss_Point.png",width=8,height=6,unit="in",res=350)
dev.off()


# gop shift 2014 -> 2016
house_shift <- house %>% mutate(Shift =(X2016.Dem - X2016.GOP) - (X2014.Dem - X2014.GOP) ) %>% filter(X2016.Dem > 10, X2014.Dem > 10, X2016.GOP > 10, X2016.Dem > 10 ,Party == "R", Shift > 0) 


gg <- ggplot(data = house_shift, aes(x=Shift, 
                                     y = Net_Support)) +
  geom_hline(yintercept=0,linetype=2) +
  #geom_point(shape = 1, aes(col = state))+
  geom_label(aes(label = Code,col = GOP_Margin)) +
  geom_smooth(method = "lm",se=FALSE) +
  theme_elliott() + 
  labs(title = "Support for A.H.C.A. by Congressional District",
       subtitle = "Among Republican districs that shifted towards the Democratic house candidate from 2014 to 2016.",
       x = "Shift towards Democratic House Candidate (%)",
       y = "Net Support (%)",
       caption = "Data: http://bit.ly/2oa0RPa") +
  theme(legend.position = "right")+
  scale_color_gradient2("2016\nGOP Margin",low = "#A569BD",mid = "#E74C3C", high = "#E74C3C",midpoint = 30)


grid.arrange(gg,my_g,heights=c(9, .5))
dev.copy(png,"graphics/AHCA_Supp_SHIFT.png",width=8,height=6,unit="in",res=350)
dev.off()