elliottmorris

library(tidyverse)
library(zoo)

trump_approve <- read_csv("https://projects.fivethirtyeight.com/polls-page/president_approval_polls.csv")

trump_approve <- trump_approve %>% 
  mutate("date" = mdy(end_date)) %>% 
  dplyr::select(pollster,
                date, 
                "approve" = yes,

elliottmorris

library(tidyverse)
library(politicaldata)
library(lubridate)
library(zoo)
library(gghighlight)

today_2020_time_difference <- as.numeric(difftime(ymd('2020-11-03'),Sys.Date(),units = 'days'))

# wrangle -----------------------------------------------------------------
# historical

elliottmorris

rm(list = ls()) #reset the environment
library(tidyverse)
library(lubridate)
library(mgcv)

exponent_weight <- function(i) {
  exp(-0.04*i)
}

TODAY_DAY <- difftime(Sys.Date(),as.Date("2017-01-21"))

elliottmorris

library(tidyverse)
library(lubridate)
library(colorspace)

# get raw polls
if(!exists("raw_polls")){
  raw_polls <- read_csv('https://projects.fivethirtyeight.com/polls-page/president_approval_polls.csv')
  }

# selects cols etc

elliottmorris

elliottmorris

elliottmorris

# This file runs an analysis of 2020 Democratic primary polls to 
# assess relationships between candidate's polling averages using
# variance-covariance matrices.
# 
# Read more about it here: 
# https://thecrosstab.substack.com/p/buttigiegs-rise-has-cost-biden-and

rm(list=ls())
library(tidyverse)
library(lubridate)

elliottmorris

# History files
.Rhistory
.Rapp.history

# Session Data files
.RData

# User-specific files
.Ruserdata

elliottmorris

vote_model <- brm(mvbind(vote_trump,vote_clinton) ~ 1 +
                    # pop level
                    state_clinton + state_black_pct + state_hispanic_pct + state_white_protestant + 
                    # group effects
                    (1|sex) + (1|race) + (1|age) + (1|edu) + (1|state_name) + (1|region) +
                    # interactions b/t group effects
                    (1|race:edu) + (1|age:edu) + (1|race:region),
                  data = cces,
                  family = bernoulli(link = "logit"),
                  # stan stuff

elliottmorris

library(tidyverse)
polls_2018 <- read_tsv("http://elections.huffingtonpost.com/pollster/api/v2/questions/18-US-House/poll-responses-clean.tsv") %>% 
    filter(month(end_date)>=6) %>% 
    mutate(Dem.Margin = Democrat-Republican) %>%
    group_by(mode) %>%
    mutate(mean_dem = mean(Dem.Margin))
    
ggplot(polls_2018,aes(x=Dem.Margin,fill=Dem.Margin>0,col=Dem.Margin>0)) + 
    geom_bar(width=0.8,alpha=0.5) + 
    scale_color_manual(values=c("TRUE"="blue","FALSE"="red")) +