G. Elliott Morris elliottmorris

## trump_approve_by_mode.R
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

## white evangelical pop by state.csv

          
            state_name
            white_evangel
            n
            prop

            
              Alabama
              No
              2635716.4303168333
              0.7215633301038805

            
              Alabama
              Yes
              1017069.5696831668
              0.27843666989611954

            
              Alaska
              No
              408543.06241488626
              0.7700455804134742

            
              Alaska
              Yes
              122000.93758511376
              0.22995441958652582

            
              Arizona
              No
              3873710.103017639
              0.8261500325275896

            
              Arizona
              Yes
              815159.8969823611
              0.17384996747241044

            
              Arkansas
              No
              1325152.1454922806
              0.6069750351168169

            
              Arkansas
              Yes
              858054.8545077195
              0.3930249648831831

            
              California
              No
              22234460.694529217
              0.8909648084418956

## state_edu_targets_2016.csv

          
            state_abb
            pct_bachelors_higher

            
              DC
              0.6483020341946732

            
              MA
              0.4908509625882079

            
              CT
              0.4712702755955542

            
              CO
              0.4706548492306638

            
              NJ
              0.45938202372319153

            
              NY
              0.45520330118475155

            
              MD
              0.4496009185101967

            
              CA
              0.42358538595803436

            
              VA
              0.42333224401155256

## buttigeg_rise.R
# 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)

## .gitignore
# History files
.Rhistory
.Rapp.history

# Session Data files
.RData

# User-specific files
.Ruserdata

## mrp_cces.R
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

## democrats_generic_ballot.R
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")) +

## gen_corr_draw.R
# function written by HuffPost Pollster's Natalie Jackson for the 2016 election
# ibraries and import --------

library(dplyr)
library('Matrix')

seat_data <- read.csv("seat_partisanship_demog.csv")  # read in a 435
seat_data <- seat_data %>%
  select(clinton2016, obama2012, house2016, pct_nonwhite)

## main.R
# Libraries, data, setup ####
rm(list=ls())
library(tidyverse)
library(gridExtra)
library(googlesheets)
source("~/Desktop/theme_elliott.R")


# load in data from Gsheet
my_sheets <- gs_ls()

## 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 ####
	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
state_name	white_evangel	n	prop
Alabama	No	2635716.4303168333	0.7215633301038805
Alabama	Yes	1017069.5696831668	0.27843666989611954
Alaska	No	408543.06241488626	0.7700455804134742
Alaska	Yes	122000.93758511376	0.22995441958652582
Arizona	No	3873710.103017639	0.8261500325275896
Arizona	Yes	815159.8969823611	0.17384996747241044
Arkansas	No	1325152.1454922806	0.6069750351168169
Arkansas	Yes	858054.8545077195	0.3930249648831831
California	No	22234460.694529217	0.8909648084418956
	state_abb	pct_bachelors_higher
	DC	0.6483020341946732
	MA	0.4908509625882079
	CT	0.4712702755955542
	CO	0.4706548492306638
	NJ	0.45938202372319153
	NY	0.45520330118475155
	MD	0.4496009185101967
	CA	0.42358538595803436
	VA	0.42333224401155256
	# 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)
	# History files
	.Rhistory
	.Rapp.history

	# Session Data files
	.RData

	# User-specific files
	.Ruserdata
	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
	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")) +
	# function written by HuffPost Pollster's Natalie Jackson for the 2016 election
	# ibraries and import --------

	library(dplyr)
	library('Matrix')

	seat_data <- read.csv("seat_partisanship_demog.csv") # read in a 435
	seat_data <- seat_data %>%
	select(clinton2016, obama2012, house2016, pct_nonwhite)
	# Libraries, data, setup ####
	rm(list=ls())
	library(tidyverse)
	library(gridExtra)
	library(googlesheets)
	source("~/Desktop/theme_elliott.R")


	# load in data from Gsheet
	my_sheets <- gs_ls()
	## 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 ####