chrishanretty

### Replicate 
### 
### Kim, Nam Kyu. "Previous Military Rule and Democratic Survival." Journal of Conflict Resolution 65, no. 2-3 (2021): 534-562
###
### including an additional covariate for a hereditary head of state
### 
library(rio)
library(tidyverse)
library(vdemdata)
library(countrycode)

chrishanretty

library(tidyverse)
library(rio)
library(hrbrthemes)

### (1) Get files in
### Parish to Ward
### https://geoportal.statistics.gov.uk/datasets/ons::parish-to-ward-to-local-authority-district-december-2020-lookup-in-england-and-wales-v2/about

par2ward <- read.csv("Parish_to_Ward_to_Local_Authority_District__December_2020__Lookup_in_England_and_Wales_V2.csv")
par2ward$FID <- NULL

chrishanretty

library(tidyverse)
library(cmdstanr)
library(MCMCpack)

### log PDF is based on eqn. 3 in https://www2.seas.gwu.edu/~dorpjr/Publications/Bookchapter/MarcelDekker2003.pdf
stan_code <-
"
functions {
    real orddir_lpdf(vector y, real[] alpha) {
    int K = size(alpha);

chrishanretty

library(countrycode)
library(tidyverse)
library(rio)

holder <- list()

### (0) PartyFacts lookup
pf <- read.csv("data/partyfacts-external-parties.csv")

### (1) Manifesto Project

chrishanretty

### Let's generate some data with a step function
library(mgcv)
set.seed(1116)

nDays <- 200
day <- 1:nDays

y <- rnorm(200, 1, 0.25) + I(day > 100) * -0.5

y <- ifelse(y < 0,

chrishanretty

library(sp)
library(raster)
library(viridis)
library(rgdal)
library(rnaturalearth)


### need two things, presumably
###  - a polygon mask
###  - a population raster

chrishanretty

library(tidyverse)

### Pivot the table so we have four columns
### L/R, GAL/TAN, issue area, and value
dat <- read.csv("surridge1.csv") %>%
    pivot_longer(cols = -c(Column1, Column2),
                 names_to = "Area")

### Tidy up the factor levels and order
dat <- dat %>% 

chrishanretty

library(brms)
library(mice)
library(tictoc)

### Set a nice long number of iters so we can be sure it's not the
### overhead of spinning up cores, passing data, etc.,
nIters <- 1e5
thin <- nIters / 1000

# Load data

chrishanretty

### Start by plotting stuff
library(tidyverse)
library(hrbrthemes)

## Download data from https://docs.google.com/spreadsheets/d/1inaI17l9Q-XADRNE_toXLp65sKu4_x4bXUFjZ3qjB2o/edit?usp=sharing
dat <- read.csv("wherever_i_saved_the_google_docs.csv")

### Estimate a simple model which we'll use when graphing
mod <- glm(I(Funded == "Yes") ~ Rank2 + ConWinner1,
           family = binomial,

chrishanretty

library(tidyverse)
library(rdrobust)
library(hrbrthemes) 
library(cowplot)
library(Cairo)

dat <- readRDS("tidy_data.rds")

dat <- dat %>%
    mutate(count_satisfied.20 = round(pct_satisfied.20 * response.20),