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library(tidyverse)
library(modelbased)
library(magrittr)

overview <- readRDS("data/overview.rds")

## helper function
get_plabs <- function (pval) {
  dplyr::case_when(is.na(pval) ~ "", pval < 0.001 ~ "***", 
                   pval < 0.01 ~ "**", pval < 0.05 ~ "*", 

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  ## Create NYT Spiral Animations
  ## Most Code comes from here: https://bydata.github.io/nyt-corona-spiral-chart/
  
  
  library(tidyverse)
  library(lubridate)
  library(gganimate)
  library(viridis)
  
  owid_url <- "https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data.csv?raw=true"

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### Code mostly inspired by https://github.com/HudsonJamie/tidy_tuesday/blob/main/2021/week_46/afrilearndata.R

library(pdftools)
library(tidygeocoder)
library(tidyverse)
library(janitor)
library(rgdal)
library(osmdata)
library(sf)
library(ggfx)

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library(sf)
library(cowplot)
library(showtext)
library(sysfonts)
library(ggplot2)
showtext_auto()
font_add_google("Quicksand")
font_add_google("Fira Sans")
font1 <- "Quicksand"

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### The following functions borrow heavily from this gist by @jnolis
### https://gist.github.com/jnolis/a41c196a5e22e2a2115d28e853d4780c
### Unfortunately, the shiny app framework didn't work for me
### So I created an interactive console script that guides you through authentification
### No shiny needed

# This function creates a URL for users to click to authenticate.
# You should use it to show a URL when users haven't authenticated yet.
get_authorization_url <- function(app, permission=NULL){

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<script>
  (function() {
    var divHTML = document.querySelectorAll(".details-open");
    divHTML.forEach(function (el) {
      var preNodes = el.getElementsByTagName("pre");
      var outputNode = preNodes[1];
      outputNode.outerHTML = "<details open class='output'><summary>Run</summary>" + outputNode.outerHTML + "</details>";
    })
  })();
(function() {

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  mutate(type = case_when(
    type == "Regio" ~ "Local Politics",
    type == "Rest" ~ "Party Youth Organization/Dutch Politicians (Other)",
    type == "Kandidaat" ~ "Dutch Politician",
    type == "Landelijk" ~ "Dutch National Party"
  ))

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paadc_ids_2021_01_22 <- old_data%>% 
  # distinct(paadc) %>% 
  mutate(impressionTime = lubridate::ymd_hms(impressionTime)) %>% 
  group_by(paadc) %>% 
  filter(impressionTime == min(impressionTime)) %>% 
  ungroup() %>% 
  distinct(paadc, .keep_all = T)  %>%
  arrange(impressionTime) %>% 
  mutate(firstImpressionTime = as.character(impressionTime)) %>% 
  select(paadc, firstImpressionTime) %>% 

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library(tidyverse)
library(ggbeeswarm)
library(ggthemes)
library(glue)

elex <- read_csv("https://raw.githubusercontent.com/favstats/USElection2020-NYT-Results/master/data/latest/presidential.csv")

elex %>% 
  mutate(votes_perc = votes/sum(votes)) %>% 
  mutate(abs_margin2020 = abs(margin2020)) %>% 

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library(tidyverse)
library(lavaan)

measurement_invariance <- function(model, data, group, fit_measures) {
  
  ## Configural Model
  model1 <- cfa(model = model, data = data, group = group)
  ## Weak/metric invariance
  model2 <- cfa(model = model, data = data, group = group, group.equal=c("loadings"))
  ## Strong/scalar invariance