Plot average levels of democracy per 2022 World Cup group

wc_groups.R

library(vdemdata)
library(tidyverse)
library(ggflags)
library(countrycode)
library(hrbrthemes)
### Get the V-Dem data in
data("vdem")
dat <- vdem |>
    distinct(country_name, country_text_id, year,
             v2x_polyarchy, v2x_polyarchy_sd) |>
    filter(!is.na(v2x_polyarchy)) |>
    group_by(country_name, country_text_id) |>
    arrange(desc(year)) |>
    slice(1) |>
    ungroup()

plot_func <- function(countries, dat, maintext) {
    df <- dat
    df <- df |>
        filter(country_text_id %in% countries)

### Fix for England and Wales
    if (any(countries %in% c("ENG",  "WAL"))) {
        df <- bind_rows(df,
                        dat |> filter(country_text_id == "GBR") |>
                        mutate(country_name = "England"))
        df <- bind_rows(df,
                        dat |> filter(country_text_id == "GBR") |>
                        mutate(country_name = "Wales"))
                        
    }
        
    df <- df |>
        arrange(v2x_polyarchy) |>
        mutate(country_name = fct_inorder(country_name),
               country_flag = countrycode(country_text_id,
                                          "iso3c",
                                          "iso2c"),
               country_flag = tolower(country_flag))

    ggplot(df, aes(x = country_name,
                   y = v2x_polyarchy,
                   ymin = v2x_polyarchy - qnorm(11/12) * v2x_polyarchy_sd,
                   ymax = v2x_polyarchy + qnorm(11/12) * v2x_polyarchy_sd)) +
        scale_x_discrete("") +
        scale_y_continuous("Liberal democracy index [0-1]",
                           limits = c(0, 1)) + 
        geom_pointrange(shape = 21, col = "#df4ca3", fill = "#d6f079") +
        geom_hline(yintercept = global_avg, col = "#df4ca377", linetype = 2) + 
        labs(title = maintext,
             subtitle = "Plotted points indicate level of liberal democracy; lines show 83% confidence intervals. Dotted line shows average amongst all competing teams. ",
             caption = "Data from the V-Dem project: v-dem.net. Graphic: @chrishanretty") + 
        coord_flip() + 
        theme_ft_rc(base_size = 14)  +
        theme(plot.title.position = "plot",
              plot.caption.position =  "plot")
}

grp_a <- c("QAT", "ECU", "SEN", "NLD")
grp_b <- c("ENG", "IRN", "USA", "WAL")
grp_c <- c("ARG", "SAU", "MEX", "POL")
grp_d <- c("FRA", "AUS", "DNK", "TUN")
grp_e <- c("ESP", "CRI", "DEU", "JPN")
grp_f <- c("BEL", "CAN", "MAR", "HRV")
grp_g <- c("BRA", "SRB", "CHE", "CMR")
grp_h <- c("PRT", "GHA", "URY", "KOR")

global_avg <- dat |>
    filter(country_text_id %in% c(grp_a, grp_b, grp_c, grp_d,
                                  grp_e, grp_f, grp_g, grp_h,
                                  "GBR")) |>
    pull(v2x_polyarchy) |>
    mean()

grp_a_plot <- plot_func(grp_a, dat, "Group A")
grp_b_plot <- plot_func(grp_b, dat, "Group B")

grp_c_plot <- plot_func(grp_c, dat, "Group C")
grp_d_plot <- plot_func(grp_d, dat, "Group D")

grp_e_plot <- plot_func(grp_e, dat, "Group E")
grp_f_plot <- plot_func(grp_f, dat, "Group F")

grp_g_plot <- plot_func(grp_g, dat, "Group G")
grp_h_plot <- plot_func(grp_h, dat, "Group H")

ggsave(filename = "grp_a_plot.png", grp_a_plot, width = 1200 / 96, height = 675 / 96)
ggsave(filename = "grp_b_plot.png", grp_b_plot, width = 1200 / 96, height = 675 / 96)
ggsave(filename = "grp_c_plot.png", grp_c_plot, width = 1200 / 96, height = 675 / 96)
ggsave(filename = "grp_d_plot.png", grp_d_plot, width = 1200 / 96, height = 675 / 96)
ggsave(filename = "grp_e_plot.png", grp_e_plot, width = 1200 / 96, height = 675 / 96)
ggsave(filename = "grp_f_plot.png", grp_f_plot, width = 1200 / 96, height = 675 / 96)
ggsave(filename = "grp_g_plot.png", grp_g_plot, width = 1200 / 96, height = 675 / 96)
ggsave(filename = "grp_h_plot.png", grp_h_plot, width = 1200 / 96, height = 675 / 96)