ct_selection_untitled-66824AC0.R

library(rio)
library(janitor)

gap <- import("E://data/global_attitudes.sav") %>% clean_names() %>% as_tibble()

gap %>% ct(country)

gap <- gap %>% 
  mutate(country = frcode(
    country == 1 ~ "Australia",
    country == 2 ~ "Belgium",
    country == 3 ~ "Canada",
    country == 4 ~ "France",
    country == 5 ~ "Germany",
    country == 6 ~ "Greece",
    country == 7 ~ "Hungary",
    country == 8 ~ "Israel",
    country == 9 ~ "Italy",
    country == 10 ~ "Japan",
    country == 11 ~ "Malaysia",
    country == 12 ~ "Netherlands",
    country == 13 ~ "Poland",
    country == 14 ~ "Singapore",
    country == 15 ~ "South Korea",
    country == 16 ~ "Spain",
    country == 17 ~ "Sweden",
    country == 18 ~ "UK"))

library(dplyr)

protest_data <- gap %>%
  mutate(imp = case_when(citizen_protest == 1 ~ 1, 
                         citizen_protest %in% c(2, 3, 4) ~ 0)) %>%
  group_by(country) %>%
  mean_ci(imp, wt = weight, ci = .84) %>%
  mutate(type = "Joining Demonstrations")

religion_data <- gap %>%
  mutate(imp = case_when(citizen_religion == 1 ~ 1, 
                         citizen_religion %in% c(2, 3, 4) ~ 0)) %>%
  group_by(country) %>%
  mean_ci(imp, wt = weight, ci = .84) %>%
  mutate(type = "Attending Religious Services")

vote_data <- gap %>%
  mutate(imp = case_when(citizen_vote == 1 ~ 1, 
                         citizen_vote %in% c(2, 3, 4) ~ 0)) %>%
  group_by(country) %>%
  mean_ci(imp, wt = weight, ci = .84) %>%
  mutate(type = "Voting")

follow_data <- gap %>%
  mutate(imp = case_when(citizen_follow == 1 ~ 1, 
                         citizen_follow %in% c(2, 3, 4) ~ 0)) %>%
  group_by(country) %>%
  mean_ci(imp, wt = weight, ci = .84) %>%
  mutate(type = "Follow Local Events")

intlevents_data <- gap %>%
  mutate(imp = case_when(citizen_intlevents == 1 ~ 1, 
                         citizen_intlevents %in% c(2, 3, 4) ~ 0)) %>%
  group_by(country) %>%
  mean_ci(imp, wt = weight, ci = .84) %>%
  mutate(type = "Follow Intern. Events")

environment_data <- gap %>%
  mutate(imp = case_when(citizen_environment == 1 ~ 1, 
                         citizen_environment %in% c(2, 3, 4) ~ 0)) %>%
  group_by(country) %>%
  mean_ci(imp, wt = weight, ci = .84) %>%
  mutate(type = "Reduce Climate Change")

vaccine_data <- gap %>%
  mutate(imp = case_when(citizen_vaccine == 1 ~ 1, 
                         citizen_vaccine %in% c(2, 3, 4) ~ 0)) %>%
  group_by(country) %>%
  mean_ci(imp, wt = weight, ci = .84) %>%
  mutate(type = "Getting COVID Vaccine")

# Combine all the results into one dataframe
all <- bind_rows(protest_data, religion_data, vote_data, follow_data, intlevents_data, environment_data, vaccine_data)

color_mapping <- c(
  "Attending Religious Services" = "#FEDC97",
  "Follow Intern. Events" = "#5f2680",
  "Follow Local Events" = "#B5B682",
  "Getting COVID Vaccine" = "#7D3C98",
  "Joining Demonstrations" = "#033f63",
  "Reduce Climate Change" = "#28666e",
  "Voting" = "#5E4A78"
)

all %>% 
  mutate(lab= round(mean, 2)) %>% 
  ggplot(., aes(x = type, y = mean, fill = type)) +
  geom_col(color = "black") +
  facet_wrap(~ country) +
  coord_flip() +
  y_pct() + 
  geom_text(aes(y = mean + .1, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 5, family = "font") +
  scale_fill_manual(values = color_mapping) +
  theme_rb() +
  labs(x = "", y = "", title = "Share Saying These Things Are 'Very Important' to Being Good Members of a Society", caption = "@ryanburge + @religiondata\nData: Global Attitudes Project, Spring 2022")
save("gap_activity_types.png", ht = 10, wd = 14)


# Continuing the country-specific mapping for religion codes
map_religion <- function(country, rel_column) {
  case_when(
    # Australia
    country == "Australia" & rel_column == 1 ~ "Roman Catholic",
    country == "Australia" & rel_column == 2 ~ "Protestant",
    country == "Australia" & rel_column == 3 ~ "Orthodox Christian",
    country == "Australia" & rel_column == 4 ~ "Atheist",
    country == "Australia" & rel_column == 5 ~ "Agnostic",
    country == "Australia" & rel_column == 6 ~ "Muslim",
    country == "Australia" & rel_column == 7 ~ "Something else",
    country == "Australia" & rel_column == 8 ~ "Nothing in particular",
    country == "Australia" & rel_column == 9 ~ "Christian, non-specific",
    country == "Australia" & rel_column == 10 ~ "Jehovah's Witness",
    country == "Australia" & rel_column == 11 ~ "Baha'i",
    country == "Australia" & rel_column == 12 ~ "Hindu",
    country == "Australia" & rel_column == 13 ~ "Buddhist",
    country == "Australia" & rel_column == 14 ~ "Sikh",
    country == "Australia" & rel_column == 15 ~ "Jewish",
    country == "Australia" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Belgium
    country == "Belgium" & rel_column == 1 ~ "Roman Catholic",
    country == "Belgium" & rel_column == 2 ~ "Protestant",
    country == "Belgium" & rel_column == 3 ~ "Muslim",
    country == "Belgium" & rel_column == 4 ~ "Jewish",
    country == "Belgium" & rel_column == 5 ~ "Buddhist",
    country == "Belgium" & rel_column == 7 ~ "Atheist",
    country == "Belgium" & rel_column == 8 ~ "Agnostic",
    country == "Belgium" & rel_column == 9 ~ "Something else",
    country == "Belgium" & rel_column == 10 ~ "Nothing in particular",
    country == "Belgium" & rel_column == 11 ~ "Just a Christian",
    country == "Belgium" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Canada
    country == "Canada" & rel_column == 1 ~ "Roman Catholic",
    country == "Canada" & rel_column == 2 ~ "Protestant",
    country == "Canada" & rel_column == 3 ~ "Christian Orthodox",
    country == "Canada" & rel_column == 4 ~ "Jewish",
    country == "Canada" & rel_column == 5 ~ "Muslim",
    country == "Canada" & rel_column == 6 ~ "Sikh",
    country == "Canada" & rel_column == 7 ~ "Hindu",
    country == "Canada" & rel_column == 8 ~ "Buddhist",
    country == "Canada" & rel_column == 9 ~ "Atheist",
    country == "Canada" & rel_column == 10 ~ "Agnostic",
    country == "Canada" & rel_column == 11 ~ "Something else",
    country == "Canada" & rel_column == 12 ~ "Nothing in particular",
    country == "Canada" & rel_column == 13 ~ "Just a Christian",
    country == "Canada" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # France
    country == "France" & rel_column == 1 ~ "Roman Catholic",
    country == "France" & rel_column == 2 ~ "Protestant",
    country == "France" & rel_column == 3 ~ "Muslim",
    country == "France" & rel_column == 4 ~ "Jewish",
    country == "France" & rel_column == 5 ~ "Atheist",
    country == "France" & rel_column == 6 ~ "Agnostic",
    country == "France" & rel_column == 7 ~ "Something else",
    country == "France" & rel_column == 8 ~ "Nothing in particular",
    country == "France" & rel_column == 9 ~ "Just a Christian",
    country == "France" & rel_column == 10 ~ "Orthodox Christian",
    country == "France" & rel_column == 11 ~ "Buddhist",
    country == "France" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Germany
    country == "Germany" & rel_column == 1 ~ "Roman Catholic",
    country == "Germany" & rel_column == 2 ~ "Protestant",
    country == "Germany" & rel_column == 3 ~ "Muslim",
    country == "Germany" & rel_column == 4 ~ "Jewish",
    country == "Germany" & rel_column == 5 ~ "Atheist",
    country == "Germany" & rel_column == 6 ~ "Agnostic",
    country == "Germany" & rel_column == 7 ~ "Something else",
    country == "Germany" & rel_column == 8 ~ "Nothing in particular",
    country == "Germany" & rel_column == 9 ~ "Just a Christian",
    country == "Germany" & rel_column == 10 ~ "Orthodox Christian",
    country == "Germany" & rel_column == 11 ~ "Buddhist",
    country == "Germany" & rel_column == 12 ~ "Hindu",
    country == "Germany" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Greece
    country == "Greece" & rel_column == 1 ~ "Orthodox Christian",
    country == "Greece" & rel_column == 2 ~ "Roman Catholic",
    country == "Greece" & rel_column == 4 ~ "Muslim",
    country == "Greece" & rel_column == 5 ~ "Atheist",
    country == "Greece" & rel_column == 6 ~ "Agnostic",
    country == "Greece" & rel_column == 7 ~ "Something else",
    country == "Greece" & rel_column == 8 ~ "Nothing in particular",
    country == "Greece" & rel_column == 9 ~ "Just a Christian",
    country == "Greece" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Italy
    country == "Italy" & rel_column == 1 ~ "Roman Catholic",
    country == "Italy" & rel_column == 2 ~ "Protestant",
    country == "Italy" & rel_column == 3 ~ "Orthodox Christian",
    country == "Italy" & rel_column == 4 ~ "Muslim",
    country == "Italy" & rel_column == 6 ~ "Atheist",
    country == "Italy" & rel_column == 7 ~ "Agnostic",
    country == "Italy" & rel_column == 8 ~ "Something else",
    country == "Italy" & rel_column == 9 ~ "Nothing in particular",
    country == "Italy" & rel_column == 10 ~ "Just a Christian",
    country == "Italy" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # South Korea
    country == "South Korea" & rel_column == 1 ~ "Buddhist",
    country == "South Korea" & rel_column == 2 ~ "Roman Catholic",
    country == "South Korea" & rel_column == 3 ~ "Protestant",
    country == "South Korea" & rel_column == 5 ~ "Confucianism or traditional religion",
    country == "South Korea" & rel_column == 6 ~ "Atheist",
    country == "South Korea" & rel_column == 7 ~ "Agnostic",
    country == "South Korea" & rel_column == 8 ~ "Something else",
    country == "South Korea" & rel_column == 9 ~ "Nothing in particular",
    country == "South Korea" & rel_column == 10 ~ "Just a Christian",
    country == "South Korea" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Israel
    country == "Israel" & rel_column == 1 ~ "Jewish",
    country == "Israel" & rel_column == 2 ~ "Muslim",
    country == "Israel" & rel_column == 3 ~ "Orthodox Christian",
    country == "Israel" & rel_column == 4 ~ "Roman Catholic",
    country == "Israel" & rel_column == 5 ~ "Druze",
    country == "Israel" & rel_column == 6 ~ "Atheist",
    country == "Israel" & rel_column == 8 ~ "Something else",
    country == "Israel" & rel_column == 10 ~ "Just a Christian",
    country == "Israel" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    country == "Poland" & rel_column == 1 ~ "Roman Catholic",
    country == "Poland" & rel_column == 2 ~ "Orthodox Christian",
    country == "Poland" & rel_column == 4 ~ "Jehovah's Witness",
    country == "Poland" & rel_column == 10 ~ "Atheist",
    country == "Poland" & rel_column == 11 ~ "Agnostic",
    country == "Poland" & rel_column == 13 ~ "Nothing in particular",
    country == "Poland" & rel_column == 14 ~ "Just a Christian",
    country == "Poland" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Singapore
    country == "Singapore" & rel_column == 1 ~ "Buddhist",
    country == "Singapore" & rel_column == 2 ~ "Muslim",
    country == "Singapore" & rel_column == 3 ~ "Protestant",
    country == "Singapore" & rel_column == 4 ~ "Roman Catholic",
    country == "Singapore" & rel_column == 5 ~ "Hindu",
    country == "Singapore" & rel_column == 6 ~ "Traditional religion",
    country == "Singapore" & rel_column == 7 ~ "Atheist",
    country == "Singapore" & rel_column == 8 ~ "Agnostic",
    country == "Singapore" & rel_column == 9 ~ "Something else",
    country == "Singapore" & rel_column == 10 ~ "Nothing in particular",
    country == "Singapore" & rel_column == 11 ~ "Just a Christian",
    country == "Singapore" & rel_column == 13 ~ "Taoism",
    country == "Singapore" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # South Korea
    country == "South Korea" & rel_column == 1 ~ "Buddhist",
    country == "South Korea" & rel_column == 2 ~ "Roman Catholic",
    country == "South Korea" & rel_column == 3 ~ "Protestant",
    country == "South Korea" & rel_column == 5 ~ "Confucianism or traditional religion",
    country == "South Korea" & rel_column == 6 ~ "Atheist",
    country == "South Korea" & rel_column == 7 ~ "Agnostic",
    country == "South Korea" & rel_column == 8 ~ "Something else",
    country == "South Korea" & rel_column == 9 ~ "Nothing in particular",
    country == "South Korea" & rel_column == 10 ~ "Just a Christian",
    country == "South Korea" & rel_column == 11 ~ "Mormon",
    country == "South Korea" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Spain
    country == "Spain" & rel_column == 1 ~ "Roman Catholic",
    country == "Spain" & rel_column == 2 ~ "Protestant",
    country == "Spain" & rel_column == 3 ~ "Muslim",
    country == "Spain" & rel_column == 4 ~ "Jewish",
    country == "Spain" & rel_column == 5 ~ "Atheist",
    country == "Spain" & rel_column == 6 ~ "Agnostic",
    country == "Spain" & rel_column == 7 ~ "Something else",
    country == "Spain" & rel_column == 8 ~ "Nothing in particular",
    country == "Spain" & rel_column == 9 ~ "Just a Christian",
    country == "Spain" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # Sweden
    country == "Sweden" & rel_column == 1 ~ "Lutheran (Church of Sweden)",
    country == "Sweden" & rel_column == 2 ~ "Roman Catholic",
    country == "Sweden" & rel_column == 3 ~ "Congregationalist",
    country == "Sweden" & rel_column == 4 ~ "Pentecostal",
    country == "Sweden" & rel_column == 5 ~ "Orthodox Christian",
    country == "Sweden" & rel_column == 6 ~ "Muslim",
    country == "Sweden" & rel_column == 7 ~ "Atheist",
    country == "Sweden" & rel_column == 8 ~ "Agnostic",
    country == "Sweden" & rel_column == 9 ~ "Something else",
    country == "Sweden" & rel_column == 10 ~ "Nothing in particular",
    country == "Sweden" & rel_column == 11 ~ "Just a Christian",
    country == "Sweden" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    # UK
    country == "UK" & rel_column == 1 ~ "Roman Catholic",
    country == "UK" & rel_column == 2 ~ "Protestant",
    country == "UK" & rel_column == 3 ~ "Muslim",
    country == "UK" & rel_column == 4 ~ "Atheist",
    country == "UK" & rel_column == 5 ~ "Agnostic",
    country == "UK" & rel_column == 6 ~ "Something else",
    country == "UK" & rel_column == 7 ~ "Nothing in particular",
    country == "UK" & rel_column == 8 ~ "Just a Christian",
    country == "UK" & rel_column == 9 ~ "Jewish",
    country == "UK" & rel_column == 10 ~ "Hindu",
    country == "UK" & rel_column == 11 ~ "Buddhist",
    country == "UK" & rel_column == 12 ~ "Sikh",
    country == "UK" & rel_column == 13 ~ "Orthodox Christian",
    country == "UK" & rel_column %in% c(98, 99) ~ "Don't know/Refused",
    
    TRUE ~ "Other"
  )
}


library(dplyr)

# Function to process data for each country and group small categories
process_country_data <- function(data, country_name, religion_var, threshold = 5) {
  data %>%
    filter(country == country_name) %>%
    mutate(standardized_religion = map_religion(country_name, !!sym(religion_var))) %>%
    filter(!standardized_religion %in% c("Just a Christian", "Don't know/Refused", "Something else")) %>%
    group_by(standardized_religion) %>%
    summarize(count = n()) %>%
    mutate(total = sum(count)) %>%
    mutate(pct = count / total * 100) %>%
    # Recode small categories into "All Others"
    mutate(standardized_religion = ifelse(pct < threshold, "All Others", standardized_religion)) %>%
    group_by(standardized_religion) %>%  # Regroup after recoding
    summarize(count = sum(count), pct = sum(count) / total[1] * 100) %>%
    mutate(country = country_name)
}

# List of countries and religion variables
countries <- c("Australia", "Belgium", "Canada", "France", "Germany", 
               "Greece", "Hungary", "Israel", "Italy", "Japan", 
               "Malaysia", "Netherlands", "Poland", "Singapore", 
               "South Korea", "Spain", "Sweden", "UK")

religion_vars <- c("d_relig_australia", "d_relig_belgium", "d_relig_canada", 
                   "d_relig_france", "d_relig_germany", "d_relig_greece", 
                   "d_relig_hungary", "d_relig_israel", "d_relig_italy", 
                   "d_relig_japan", "d_relig_malaysia", "d_relig_netherlands", 
                   "d_relig_poland", "d_relig_singapore", "d_relig_skorea_a", 
                   "d_relig_spain", "d_relig_sweden", "d_relig_uk")

# Initialize an empty list to store results
all_results <- list()

# Loop through each country and religion variable
for (i in seq_along(countries)) {
  country_name <- countries[i]
  religion_var <- religion_vars[i]
  
  # Process data for each country, setting a threshold of 5% for small categories
  result <- process_country_data(gap, country_name, religion_var, threshold = 5)
  
  # Append result to the list
  all_results[[i]] <- result
}

# Combine all results into a single data frame
final_results <- bind_rows(all_results)




final_results <- final_results %>% 
  filter(country != "Hungary") %>% 
  filter(country != "Japan") %>% 
  filter(country != "Malaysia") %>% 
  filter(country != "Sweden") %>% 
  filter(country != "Netherlands")


hungary <- gap %>% 
  filter(country == "Hungary") %>% 
  mutate(rel = d_relig_hungary) %>% 
  mutate(rel = frcode(rel == 1 ~ "Roman Catholic",
                      rel == 2 | rel == 3 ~ "Protestant", 
                      rel == 7 ~ "Atheist", 
                      rel == 8 ~ "Agnostic", 
                      rel == 10 ~ "Nothing in particular", 
                      TRUE ~ "All Others")) %>% 
  ct(rel, wt = weight) %>% 
  rename(standardized_religion = rel) %>% 
  rename(count = n) %>% 
  mutate(pct = pct*100) %>% 
  mutate(country = "Hungary")

japan <- gap %>% 
  filter(country == "Japan") %>% 
  mutate(rel = d_relig_japan) %>% 
  mutate(rel = frcode(rel == 1 ~ "Buddhist",
                      rel == 8 ~ "Nothing in particular", 
                      TRUE ~ "All Others")) %>% 
  ct(rel, wt = weight) %>% 
  rename(standardized_religion = rel) %>% 
  rename(count = n) %>% 
  mutate(pct = pct*100) %>% 
  mutate(country = "Japan")

malaysia <- gap %>% 
  filter(country == "Malaysia") %>% 
  mutate(rel = d_relig_malaysia) %>% 
  mutate(rel = frcode(rel == 1 ~ "Muslim",
                      rel == 2 ~ "Buddhist", 
                      rel == 3 ~ "Hindu", 
                      TRUE ~ "All Others")) %>% 
  ct(rel, wt = weight) %>% 
  rename(standardized_religion = rel) %>% 
  rename(count = n) %>% 
  mutate(pct = pct*100) %>% 
  mutate(country = "Malaysia")

sweden <- gap %>% 
  filter(country == "Sweden") %>% 
  mutate(rel = d_relig_sweden) %>% 
  mutate(rel = frcode(rel == 1 | rel == 3 | rel == 4 ~ "Protestant", 
                      rel == 7 ~ "Atheist", 
                      rel == 8 ~ "Agnostic", 
                      rel == 10 ~ "Nothing in particular", 
                      TRUE ~ "All Others")) %>% 
  ct(rel, wt = weight) %>% 
  rename(standardized_religion = rel) %>% 
  rename(count = n) %>% 
  mutate(pct = pct*100) %>% 
  mutate(country = "Sweden")

neth <- gap %>% 
  filter(country == "Netherlands") %>% 
  mutate(rel = d_relig_netherlands) %>% 
  mutate(rel = frcode(rel == 1 ~ "Roman Catholic", 
                      rel == 2 | rel == 3 ~ "Protestant", 
                      rel == 8 ~ "Atheist",
                      rel == 9 ~ "Agnostic",
                      rel == 11 ~ "Nothing in particular", 
                      TRUE ~ "All Others")) %>% 
  ct(rel, wt = weight) %>% 
  rename(standardized_religion = rel) %>% 
  rename(count = n) %>% 
  mutate(pct = pct*100) %>% 
  mutate(country = "Netherlands")

five <- bind_rows(hungary, japan, malaysia, sweden, neth)

gg <- bind_rows(final_results, five)

gg <- gg %>% 
  mutate(pct = pct/100) 

gg <- gg %>% 
  mutate(rel = as.factor(standardized_religion))

color_mapping_rel <- c(
  "Agnostic" = "#28666e",             # Keeping this one, it's distinct
  "All Others" = "#5f2680",            # Keeping for consistency
  "Atheist" = "#f08080",               # Light Coral
  "Buddhist" = "#ffa500",               # Orange 
  "Hindu" = "#ffd700",                 # Gold
  "Jewish" = "#4b0082",                 # Indigo
  "Muslim" = "#008000",                 # Green
  "Nothing in particular" = "#800080",  # Sienna
  "Orthodox Christian" = "#a0522d",     # Purple
  "Protestant" = "#5f2680",            # Same as All Others
  "Roman Catholic" = "#add8e6"          # Light Blue
)

gg <- gg %>%
  mutate(rel = fct_relevel(rel, 
                           "Roman Catholic", "Protestant", "Orthodox Christian", 
                           "Jewish", "Muslim", "Buddhist", "Hindu", 
                           "Atheist", "Agnostic", "Nothing in particular", "All Others"))


gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(rel))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ country, ncol =1, strip.position = "left") +
  theme_rb() +
  theme(legend.position = "bottom") +
  scale_fill_manual(values = color_mapping_rel) +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 2)) +
  theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct > .05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "black") +
  geom_text(aes(label = ifelse(pct > .05 & rel == "Protestant", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct > .05 & rel == "Jewish", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct > .05 & rel == "Orthodox Christian", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct > .05 & rel == "All Others", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct > .05 & rel == "Nothing in particular", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "The Religious Composition of 18 Countries", caption = "@ryanburge + @religiondata\nData: Global Attitudes Project, Spring 2022") 
save("rel_comp_18_countries.png", wd = 9, ht = 8)


gg <- gap %>% 
  group_by(country) %>% 
  mutate(imp = religion_import) %>% 
  mutate(imp = frcode(imp == 4 ~ "Not at all", 
                      imp == 3 ~ "Not too", 
                      imp == 2 ~ "Somewhat", 
                      imp == 1 ~ "Very important")) %>% 
  ct(imp, wt = weight, show_na = FALSE)

lvl <- gg %>% 
  filter(imp == "Not at all") %>% 
  select(country, sort = pct)


gg <- left_join(gg, lvl)

gg$country <- fct_reorder(gg$country, gg$sort, .desc = TRUE)


lighten_color <- function(color, factor = 0.3) {
  col <- col2rgb(color)
  col <- col + (255 - col) * factor
  rgb(t(col), maxColorValue = 255)
}

# Apply lightened colors to your palette
lightened_colors <- c(
  lighten_color("#033f63", 0.3),
  lighten_color("#28666e", 0.3),
  lighten_color("#7D3C98", 0.3),
  lighten_color("#5f2680", 0.3)
)

distinguishable_colors <- diverging_hcl(4, palette = "Purple-Blue")


gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(imp))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ country, ncol =1, strip.position = "left") +
  scale_fill_manual(values = lightened_colors) +
  theme_rb()  +
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  labs(x = "", y = "", title = "How important is religion in your life?", caption = "@ryanburge + @religiondata\nData: Global Attitudes Project, Spring 2022") 
save("rel_importance.png", wd = 9, ht = 6)


library(dplyr)

# Adding the region variable
gap <- gap %>%
  mutate(region = case_when(
    country %in% c("Australia") ~ "Australia",
    country %in% c("Belgium", "France", "Germany", "Greece", "Hungary", "Italy", "Netherlands", "Poland", "Spain", "Sweden", "UK") ~ "Europe",
    country %in% c("Canada") ~ "Canada",
    country %in% c("Israel") ~ "Israel",
    country %in% c("Japan", "Malaysia", "Singapore", "South Korea") ~ "Asia",
    TRUE ~ "Other"  # If there are other countries, assign them to "Other"
  ))

one <- gap %>% 
  filter(country != "Australia") %>% 
  mutate(age2 = frcode(age >= 18 & age <= 29 ~ "18-29",
                       age >= 30 & age <= 49 ~ "30-49",
                       age >= 50 & age <= 64 ~ "50-64",
                       age >= 65 ~ "65+")) %>% 
  mutate(imp = religion_import) %>% 
  mutate(imp = case_when(imp == 4 ~ 1, 
                         imp == 1 | imp == 2 | imp == 3 ~ 0)) %>% 
  group_by(country, age2) %>% 
  mean_ci(imp, wt = weight, ci = .84) 


two <- gap %>% 
  filter(country == "Australia") %>% 
  mutate(age2 = age_australia) %>% 
  mutate(age2 = frcode(age2 == 1 ~ "18-29", 
                       age2 == 2 ~ "30-49", 
                       age2 == 3 ~ "50-64", 
                       age2 == 4 ~ "65+")) %>% 
  mutate(imp = religion_import) %>% 
  mutate(imp = case_when(imp == 4 ~ 1, 
                         imp == 1 | imp == 2 | imp == 3 ~ 0)) %>% 
  group_by(country, age2) %>% 
  mean_ci(imp, wt = weight, ci = .84) %>% filter(age2 != "NA")

both <- bind_rows(one, two)


color_mapping <- c(
  "Belgium" = "#e1e1e1",
  "Canada" = "#28666e",
  "France" = "#5f2680",
  "Germany" = "#B5B682",
  "Greece" = "#FEDC97",
  "Hungary" = "#7D3C98",
  "Israel" = "#5f2680",
  "Italy" = "#033f63",
  "Japan" = "#28666e",
  "Malaysia" = "#e1e1e1",
  "Netherlands" = "#B5B682",
  "Poland" = "#FEDC97",
  "Singapore" = "#7D3C98",
  "South Korea" = "#5f2680",
  "Spain" = "#033f63",
  "Sweden" = "#28666e",
  "UK" = "#e1e1e1",
  "Australia" = "#B5B682"
)



both %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = reorder(country, mean), y = mean, fill = country)) +
  geom_col(color = "black") +
  facet_wrap(~ age2) +
  coord_flip() +
  scale_fill_manual(values = color_mapping) +
  theme_rb() +
  y_pct() + 
  theme(strip.text = element_text(size = 20)) +
  geom_text(aes(y = mean + .035, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 5, family = "font")  +
  labs(x = "", y = "", title = "Share Saying Religion is Not Important at All by Age and Country", caption = "@ryanburge + @religiondata\nData: Global Attitudes Project, Spring 2022")
save("rel_imp_18_countries_arda.png", ht = 10)
  
library(ggpubr)

both %>% 
  filter(age2 == "18-29" | age2 == "65+") %>% 
  ggplot(., aes(x = age2, y = mean, fill = country)) +
  geom_col(color = 'black') +
  facet_wrap(~ country)

one <- both %>% 
  filter(age2 == "18-29") %>% 
  select(country, young = mean)

two <- both %>% 
  filter(age2 == "65+") %>% 
  select(country, old = mean)

lab <- left_join(one, two) %>% 
  mutate(diff = old - young) %>% 
  mutate(diff = round(diff, 2)) %>% 
  mutate(diff = diff*100) %>% 
  mutate(diff = abs(diff)) %>% 
  mutate(diff = paste0(diff, "%")) %>% 
  select(country, diff)



ttt <- left_join(both, lab)

library(ggpubr)

ttt %>% 
  filter(age2 == "18-29" | age2 == "65+") %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = age2, y = mean, fill = country)) +
  geom_col(color = "black") +
  facet_wrap(~ country) +
  theme_rb() +
  geom_bracket(
    data = ttt, 
    aes(label = ifelse(country %in% c("Belgium", "Israel"), paste0("+", diff), diff)), 
    xmin = 1, xmax = 2, y.position = .675, label.size = 7, size = 1, family = "font"
  ) +
  scale_y_continuous(labels = percent, limits = c(0, .75)) +
  scale_fill_manual(values = color_mapping) +
  geom_text(aes(y = mean + .075, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 9, family = "font")  +
  # geom_text(aes(y = .075, label = ifelse(rel == "Religious", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 9, family = "font", color = "white") +
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "Share Saying Religion is Not Important at All Among the Youngest and Oldest Adults", caption = "@ryanburge + @religiondata\nData: Global Attitudes Project, Spring 2022")
save("compare_imp_young_old.png", wd = 9, ht = 10)


gg <- gap %>% 
  mutate(rel = citizen_religion) %>% 
  mutate(rel = case_when(rel == 4 ~ 1, 
                         rel == 1 | rel == 2 | rel == 3 ~ 0)) %>% 
  group_by(country) %>% 
  socsci::mean_ci(rel, wt = weight, ci = .84)



color_mapping <- c(
  "Belgium" = "#e1e1e1",
  "Canada" = "#28666e",
  "France" = "#5f2680",
  "Germany" = "#B5B682",
  "Greece" = "#FEDC97",
  "Hungary" = "#7D3C98",
  "Israel" = "#5f2680",
  "Italy" = "#033f63",
  "Japan" = "#28666e",
  "Malaysia" = "#e1e1e1",
  "Netherlands" = "#B5B682",
  "Poland" = "#FEDC97",
  "Singapore" = "#7D3C98",
  "South Korea" = "#5f2680",
  "Spain" = "#033f63",
  "Sweden" = "#28666e",
  "UK" = "#e1e1e1",
  "Australia" = "#B5B682"
)

gg %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = reorder(country, mean), y = mean, fill = country)) +
  geom_col(color = "black") +
  coord_flip() +
  theme_rb() +
  error_bar() + 
  y_pct() + 
  lab_bar(top = FALSE, type = lab, pos = .02, sz = 6) +
  geom_text(aes(y = .02, label = ifelse(country == "Spain", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "France", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "Hungary", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "Israel", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "South Korea", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "Singapore", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "Italy", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "Canada", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "Sweden", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  geom_text(aes(y = .02, label = ifelse(country == "Japan", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white")  + 
  scale_fill_manual(values = color_mapping) +
  theme(plot.title = element_text(size = 14)) +
  labs(x = "", y = "", title = "Share Saying Attending Religious Services is Not at All Important to be a Good Member of Society", caption = "@ryanburge + @religiondata\nData: Global Attitudes Project, Spring 2022")
save("good_member_rel_gap.png")