ct_selection_untitled-8511D0E9.R

library(janitor)

wvs <- read_csv("E://data/wvs_7.csv") %>% clean_names() 

library(googlesheets4)

codes <- read_sheet("https://docs.google.com/spreadsheets/d/13CPhje432YrYamL6nkWSk-5dowH3_km_SQdmbF2tZvI/edit?usp=sharing")

wvs <- wvs  %>% 
  mutate(num = b_country)

wvs <- left_join(wvs, codes)
process_group <- function(data, question, type) {
  data %>%
    mutate(vv = !!sym(question)) %>%
    mutate(vv = case_when(
      vv == 2 ~ 1,
      vv == 1 | vv == 0 ~ 0
    )) %>%
    group_by(name) %>%
    mean_ci(vv, wt = w_weight, ci = .84) %>%
    mutate(type = type)
}

questions <- c("q94", "q95", "q96", "q97", "q98", "q99", "q100", "q101", "q102", "q103", "q104")
types <- c("Religious", "Recreational", "Art/Music/Educ.", "Labor Union", "Political Party", "Environmental Org.", "Professional Org.", "Charitable Org.", "Consumer Org.", "Mutual Aid", "Women's Group")

results <- map2_dfr(questions, types, ~process_group(wvs, .x, .y))

all <- results %>% filter(name != "NA")

region <- read_sheet("https://docs.google.com/spreadsheets/d/1avVTlFzx5xhT5JK6OS3Gcbi5CrjFnGM_f5zvItddgsQ/edit?usp=sharing")

gg <- left_join(all, region)

graph <- gg %>% 
  filter(type == "Religious") %>% 
  arrange(-mean) %>% as.data.frame()

graph[7,1]   <- "Bolivia"
graph[21, 1] <- "Iran"
graph[39, 1] <- "South Korea"
graph[42, 1] <- "Netherlands"
graph[11, 1] <- "Philippines"
graph[56, 1] <- "Russia"
graph[40, 1] <- "Taiwan"
graph[14, 1] <- "United States"
graph[18, 1] <- "Venezuela"
graph[59, 1] <- "Vietnam"
graph[31, 1] <- "United Kingdom"



graph %>% 
  ggplot(., aes(x= reorder(name, mean),  y=mean)) +
  geom_point(size = 2.5, stroke = 1, shape = 21, fill = "white", aes(color = region)) + 
  geom_segment(aes(x = name, xend = name, y = 0, yend = mean - .0075), show.legend = FALSE) +
  coord_flip() +
  theme_rb() + 
  scale_color_gdocs() +
  scale_y_continuous(labels = percent) +
  labs(x = "", y = "", title = "Share Who Are Active Members in A Church or Religious Organization", caption = "@ryanburge\nData: World Values Survey, Wave 7 (2017-2022)")
save("lollipops_wvs_church.png", ht = 10)


graph <- gg %>% 
  filter(type == "Religious") %>% 
  arrange(-mean) %>% as.data.frame()

imp <- wvs %>% 
  mutate(rel = q6) %>% 
  mutate(rel = case_when(rel == 1 ~ 1, 
                         rel == 2 | rel == 3 | rel == 4 ~ 0)) %>% 
  group_by(name) %>% 
  mean_ci(rel, wt = w_weight)

imp <- imp %>% 
  select(name, imp = mean)

org <- graph %>% 
  select(name, org = mean)

both <- left_join(org, imp)

both <- left_join(both, codes)

both <- left_join(both, region)



both[7,1]   <- "Bolivia"
both[21, 1] <- "Iran"
both[39, 1] <- "South Korea"
both[42, 1] <- "Netherlands"
both[11, 1] <- "Philippines"
both[56, 1] <- "Russia"
both[40, 1] <- "Taiwan"
both[14, 1] <- "United States"
both[18, 1] <- "Venezuela"
both[59, 1] <- "Vietnam"
both[31, 1] <- "United Kingdom"

library(ggrepel)

both %>% 
  ggplot(., aes(x = imp, y = org)) +
  geom_point(aes(color = region), stroke = .75, shape = 21) +
  geom_smooth(se = FALSE, method = lm, color = "black", linewidth = .5, linetype = "twodash") +
  scale_color_gdocs() +
  geom_text_repel(data = both, aes(x = imp, y = org, label = abb), family = "font", size = 4) +
  theme_rb() +
  y_pct() + 
  x_pct() +
  labs(x = "Share Saying Religion is Very Important", y = "Share Who Are Active Members in Religious Group", title = "Relationship Between Religious Importance and Active Membership", caption = "@ryanburge\nData: World Values Survey, Wave 7 (2017-2022)")
save("wvs_scatter_imp_org.png")

aa <- both %>% 
  filter(imp > .5) %>% 
  filter(org < .20) %>% 
  mutate(flag = "a")

count <- wvs %>% 
  group_by(name) %>% 
  mutate(bel = q94) %>% 
  mutate(bel = frcode(q94 == 2 ~ "Active", 
                      q94 == 1 ~ "Inactive", 
                      q94 == 0 ~ "Don't Belong")) %>% 
  ct(bel, wt = w_weight, show_na = FALSE)

graph <- left_join(count, aa) %>% filter(flag == "a")


graph %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(bel))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ name, ncol =1, strip.position = "left") +
  theme_rb() +
  scale_fill_manual(values = c("#19798B", "#E74B47", "#F3D567")) +
  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 = 8, family = "font", color = "black") +
  geom_text(aes(label = ifelse(bel == "Don't Belong", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  theme(plot.title = element_text(size = 14)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "Are a member, an active member, an inactive member or not a member of a church or religious organization", caption = "@ryanburge\nData: World Values Survey, Wave 7 (2017-2022)") 
save("wvs_active_inactive.png", wd = 9, ht = 8)

highest_percentage <- results %>%
  group_by(name) %>% 
  filter(mean == max(mean)) %>%
  select(region = name, type)

highest_percentage %>% write_sheet()

high <- read_sheet("https://docs.google.com/spreadsheets/d/1AYsevwSAF2XUICNq3vOLU3BTNzWRcBAIaj7aktRgtgk/edit?usp=sharing")

world_coordinates <- map_data("world") 

world_coordinates <- left_join(world_coordinates, high) 



ggplot() + 
  geom_map( 
    data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region, fill = type)) +
   theme_rb(legend = TRUE) +
  scale_fill_manual(values = c(moma.colors("Klein", 9))) +
  theme(legend.position = "bottom") +
  labs(x = "", y  = "", caption = "@ryanburge\nData: World Values Survey, Wave 7 (2017-2022)", title = "Group with the Highest Percentage of Active Members") +
  theme(legend.position = "bottom",
        axis.text = element_blank(),  # Hide axis text
        axis.title = element_blank(),  # Hide axis title
        panel.grid = element_blank()) +  # Hide gridlines
  theme(axis.text = element_blank()) 
save("wvs_active_mem_map.png")


last <- all %>% 
  filter(type != "Religious")  %>% 
  group_by(name) %>% 
  summarise(sum = sum(mean)) %>% 
  arrange(-sum)



graph <- gg %>% 
  filter(type == "Religious")  %>% 
  select(name, rel = mean, region)

last <- left_join(last, graph)

last <- left_join(last, codes)



last[7,1]   <- "Bolivia"
last[14, 1] <- "United States"
last[16, 1] <- "Philippines"
last[20, 1] <- "United Kingdom"
last[23, 1] <- "Iran"
last[32, 1] <- "Netherlands"
last[33, 1] <- "Taiwan"
last[37, 1] <- "Venezuela"
last[38, 1] <- "Vietnam"
last[46, 1] <- "South Korea"
last[62, 1] <- "Russia"


last %>% 
  ggplot(., aes(x = rel, y = sum)) +
  geom_point(aes(color = region), stroke = .75, shape = 21) +
  geom_smooth(se = FALSE, method = lm, color = "black", linewidth = .5, linetype = "twodash") +
  scale_color_gdocs() +
  geom_text_repel(data = last, aes(x = rel, y = sum, label = abb), family = "font", size = 4) +
  theme_rb() +
  x_pct() +
  labs(x = "Share Who are an Active Member of a Religious Group", 
       y = "Summed Index of All Group Membership (Excluding Religion)", 
       title = "Does Religious Membership Drive All Group Membership?", 
       caption = "@ryanburge\nData: World Values Survey, Wave 7 (2017-2022)")
save("wvs_scatter_joining.png")