WVS_chohort_diversity

library(dplyr)
library(tidyr)
library(ggplot2)
library(forcats)
library(sjlabelled)
library(ggthemes)

w5 <- readRDS("wvs5_2005_2009.rds")
wv5 <- w5 %>% 
  mutate(Country = as.character(as_label(V2)),
         Sex = as_label(V235),
         n.child = as_label(V56),
         Diversity = sjlabelled::as_label(V221),
         Year = as.character(as_label(V260)),
         Country = paste(Country, Year),
         Year.of.Birth = as.vector(V236),
         Age = as.numeric(Year) - Year.of.Birth
         ) %>%
  select(Country, Diversity, Age, n.child, Sex) 

wv5 %>% filter(as.numeric(Diversity) > 5, Age < 90, as.numeric(n.child) > 5,
               Sex != "No answer") %>%
  mutate(children = ifelse(as.numeric(n.child) > 6, "parent", "nonparent"),
         diversity = ifelse(as.numeric(Diversity) > 12, "Strongly supportive", "Not strongly supportive")) %>%
  group_by(Sex, Country, children) %>% 
  summarise(av_age = mean(Age), respondents=n(), 
            diversity_support = 100* sum(diversity == "Strongly supportive") / n()) %>%
  ungroup() %>% arrange(Sex, Country, children) %>% 
  group_by(Sex, Country) %>% 
  summarise(parent_age_diff = av_age[2] - av_age[1], 
            diversity_diff = diversity_support[2] - diversity_support[1]) %>%
  ungroup() %>% 
  ggplot(aes(x=parent_age_diff, y=diversity_diff)) + geom_point() + geom_vline(xintercept=15, alpha=0.2) +
    geom_smooth(method="lm") + theme_tufte() + facet_wrap(~Sex) + scale_colour_viridis_d() + 
    ggtitle("Change in average age given parenthood vs change in strong support for ethnic diversity") +
  geom_hline(yintercept=0, alpha=0.2) + ylab("change in strong ethnic diversity support with parenthood") +
  xlab("difference in mean age between parents and non-parents")