gg-data-year.R

# Or we may control for years of sampling
  # This allows us to see if the relationship changes much over time
lm_data_year <- data_long %>% 
  left_join(., gdp_per_capita[,-5], by = c("ccode", "country.name", "year")) %>% 
  rename(., value = value.x, gdp = value.y) %>% 
  group_by(year, variable) %>% 
  mutate(n = sum(!is.na(gdp))) %>%
  ungroup() %>% 
  filter(n > 10) %>% # Require at least 10 data points 
  nest(-n, -year, -variable) %>%
  mutate(fit = map(data, ~ lm(value ~ gdp, data = .)),
    results = map(fit, glance)) %>% 
  unnest(results) %>%
  select(n, variable, year,  adj.r.squared, p.value) %>% 
  arrange(-adj.r.squared) %>% 
  filter(variable != "gdp_per_capita")

# And now for a time series
ggplot(data = lm_data_year, aes(x = year, y = adj.r.squared)) +
  geom_line(aes(colour = variable)) +
  labs(y = "R^2")