gg-data-all.R

# Here we run linear models on all available data against GDP per capita
  # We are not controlling for country or year
lm_data_all <- data_long %>%
  left_join(., gdp_per_capita[,-5], by = c("ccode", "country.name", "year")) %>% 
  rename(., value = value.x, gdp = value.y) %>% 
  group_by(variable) %>% 
  mutate(n = sum(!is.na(gdp))) %>%
  ungroup() %>% 
  nest(-n, -variable) %>%
  mutate(fit = map(data, ~ lm(value ~ gdp, data = .)),
    results = map(fit, glance)) %>% 
  unnest(results) %>%
  select(n, variable, adj.r.squared, p.value) %>% 
  arrange(-adj.r.squared) %>% 
  filter(variable != "gdp_per_capita")
  
  # These data are best represented with a table
knitr::kable(lm_data_all, digits = 3, caption = "R^2 and p-values for the relationship between several metrics and GDP per capita. Years and country of sampling were not controlled for.")