tidy_fit.r

fit <- num_df %>% 
  map(scale) %>%
  as.data.frame() %>% 
  bootstrap(10) %>% 
  do(tidy(lm(imdb_score ~., data = .), conf.int = TRUE)) %>% 
  mutate(sig_0005 = p.value < 0.005)

fit %>% 
  filter(term != "(Intercept)") %>% 
  group_by(term) %>% 
  ggplot(aes(reorder(term, estimate), estimate, colour = sig_0005)) + 
  geom_point(alpha = 0.4) +
  coord_flip() +
  theme_minimal() +
  geom_hline(aes(yintercept = 0), linetype = "dashed", colour = "blue") +
  labs(title = "Linear regression estimates", 
       subtitle = "Dependent variable: IMDB score", 
       x = "Independent variable", 
       y = "estimate")