Get and read event and episode narratives for the events in the figure with episodes with most events

[geanders]

We have the wonderful figure with the top episodes by number of events and how they're spread across events. I think that the episode IDs won't mean much to readers, though (e.g., "133801"), and so it might be more helpful to give each episode a better "label" (e.g., "early January winter storm in the Northeast"), and we could figure these out by reading the narratives.

Let's see if we can pull and print out the episode and narrative info for each storm. I think this code should get us the episode narratives, once you've run the code to define top_episodes:

events_2019 %>% 
  filter(EPISODE_ID %in% 
  top_episodes$EPISODE_ID) %>% 
  select(EPISODE_ID, EPISODE_NARRATIVE) %>% 
  distinct()

Also, I think we have room to add a few more episodes to this plot. In the code where we make the dataframe for it:

top_episodes <- events_2019 %>%
  group_by(EPISODE_ID) %>%
  summarize(n = n()) %>%
  arrange(desc(n)) %>%
  slice(1:10)

maybe let's change the last line to slice(1:15) or slice(1:12)? We could explore and see which creates a plot that's not too large. With a few more episodes, we might pick up some other interesting weather systems. I think if we expand a few, we'll capture one episode that was a heat wave.