PAIRWISE_GRIMMER_PATH <- here("analyses/15_validate_distance/grimmer_pairwise_distances.csv")
pairwise_tib <- read_csv(PAIRWISE_GRIMMER_PATH)

Here are the words I explored:

unique(pairwise_tib$word)
##  [1] "announces"      "city"           "department"     "important"     
##  [5] "jobs"           "need"           "support"        "taxes"         
##  [9] "administration" "help"           "bill"           "tax"           
## [13] "america"        "safe"           "act"            "fight"         
## [17] "message"        "law"            "agency"         "workers"       
## [21] "work"           "resources"      "federal"        "growth"        
## [25] "stimulating"    "school"         "announcement"   "investment"    
## [29] "project"        "limit"          "dollars"        "effort"        
## [33] "cut"            "action"         "country"        "water"         
## [37] "authority"      "give"           "control"        "director"      
## [41] "crisis"         "urgent"         "suffer"         "human"         
## [45] "citizenship"    "measure"        "debate"         "committee"     
## [49] "today"          "program"        "funding"        "health"        
## [53] "security"       "people"         "children"       "service"       
## [57] "local"          "education"      "plan"           "report"
pairwise_tib %>%
  group_by(word, angle) %>%
  slice(1:50) %>%
  count(word, angle) %>%
  ggplot(aes(x = angle, y = n, fill = word)) +
  geom_bar(stat = "identity") +
  facet_wrap(~word, scales = "free" ) + 
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  scale_x_continuous( breaks = c(0,30, 45, 60, 90))

pairwise_tib %>%
  group_by(word, angle) %>%
  slice(1:50) %>%
  arrange(word, angle) %>%
  DT::datatable()