```r
word_by_rts <- tidy_tweets %>% 
  group_by(id, word, person) %>% 
  summarise(rts = first(retweets)) %>% 
  group_by(person, word) %>% 
  summarise(retweets = median(rts), uses = n()) %>%
  left_join(totals) %>%
  filter(retweets != 0) %>%
  ungroup()

word_by_rts %>% 
  filter(uses >= 5) %>%
  arrange(desc(retweets))
```

```
## # A tibble: 170 x 5
##    person word          retweets  uses total_rts
##    <chr>  <chr>            <dbl> <int>     <dbl>
##  1 David  animation           85     5     13014
##  2 David  gganimate           75     6     13014
##  3 David  error               56     7     13014
##  4 David  start               56     6     13014
##  5 David  download            52     5     13014
##  6 Julia  tidytext            50     7      1750
##  7 David  introducing         45     6     13014
##  8 David  understanding       37     6     13014
##  9 David  ab                  36     5     13014
## 10 David  bayesian            34     7     13014
## # … with 160 more rows
```