```r
frequency <- tidy_tweets %>% 
  group_by(person) %>% 
  count(word, sort = TRUE) %>% 
  left_join(tidy_tweets %>% 
              group_by(person) %>% 
              summarise(total = n())) %>%
  mutate(freq = n/total)

frequency
```

```
## # A tibble: 24,067 x 5
## # Groups:   person [2]
##    person word               n total    freq
##    <chr>  <chr>          <int> <int>   <dbl>
##  1 Julia  @selkie1970      570 74152 0.00769
##  2 Julia  time             557 74152 0.00751
##  3 Julia  @skedman         531 74152 0.00716
##  4 Julia  day              437 74152 0.00589
##  5 Julia  baby             392 74152 0.00529
##  6 David  @hadleywickham   308 20699 0.0149 
##  7 Julia  love             302 74152 0.00407
##  8 Julia  @haleynburke     298 74152 0.00402
##  9 Julia  house            283 74152 0.00382
## 10 Julia  morning          278 74152 0.00375
## # ... with 24,057 more rows
```