```r
bigram_tf_idf <- bigrams_united %>%
  count(book, bigram) %>%
  bind_tf_idf(bigram, book, n) %>%
  arrange(desc(tf_idf))

bigram_tf_idf
```

```
## # A tibble: 36,217 x 6
##    book                bigram                n     tf   idf tf_idf
##    <fct>               <chr>             <int>  <dbl> <dbl>  <dbl>
##  1 Persuasion          captain wentworth   170 0.0299  1.79 0.0535
##  2 Mansfield Park      sir thomas          287 0.0287  1.79 0.0515
##  3 Mansfield Park      miss crawford       215 0.0215  1.79 0.0386
##  4 Persuasion          lady russell        118 0.0207  1.79 0.0371
##  5 Persuasion          sir walter          113 0.0198  1.79 0.0356
##  6 Emma                miss woodhouse      162 0.0170  1.79 0.0305
##  7 Northanger Abbey    miss tilney          82 0.0159  1.79 0.0286
##  8 Sense & Sensibility colonel brandon     108 0.0150  1.79 0.0269
##  9 Emma                frank churchill     132 0.0139  1.79 0.0248
## 10 Pride & Prejudice   lady catherine      100 0.0138  1.79 0.0247
## # … with 36,207 more rows
```