```r
library(igraph)

# original counts
bigram_counts
```

```
## # A tibble: 33,421 x 3
##    word1   word2         n
##    <chr>   <chr>     <int>
##  1 sir     thomas      287
##  2 miss    crawford    215
##  3 captain wentworth   170
##  4 miss    woodhouse   162
##  5 frank   churchill   132
##  6 lady    russell     118
##  7 lady    bertram     114
##  8 sir     walter      113
##  9 miss    fairfax     109
## 10 colonel brandon     108
## # … with 33,411 more rows
```

```r
# filter for only relatively common combinations
bigram_graph <- bigram_counts %>%
  filter(n > 20) %>%
  graph_from_data_frame()

bigram_graph
```

```
## IGRAPH 98364d7 DN-- 91 77 -- 
## + attr: name (v/c), n (e/n)
## + edges from 98364d7 (vertex names):
##  [1] sir     ->thomas     miss    ->crawford   captain ->wentworth  miss    ->woodhouse 
##  [5] frank   ->churchill  lady    ->russell    lady    ->bertram    sir     ->walter    
##  [9] miss    ->fairfax    colonel ->brandon    miss    ->bates      lady    ->catherine 
## [13] sir     ->john       jane    ->fairfax    miss    ->tilney     lady    ->middleton 
## [17] miss    ->bingley    thousand->pounds     miss    ->dashwood   miss    ->bennet    
## [21] john    ->knightley  miss    ->morland    captain ->benwick    dear    ->miss      
## [25] miss    ->smith      miss    ->crawford's henry   ->crawford   miss    ->elliot    
## [29] dr      ->grant      miss    ->bertram    sir     ->thomas's   ten     ->minutes   
## + ... omitted several edges
```