ParseR combines functionality from the tidytext package and the tidygraph package to enable users to create network visualisations of common terms in a data set.
We’ll play through an example by creating a bi-gram network from a sample of the data set included in the ParseR package.
# Generate a sample
set.seed(1)
example <- ParseR::sprinklr_export %>%
dplyr::sample_n(1000)Count the n-grams
Each post will be broken down into bi-grams (i.e. pairs of words) and the 25 most frequent bi-grams will be returned.
counts <- example %>%
ParseR::count_ngram(text_var = Message, n = 2, top_n = 25)Note that counts is a list object:
class(counts)## [1] "list"It contains two objects:
- “view”
- A human-readable tibble with the most common n-grams.
counts %>%
purrr::pluck("view")## # A tibble: 20 × 3
## word1 word2 ngram_freq
## <chr> <chr> <int>
## 1 hispanic heritage 503
## 2 heritage month 253
## 3 heritage celebration 39
## 4 celebrating hispanic 37
## 5 national hispanic 33
## 6 beto orourke 32
## 7 bobby beto 30
## 8 hispanic caucus 30
## 9 lacks hispanic 30
## 10 caucus refuses 29
## 11 orourke membership 29
## 12 refuses bobby 29
## 13 flashback hispanic 27
## 14 heritage festival 25
## 15 celebrate hispanic 20
## 16 heritage night 18
## 17 month celebration 14
## 18 annual hispanic 13
## 19 heritage day 13
## 20 celebrates hispanic 10- “viz”
- A
tbl_graphobject that can be used to produce a network visualisation.
counts %>%
purrr::pluck("viz")## # A tbl_graph: 20 nodes and 20 edges
## #
## # A directed acyclic simple graph with 1 component
## #
## # Node Data: 20 × 2 (active)
## word word_freq
## <chr> <int>
## 1 hispanic 638
## 2 heritage 531
## 3 month 275
## 4 day 107
## 5 celebration 96
## 6 celebrating 81
## # … with 14 more rows
## #
## # Edge Data: 20 × 3
## from to ngram_freq
## <int> <int> <int>
## 1 1 2 503
## 2 2 3 253
## 3 2 5 39
## # … with 17 more rows