Concept Network- Compare and plot Concept Network

This function takes a string of terms (separated by commas) or a single term and, using `textrank_keywords()` from `textrank` package, filters data based on `pos_filter` and finds words connected to search terms for each group. Then it plots a Concept Network for each group based on the calculated weights of these terms and the frequency of co-occurrences, indicating any words that are unique to each group's Network plot.

Usage

fst_concept_network_compare(
  data,
  concepts,
  field,
  norm = NULL,
  threshold = NULL,
  pos_filter = NULL,
  use_svydesign_field = FALSE,
  id = "",
  svydesign = NULL,
  exclude_nulls = FALSE,
  rename_nulls = "null_data",
  title_size = 20,
  subtitle_size = 15
)

Arguments

data

A dataframe of text in CoNLL-U format with additional `field` column for splitting data.

concepts

List of terms to search for, separated by commas.

field

Column in `data` used for splitting groups

norm

The method for normalising the data. Valid settings are `"number_words"` (the number of words in the responses, default), `"number_resp"` (the number of responses), or `NULL` (raw count returned).

threshold

A minimum number of occurrences threshold for 'edge' between searched term and other word, default is `NULL`. Note, the threshold is applied before normalisation.

pos_filter

List of UPOS tags for inclusion, default is `NULL` to include all UPOS tags.

use_svydesign_field

Option to get `field` for splitting the data from a svydesign object, default is `FALSE`

id

ID column from raw data, required if `use_svydesign_weights = TRUE` and must match the `docid` in formatted `data`.

svydesign

A svydesign object which contains the raw data and weights.

exclude_nulls

Whether to include NULLs in `field` column, default is `FALSE`

rename_nulls

What to fill NULL values with if `exclude_nulls = FALSE`.

title_size

size to display plot title

subtitle_size

size to display title of individual concept network

Value

Multiple concept network plots with concept and unique words highlighted.

Examples

con1 <- "lyödä, lyöminen"
fst_concept_network_compare(fst_child, concepts = con1, field = 'gender')

s <- survey::svydesign(id=~1, weights= ~paino, data = child)
c2 <- fst_child_2
i <- 'fsd_id'
fst_concept_network_compare(c2, con1, 'gender', NULL, NULL, NULL, TRUE, i, s)

con2 <- "köyhyys, nälänhätä, sota"
fst_concept_network_compare(fst_dev_coop, con2, 'gender')