This is a 'data.table' implementation that mimics the output of
pairwise_count() from 'widyr' to reduce package dependency. This is used
internally within tm_cooc().
pairwise_count(data, id = "line", word = "word")Data frame output from tm_clean().
String to represent the id variable. Defaults to "line".
String to represent the word variable. Defaults to "word".
data frame with the following columns representing a pairwise count:
"item1"
"item2"
"n"
Other Support:
camel_clean(),
check_inputs(),
combine_signals(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
p_test(),
plot_WOE(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
totals_reorder(),
tstamp(),
us_to_space(),
wrap()
Other Text-mining:
meeting_tm_report(),
subject_validate(),
subject_validate_report(),
tm_clean(),
tm_cooc(),
tm_freq(),
tm_wordcloud()
td <- data.frame(line = c(1, 1, 2, 2),
word = c("work", "meeting", "catch", "up"))
pairwise_count(td, id = "line", word = "word")
#> # A tibble: 2 × 3
#> item1 item2 n
#> <chr> <chr> <int>
#> 1 work meeting 1
#> 2 catch up 1