Topics will be renamed in order of the input of new_names, which should be a character vector. For example, if you had topic_1 and topic_2, and you wanted to rename them 'sustainability' and 'climate_change', new_names should be input as new_names = c("sustainability", "climate_change"). This will mean that when plotting, the order is preserved. If you want to re-order the topics after renaming them, you should use `forcats::fct_reorder()` separately, or `mutate` & `factor()`
Arguments
- classified_df
output of the `topics_classify` function
- k
Value of k used to generate topic model
- new_names
Desired new names in order of k
- topic_var
The topic variable in your classified df
Examples
list_data <- SegmentR:::test_data()
#> removing stopwords
#> Making DTMs
#> making tuning grid
#> setting up LDAs
probabilities <- list_data$explore$probabilities[[1]]
data <- list_data$lda$data[[1]]
linked <- topics_link(data, probabilities)
classified <- topics_classify(linked, 0.75)
topics_rename(classified, k = 3, new_names = c("Name 1", "Name 2", "Name 3"))
#> # A tibble: 7 × 6
#> rowid message url_var message_id topic topic_probability
#> <dbl> <chr> <chr> <chr> <fct> <dbl>
#> 1 20 #DiaDeLosMuertos #DayOfTheDe… https:… 20 Name… 0.843
#> 2 21 #DiaDeLosMuertos #DayOfTheDe… https:… 21 Name… 0.784
#> 3 69 Fun ones #HispanicHeritageMo… https:… 69 Name… 0.905
#> 4 80 Wake Up Texas! #VETOBETO. Be… https:… 80 Name… 0.792
#> 5 87 #halloween #cna #cnasofinsta… https:… 87 Name… 0.771
#> 6 3 Pura Belpré Award Winners 19… https:… 3 Name… 0.917
#> 7 49 Celebrating Hispanic Heritag… https:… 49 Name… 0.778