| augment.CTM | Tidiers for LDA and CTM objects from the topicmodels package |
| augment.jobjRef | Tidiers for Latent Dirichlet Allocation models from the mallet package |
| augment.LDA | Tidiers for LDA and CTM objects from the topicmodels package |
| augment.STM | Tidiers for Structural Topic Models from the stm package |
| bind_tf_idf | Bind the term frequency and inverse document frequency of a tidy text dataset to the dataset |
| cast_dfm | Casting a data frame to a DocumentTermMatrix, TermDocumentMatrix, or dfm |
| cast_dtm | Casting a data frame to a DocumentTermMatrix, TermDocumentMatrix, or dfm |
| cast_sparse | Create a sparse matrix from row names, column names, and values in a table. |
| cast_tdm | Casting a data frame to a DocumentTermMatrix, TermDocumentMatrix, or dfm |
| corpus_tidiers | Tidiers for a corpus object from the quanteda package |
| dictionary_tidiers | Tidy dictionary objects from the quanteda package |
| get_sentiments | Get a tidy data frame of a single sentiment lexicon |
| get_stopwords | Get a tidy data frame of a single stopword lexicon |
| glance.corpus | Tidiers for a corpus object from the quanteda package |
| glance.CTM | Tidiers for LDA and CTM objects from the topicmodels package |
| glance.estimateEffect | Tidiers for Structural Topic Models from the stm package |
| glance.LDA | Tidiers for LDA and CTM objects from the topicmodels package |
| glance.STM | Tidiers for Structural Topic Models from the stm package |
| lda_tidiers | Tidiers for LDA and CTM objects from the topicmodels package |
| mallet_tidiers | Tidiers for Latent Dirichlet Allocation models from the mallet package |
| nma_words | English negators, modals, and adverbs |
| parts_of_speech | Parts of speech for English words from the Moby Project |
| reorder_func | Reorder an x or y axis within facets |
| reorder_within | Reorder an x or y axis within facets |
| scale_x_reordered | Reorder an x or y axis within facets |
| scale_y_reordered | Reorder an x or y axis within facets |
| sentiments | Sentiment lexicon from Bing Liu and collaborators |
| stm_tidiers | Tidiers for Structural Topic Models from the stm package |
| stop_words | Various lexicons for English stop words |
| tdm_tidiers | Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package |
| tidy.Corpus | Tidy a Corpus object from the tm package |
| tidy.corpus | Tidiers for a corpus object from the quanteda package |
| tidy.CTM | Tidiers for LDA and CTM objects from the topicmodels package |
| tidy.dfm | Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package |
| tidy.dfmSparse | Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package |
| tidy.dictionary2 | Tidy dictionary objects from the quanteda package |
| tidy.DocumentTermMatrix | Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package |
| tidy.estimateEffect | Tidiers for Structural Topic Models from the stm package |
| tidy.jobjRef | Tidiers for Latent Dirichlet Allocation models from the mallet package |
| tidy.LDA | Tidiers for LDA and CTM objects from the topicmodels package |
| tidy.simple_triplet_matrix | Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package |
| tidy.STM | Tidiers for Structural Topic Models from the stm package |
| tidy.TermDocumentMatrix | Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package |
| tidy_triplet | Utility function to tidy a simple triplet matrix |
| unnest_characters | Wrapper around unnest_tokens for characters and character shingles |
| unnest_character_shingles | Wrapper around unnest_tokens for characters and character shingles |
| unnest_lines | Wrapper around unnest_tokens for sentences, lines, and paragraphs |
| unnest_ngrams | Wrapper around unnest_tokens for n-grams |
| unnest_paragraphs | Wrapper around unnest_tokens for sentences, lines, and paragraphs |
| unnest_ptb | Wrapper around unnest_tokens for Penn Treebank Tokenizer |
| unnest_regex | Wrapper around unnest_tokens for regular expressions |
| unnest_sentences | Wrapper around unnest_tokens for sentences, lines, and paragraphs |
| unnest_skip_ngrams | Wrapper around unnest_tokens for n-grams |
| unnest_tokens | Split a column into tokens |