Function reference • rainette

Simple clustering

Simple clustering computation and visualisation

rainette(): Corpus clustering based on the Reinert method - Simple clustering

cutree_rainette(): Cut a rainette result tree into groups of documents

rainette_plot(): Generate a clustering description plot from a rainette result

rainette_explor(): Shiny gadget for rainette clustering exploration

Double clustering

Double clustering computation and visualisation

rainette2(): Corpus clustering based on the Reinert method - Double clustering

cutree_rainette2(): Cut a rainette2 result object into groups of documents

rainette2_complete_groups(): Complete groups membership with knn classification

rainette2_plot(): Generate a clustering description plot from a rainette2 result

rainette2_explor(): Shiny gadget for rainette2 clustering exploration

Clustering statistics

Functions used to get clustering statistics

rainette_stats(): Generate cluster keyness statistics from a rainette result

Corpus importation and segmentation

Functions to import or segment a textual corpus

import_corpus_iramuteq(): Import a corpus in Iramuteq format

split_segments(): Split a character string or corpus into segments

merge_segments(): Merges segments according to minimum segment size

Documents-clusters tables

Functions to describe relationships between documents and clusters when clustering on a segmented corpus

clusters_by_doc_table(): Returns the number of segment of each cluster for each source document

docs_by_cluster_table(): Returns, for each cluster, the number of source documents with at least n segments of this cluster

Utility functions

Functions used internally for computation

cluster_tab(): Split a dtm into two clusters with reinert algorithm

cutree(): Cut a tree into groups

order_docs(): return documents indices ordered by CA first axis coordinates

select_features(): Remove features from dtm of each group base don cc_test and tsj

switch_docs(): Switch documents between two groups to maximize chi-square value