# [en] Introduction to rainette

#### Julien Barnier

#### 2026-01-05

## Corpus preparation

For a description of the Reinert clustering method and its implementation in `rainette`, please see the algorithms description vignette.

### Split corpus into segments

As it doesn’t take into account terms frequencies but only there presence / absence, and as it assigns each document to only one cluster, the Reinert method must be applied to short and “homogeneous” documents. It could be ok if you work on tweets or short answers to a specific question, but with longer documents they must first be split into short textual segments.

You can use the `split_segments()` function for that, and it can be applied directly on a `tm` or `quanteda` corpus. In this article we will apply it to the sample `data_corpus_inaugural` corpus provided by `quanteda`.

    library(quanteda)
    library(rainette)

    ## Split documents into segments
    corpus <- split_segments(data_corpus_inaugural, segment_size = 40)

`split_segments` will split the original texts into smaller chunks, attempting to respect sentences and punctuation when possible. The function takes two arguments :

- `segment_size` : the preferred segment size, in words
- `segment_size_window` : the “window” into which looking for the best segment split, in words. If `NULL`, it is set to `0.4 * segment_size`.

The result of the function is a `quanteda` corpus, which keeps the original corpus metadata and adds an additional `segment_source` variable, which keeps track of which segment belongs to which document.

    corpus

    ## Corpus consisting of 3,641 documents and 5 docvars.
    ## 1789-Washington_1 :
    ## "Fellow-Citizens of the Senate and of the House of Representa..."
    ##
    ## 1789-Washington_2 :
    ## "On the one hand, I was summoned by my Country, whose voice I..."
    ##
    ## 1789-Washington_3 :
    ## "as the asylum of my declining years - a retreat which was re..."
    ##
    ## 1789-Washington_4 :
    ## "On the other hand, the magnitude and difficulty of the trust..."
    ##
    ## 1789-Washington_5 :
    ## "could not but overwhelm with despondence one who (inheriting..."
    ##
    ## 1789-Washington_6 :
    ## "In this conflict of emotions all I dare aver is that it has ..."
    ##
    ## [ reached max_ndoc ... 3,635 more documents ]

    head(docvars(corpus))

    ##   Year  President FirstName Party  segment_source
    ## 1 1789 Washington    George  none 1789-Washington
    ## 2 1789 Washington    George  none 1789-Washington
    ## 3 1789 Washington    George  none 1789-Washington
    ## 4 1789 Washington    George  none 1789-Washington
    ## 5 1789 Washington    George  none 1789-Washington
    ## 6 1789 Washington    George  none 1789-Washington

### dfm computation

The next step is to compute the document-feature matrix. As our `corpus` object is a `quanteda` corpus, we can tokenize it and then use the `dfm()` function.

    tok <- tokens(corpus, remove_punct = TRUE)
    tok <- tokens_tolower(tok)
    tok <- tokens_remove(tok, stopwords("en"))
    dtm <- dfm(tok)

We also filter out terms that appear in less than 10 segments by using `dfm_trim`.

    dtm <- dfm_trim(dtm, min_docfreq = 10)

## Simple clustering

We are now ready to compute a simple Reinert clustering by using the `rainette()` function. Its main arguments are :

- `k` : the number of clusters to compute.
- `min_segment_size` : the minimum number of terms in each segment. If a segment contains less than this number of terms, it will be merged with the following or previous one if they come from the same source document. The default value is 0, ie no merging is done.
- `min_split_members` : if a cluster is smaller than this value, it won’t be split afterwards (default : 5).

Here we will compute 5 clusters with a `min_segment_size` of 15 :

    res <- rainette(dtm, k = 5, min_segment_size = 15)

To help exploring the clustering results, `rainette` provides an interactive interface which can be launched with `rainette_explor()` :

    rainette_explor(res, dtm, corpus)

rainette_explor() interface

The interface allows to change the number of clusters, the displayed statistic, etc., and see the result in real time. By default the most specific terms are displayed with a blue bar or a red one for those with a negative keyness (if Show negative values has been checked).

You can also click on the Get R code button to get the R code to reproduce the current plot and to compute cluster membership.

The Cluster documents tab allows to browse the documents from a given cluster. You can filter them by giving a term or a regular expression in the Filter by term field :

rainette_explor() cluster documents tab

You can use `cutree` to get each document cluster at level `k` :

    cluster <- cutree(res, k = 5)

This vector can be used, for example, as a new corpus metadata variable :

    corpus$cluster <- cutree(res, k = 5)
    head(docvars(corpus))

    ##   Year  President FirstName Party  segment_source cluster
    ## 1 1789 Washington    George  none 1789-Washington       4
    ## 2 1789 Washington    George  none 1789-Washington       4
    ## 3 1789 Washington    George  none 1789-Washington       5
    ## 4 1789 Washington    George  none 1789-Washington       5
    ## 5 1789 Washington    George  none 1789-Washington       3
    ## 6 1789 Washington    George  none 1789-Washington       3

Here, the clusters have been assigned to the segments, not to the original documents as a whole. The `clusters_by_doc_table` allows to display, for each original document, the number of segment belonging to each cluster :

    clusters_by_doc_table(corpus, clust_var = "cluster")

    ## # A tibble: 60 × 6
    ##    doc_id          clust_1 clust_2 clust_3 clust_4 clust_5
    ##    <chr>             <int>   <int>   <int>   <int>   <int>
    ##  1 1789-Washington       0       0       6      29       2
    ##  2 1793-Washington       0       0       4       0       0
    ##  3 1797-Adams            0       8       2      46       3
    ##  4 1801-Jefferson        0      20       2      20       2
    ##  5 1805-Jefferson        0       3       3      45       6
    ##  6 1809-Madison          0       0       7      17       4
    ##  7 1813-Madison          0       5       3      25       0
    ##  8 1817-Monroe           0       3      10      61      14
    ##  9 1821-Monroe           0       0      10      77      28
    ## 10 1825-Adams            0       8       8      53       8
    ## # ℹ 50 more rows

By adding `prop = TRUE`, the same table is displayed with row percentages :

    clusters_by_doc_table(corpus, clust_var = "cluster", prop = TRUE)

    ## # A tibble: 60 × 6
    ##    doc_id          clust_1 clust_2 clust_3 clust_4 clust_5
    ##    <chr>             <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    ##  1 1789-Washington       0    0      16.2     78.4    5.41
    ##  2 1793-Washington       0    0     100        0      0
    ##  3 1797-Adams            0   13.6     3.39    78.0    5.08
    ##  4 1801-Jefferson        0   45.5     4.55    45.5    4.55
    ##  5 1805-Jefferson        0    5.26    5.26    78.9   10.5
    ##  6 1809-Madison          0    0      25       60.7   14.3
    ##  7 1813-Madison          0   15.2     9.09    75.8    0
    ##  8 1817-Monroe           0    3.41   11.4     69.3   15.9
    ##  9 1821-Monroe           0    0       8.70    67.0   24.3
    ## 10 1825-Adams            0   10.4    10.4     68.8   10.4
    ## # ℹ 50 more rows

Conversely, the `docs_by_cluster_table` allows to display, for each cluster, the number and proportion of original document including at least one segment of this cluster :

    docs_by_cluster_table(corpus, clust_var = "cluster")

    ## # A tibble: 5 × 3
    ##   cluster     n   `%`
    ##   <chr>   <int> <dbl>
    ## 1 clust_1    13  21.7
    ## 2 clust_2    54  90
    ## 3 clust_3    32  53.3
    ## 4 clust_4    45  75
    ## 5 clust_5    37  61.7

## Double clustering

`rainette` also provides a “double clustering” algorithm, as described in the algorithms description vignette : two simple clusterings are computed with different `min_segment_size` values, and then crossed together to get more robust clusters.

This can be done with the `rainette2()` function, which can be applied to two already computed simple clusterings. Here, we compute them with `min_segment_size` at 10 and 15.

    res1 <- rainette(dtm, k = 5, min_segment_size = 10)
    res2 <- rainette(dtm, k = 5, min_segment_size = 15)

We then use `rainette2()` to combine them. The `max_k` argument is used to specify the maximum number of clusters.

    res <- rainette2(res1, res2, max_k = 5)

One important argument of `rainette2()` is the `full` argument :

- If `full = TRUE` (the default), the best crossed clusters partition selection is made by keeping all non empty crossed clusters. This allows an exhaustive search and to identify the partition either with the highest sum of association χ², or the one with the highest total size. However, computation times can rise rapidly with the number of clusters.
- If `full = FALSE`, only the crossed clusters whose clusters are the most mutually associated are kept. Computations are much faster, but the highest k level with available partitions may be lower, and only the partitions with the highest association χ² values can be identified.

To be noted : with `full = TRUE`, if runtime is too high you can add the `parallel = TRUE` argument to paralellise some computations (won’t work on Windows, though, and may use much more RAM).

In both cases, the resulting object is a tibble with, for each level k, the optimal partitions and their characteristics. Another interactive interface is available to explore the results. It is launched with `rainette2_explor()`.

    rainette2_explor(res, dtm, corpus)

rainette2_explor() interface

The interface is very similar to the previous one, except there is no dendrogram anymore, but a single barplot of cluster sizes instead. Be careful of the number of `NA` (not assigned segments), as it can be quite high.

If some points are not assigned to any cluster, you can use `rainette2_complete_groups()` to assign them to the nearest one by using a k-nearest-neighbors algorithm (with k=1). However this may not be recommended as you would then loose the “robustness” of the new clusters computed by `rainette2()`.

    clusters <- cutree(res, k = 5)
    clusters_completed <- rainette2_complete_groups(dtm, clusters)