[en] Introduction to rainette

Corpus preparation

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

library(quanteda)
library(rainette)

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

corpus

## Corpus consisting of 3,584 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 ... 3,578 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

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

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       3
## 4 1789 Washington    George  none 1789-Washington       3
## 5 1789 Washington    George  none 1789-Washington       5
## 6 1789 Washington    George  none 1789-Washington       5

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: 59 × 6
##    doc_id          clust_1 clust_2 clust_3 clust_4 clust_5
##    <chr>             <int>   <int>   <int>   <int>   <int>
##  1 1789-Washington       0       0       4      27       6
##  2 1793-Washington       0       0       0       0       4
##  3 1797-Adams            8       0       5      44       2
##  4 1801-Jefferson       18       0       3      21       2
##  5 1805-Jefferson        2       3       6      43       3
##  6 1809-Madison          0       0       4      17       7
##  7 1813-Madison          0       5       5      20       3
##  8 1817-Monroe           2       0      27      47      12
##  9 1821-Monroe           2       0      37      66      10
## 10 1825-Adams            8       2      13      46       8
## # … with 49 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: 59 × 6
##    doc_id          clust_1 clust_2 clust_3 clust_4 clust_5
##    <chr>             <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 1789-Washington    0       0      10.8     73.0   16.2 
##  2 1793-Washington    0       0       0        0    100   
##  3 1797-Adams        13.6     0       8.47    74.6    3.39
##  4 1801-Jefferson    40.9     0       6.82    47.7    4.55
##  5 1805-Jefferson     3.51    5.26   10.5     75.4    5.26
##  6 1809-Madison       0       0      14.3     60.7   25   
##  7 1813-Madison       0      15.2    15.2     60.6    9.09
##  8 1817-Monroe        2.27    0      30.7     53.4   13.6 
##  9 1821-Monroe        1.74    0      32.2     57.4    8.70
## 10 1825-Adams        10.4     2.60   16.9     59.7   10.4 
## # … with 49 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    52  88.1
## 2 clust_2    40  67.8
## 3 clust_3    40  67.8
## 4 clust_4    44  74.6
## 5 clust_5    34  57.6

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)