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<h1 class="title toc-ignore">Converting to and from Document-Term Matrix
and Corpus objects</h1>
<h4 class="author">Julia Silge and David Robinson</h4>
<h4 class="date">2022-08-19</h4>

<div id="tidying-document-term-matrices" class="section level3">
<h3>Tidying document-term matrices</h3>
Many existing text mining datasets are in the form of a
<code>DocumentTermMatrix</code> class (from the tm package). For
example, consider the corpus of 2246 Associated Press articles from the
topicmodels package:
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>library(tm)
<a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>data(&quot;AssociatedPress&quot;, package = &quot;topicmodels&quot;)
<a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>AssociatedPress</code></pre></div>
<pre><code>## &lt;&lt;DocumentTermMatrix (documents: 2246, terms: 10473)&gt;&gt;
## Non-/sparse entries: 302031/23220327
## Sparsity : 99%
## Maximal term length: 18
## Weighting : term frequency (tf)</code></pre>
If we want to analyze this with tidy tools, we need to turn it into a
one-term-per-document-per-row data frame first. The <code>tidy</code>
function does this. (For more on the tidy verb, <a href="https://github.com/dgrtwo/broom">see the broom package</a>).
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>library(dplyr)
<a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>library(tidytext)
<a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>
<a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>ap_td &lt;- tidy(AssociatedPress)</code></pre></div>
Just as shown in <a href="tidytext.html">this vignette</a>, having
the text in this format is convenient for analysis with the tidytext
package. For example, you can perform sentiment analysis on these
newspaper articles.
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>ap_sentiments &lt;- ap_td %&gt;%
<a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> inner_join(get_sentiments(&quot;bing&quot;), by = c(term = &quot;word&quot;))
<a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>
<a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>ap_sentiments</code></pre></div>
<pre><code>## # A tibble: 30,094 × 4
## document term count sentiment
## &lt;int&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; 
## 1 1 assault 1 negative 
## 2 1 complex 1 negative 
## 3 1 death 1 negative 
## 4 1 died 1 negative 
## 5 1 good 2 positive 
## 6 1 illness 1 negative 
## 7 1 killed 2 negative 
## 8 1 like 2 positive 
## 9 1 liked 1 positive 
## 10 1 miracle 1 positive 
## # … with 30,084 more rows</code></pre>
We can find the most negative documents:
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>library(tidyr)
<a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>
<a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>ap_sentiments %&gt;%
<a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> count(document, sentiment, wt = count) %&gt;%
<a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> spread(sentiment, n, fill = 0) %&gt;%
<a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> mutate(sentiment = positive - negative) %&gt;%
<a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> arrange(sentiment)</code></pre></div>
<pre><code>## # A tibble: 2,190 × 4
## document negative positive sentiment
## &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
## 1 1251 54 6 -48
## 2 1380 53 5 -48
## 3 531 51 9 -42
## 4 43 45 11 -34
## 5 1263 44 10 -34
## 6 2178 40 6 -34
## 7 334 45 12 -33
## 8 1664 38 5 -33
## 9 2147 47 14 -33
## 10 516 38 6 -32
## # … with 2,180 more rows</code></pre>
Or visualize which words contributed to positive and negative
sentiment:
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>library(ggplot2)
<a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>
<a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>ap_sentiments %&gt;%
<a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> count(sentiment, term, wt = count) %&gt;%
<a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> filter(n &gt;= 150) %&gt;%
<a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> mutate(n = ifelse(sentiment == &quot;negative&quot;, -n, n)) %&gt;%
<a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> mutate(term = reorder(term, n)) %&gt;%
<a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> ggplot(aes(term, n, fill = sentiment)) +
<a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> geom_bar(stat = &quot;identity&quot;) +
<a href="#cb8-10" aria-hidden="true" tabindex="-1"></a> theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
<a href="#cb8-11" aria-hidden="true" tabindex="-1"></a> ylab(&quot;Contribution to sentiment&quot;)</code></pre></div>
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" />
Note that a tidier is also available for the <code>dfm</code> class
from the quanteda package:
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>library(methods)
<a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>
<a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>data(&quot;data_corpus_inaugural&quot;, package = &quot;quanteda&quot;)
<a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>d &lt;- quanteda::dfm(data_corpus_inaugural, verbose = FALSE)
<a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>
<a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>d</code></pre></div>
<pre><code>## Document-feature matrix of: 59 documents, 9,439 features (91.84% sparse) and 4 docvars.
## features
## docs fellow-citizens of the senate and house representatives :
## 1789-Washington 1 71 116 1 48 2 2 1
## 1793-Washington 0 11 13 0 2 0 0 1
## 1797-Adams 3 140 163 1 130 0 2 0
## 1801-Jefferson 2 104 130 0 81 0 0 1
## 1805-Jefferson 0 101 143 0 93 0 0 0
## 1809-Madison 1 69 104 0 43 0 0 0
## features
## docs among vicissitudes
## 1789-Washington 1 1
## 1793-Washington 0 0
## 1797-Adams 4 0
## 1801-Jefferson 1 0
## 1805-Jefferson 7 0
## 1809-Madison 0 0
## [ reached max_ndoc ... 53 more documents, reached max_nfeat ... 9,429 more features ]</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>tidy(d)</code></pre></div>
<pre><code>## # A tibble: 45,453 × 3
## document term count
## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt;
## 1 1789-Washington fellow-citizens 1
## 2 1797-Adams fellow-citizens 3
## 3 1801-Jefferson fellow-citizens 2
## 4 1809-Madison fellow-citizens 1
## 5 1813-Madison fellow-citizens 1
## 6 1817-Monroe fellow-citizens 5
## 7 1821-Monroe fellow-citizens 1
## 8 1841-Harrison fellow-citizens 11
## 9 1845-Polk fellow-citizens 1
## 10 1849-Taylor fellow-citizens 1
## # … with 45,443 more rows</code></pre>
</div>
<div id="casting-tidy-text-data-into-a-documenttermmatrix" class="section level3">
<h3>Casting tidy text data into a DocumentTermMatrix</h3>
Some existing text mining tools or algorithms work only on sparse
document-term matrices. Therefore, tidytext provides <code>cast_</code>
verbs for converting from a tidy form to these matrices.
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>ap_td</code></pre></div>
<pre><code>## # A tibble: 302,031 × 3
## document term count
## &lt;int&gt; &lt;chr&gt; &lt;dbl&gt;
## 1 1 adding 1
## 2 1 adult 2
## 3 1 ago 1
## 4 1 alcohol 1
## 5 1 allegedly 1
## 6 1 allen 1
## 7 1 apparently 2
## 8 1 appeared 1
## 9 1 arrested 1
## 10 1 assault 1
## # … with 302,021 more rows</code></pre>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a># cast into a Document-Term Matrix
<a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>ap_td %&gt;%
<a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> cast_dtm(document, term, count)</code></pre></div>
<pre><code>## &lt;&lt;DocumentTermMatrix (documents: 2246, terms: 10473)&gt;&gt;
## Non-/sparse entries: 302031/23220327
## Sparsity : 99%
## Maximal term length: 18
## Weighting : term frequency (tf)</code></pre>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a># cast into a Term-Document Matrix
<a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>ap_td %&gt;%
<a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> cast_tdm(term, document, count)</code></pre></div>
<pre><code>## &lt;&lt;TermDocumentMatrix (terms: 10473, documents: 2246)&gt;&gt;
## Non-/sparse entries: 302031/23220327
## Sparsity : 99%
## Maximal term length: 18
## Weighting : term frequency (tf)</code></pre>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a># cast into quanteda&#39;s dfm
<a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>ap_td %&gt;%
<a href="#cb19-3" aria-hidden="true" tabindex="-1"></a> cast_dfm(term, document, count)</code></pre></div>
<pre><code>## Document-feature matrix of: 10,473 documents, 2,246 features (98.72% sparse) and 0 docvars.
## features
## docs 1 2 3 4 5 6 7 8 9 10
## adding 1 0 0 0 0 0 0 0 0 0
## adult 2 0 0 0 0 0 0 0 0 0
## ago 1 0 1 3 0 2 0 0 0 0
## alcohol 1 0 0 0 0 0 0 0 0 0
## allegedly 1 0 0 0 0 0 0 0 0 0
## allen 1 0 0 0 0 0 0 0 0 0
## [ reached max_ndoc ... 10,467 more documents, reached max_nfeat ... 2,236 more features ]</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a># cast into a Matrix object
<a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>m &lt;- ap_td %&gt;%
<a href="#cb21-3" aria-hidden="true" tabindex="-1"></a> cast_sparse(document, term, count)
<a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>class(m)</code></pre></div>
<pre><code>## [1] &quot;dgCMatrix&quot;
## attr(,&quot;package&quot;)
## [1] &quot;Matrix&quot;</code></pre>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>dim(m)</code></pre></div>
<pre><code>## [1] 2246 10473</code></pre>
This allows for easy reading, filtering, and processing to be done
using dplyr and other tidy tools, after which the data can be converted
into a document-term matrix for machine learning applications.
</div>
<div id="tidying-corpus-data" class="section level3">
<h3>Tidying corpus data</h3>
You can also tidy Corpus objects from the tm package. For example,
consider a Corpus containing 20 documents, one for each
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>reut21578 &lt;- system.file(&quot;texts&quot;, &quot;crude&quot;, package = &quot;tm&quot;)
<a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>reuters &lt;- VCorpus(DirSource(reut21578),
<a href="#cb25-3" aria-hidden="true" tabindex="-1"></a> readerControl = list(reader = readReut21578XMLasPlain))
<a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>
<a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>reuters</code></pre></div>
<pre><code>## &lt;&lt;VCorpus&gt;&gt;
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 20</code></pre>
The <code>tidy</code> verb creates a table with one row per
document:
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>reuters_td &lt;- tidy(reuters)
<a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>reuters_td</code></pre></div>
<pre><code>## # A tibble: 20 × 17
## author datetimestamp descr…¹ heading id langu…² origin topics
## &lt;chr&gt; &lt;dttm&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; 
## 1 &lt;NA&gt; 1987-02-26 17:00:56 &quot;&quot; DIAMON… 127 en Reute… YES 
## 2 BY TED D&#39;AFF… 1987-02-26 17:34:11 &quot;&quot; OPEC M… 144 en Reute… YES 
## 3 &lt;NA&gt; 1987-02-26 18:18:00 &quot;&quot; TEXACO… 191 en Reute… YES 
## 4 &lt;NA&gt; 1987-02-26 18:21:01 &quot;&quot; MARATH… 194 en Reute… YES 
## 5 &lt;NA&gt; 1987-02-26 19:00:57 &quot;&quot; HOUSTO… 211 en Reute… YES 
## 6 &lt;NA&gt; 1987-03-01 03:25:46 &quot;&quot; KUWAIT… 236 en Reute… YES 
## 7 By Jeremy Cl… 1987-03-01 03:39:14 &quot;&quot; INDONE… 237 en Reute… YES 
## 8 &lt;NA&gt; 1987-03-01 05:27:27 &quot;&quot; SAUDI … 242 en Reute… YES 
## 9 &lt;NA&gt; 1987-03-01 08:22:30 &quot;&quot; QATAR … 246 en Reute… YES 
## 10 &lt;NA&gt; 1987-03-01 18:31:44 &quot;&quot; SAUDI … 248 en Reute… YES 
## 11 &lt;NA&gt; 1987-03-02 01:05:49 &quot;&quot; SAUDI … 273 en Reute… YES 
## 12 &lt;NA&gt; 1987-03-02 07:39:23 &quot;&quot; GULF A… 349 en Reute… YES 
## 13 &lt;NA&gt; 1987-03-02 07:43:22 &quot;&quot; SAUDI … 352 en Reute… YES 
## 14 &lt;NA&gt; 1987-03-02 07:43:41 &quot;&quot; KUWAIT… 353 en Reute… YES 
## 15 &lt;NA&gt; 1987-03-02 08:25:42 &quot;&quot; PHILAD… 368 en Reute… YES 
## 16 &lt;NA&gt; 1987-03-02 11:20:05 &quot;&quot; STUDY … 489 en Reute… YES 
## 17 &lt;NA&gt; 1987-03-02 11:28:26 &quot;&quot; STUDY … 502 en Reute… YES 
## 18 &lt;NA&gt; 1987-03-02 12:13:46 &quot;&quot; UNOCAL… 543 en Reute… YES 
## 19 By BERNICE N… 1987-03-02 14:38:34 &quot;&quot; NYMEX … 704 en Reute… YES 
## 20 &lt;NA&gt; 1987-03-02 14:49:06 &quot;&quot; ARGENT… 708 en Reute… YES 
## # … with 9 more variables: lewissplit &lt;chr&gt;, cgisplit &lt;chr&gt;, oldid &lt;chr&gt;,
## # topics_cat &lt;named list&gt;, places &lt;named list&gt;, people &lt;chr&gt;, orgs &lt;chr&gt;,
## # exchanges &lt;chr&gt;, text &lt;chr&gt;, and abbreviated variable names ¹description,
## # ²language</code></pre>
Similarly, you can <code>tidy</code> a <code>corpus</code> object
from the quanteda package:
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>library(quanteda)
<a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>
<a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>data(&quot;data_corpus_inaugural&quot;)
<a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>
<a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>data_corpus_inaugural</code></pre></div>
<pre><code>## Corpus consisting of 59 documents and 4 docvars.
## 1789-Washington :
## &quot;Fellow-Citizens of the Senate and of the House of Representa...&quot;
## 
## 1793-Washington :
## &quot;Fellow citizens, I am again called upon by the voice of my c...&quot;
## 
## 1797-Adams :
## &quot;When it was first perceived, in early times, that no middle ...&quot;
## 
## 1801-Jefferson :
## &quot;Friends and Fellow Citizens: Called upon to undertake the du...&quot;
## 
## 1805-Jefferson :
## &quot;Proceeding, fellow citizens, to that qualification which the...&quot;
## 
## 1809-Madison :
## &quot;Unwilling to depart from examples of the most revered author...&quot;
## 
## [ reached max_ndoc ... 53 more documents ]</code></pre>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>inaug_td &lt;- tidy(data_corpus_inaugural)
<a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>inaug_td</code></pre></div>
<pre><code>## # A tibble: 59 × 5
## text Year Presi…¹ First…² Party
## &lt;chr&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;fct&gt;
## 1 &quot;Fellow-Citizens of the Senate and of the House … 1789 Washin… George none 
## 2 &quot;Fellow citizens, I am again called upon by the … 1793 Washin… George none 
## 3 &quot;When it was first perceived, in early times, th… 1797 Adams John Fede…
## 4 &quot;Friends and Fellow Citizens:\n\nCalled upon to … 1801 Jeffer… Thomas Demo…
## 5 &quot;Proceeding, fellow citizens, to that qualificat… 1805 Jeffer… Thomas Demo…
## 6 &quot;Unwilling to depart from examples of the most r… 1809 Madison James Demo…
## 7 &quot;About to add the solemnity of an oath to the ob… 1813 Madison James Demo…
## 8 &quot;I should be destitute of feeling if I was not d… 1817 Monroe James Demo…
## 9 &quot;Fellow citizens, I shall not attempt to describ… 1821 Monroe James Demo…
## 10 &quot;In compliance with an usage coeval with the exi… 1825 Adams John Q… Demo…
## # … with 49 more rows, and abbreviated variable names ¹President, ²FirstName</code></pre>
This lets us work with tidy tools like <code>unnest_tokens</code> to
analyze the text alongside the metadata.
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>inaug_words &lt;- inaug_td %&gt;%
<a href="#cb33-2" aria-hidden="true" tabindex="-1"></a> unnest_tokens(word, text) %&gt;%
<a href="#cb33-3" aria-hidden="true" tabindex="-1"></a> anti_join(stop_words)
<a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>
<a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>inaug_words</code></pre></div>
<pre><code>## # A tibble: 50,965 × 5
## Year President FirstName Party word 
## &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;fct&gt; &lt;chr&gt; 
## 1 1789 Washington George none fellow 
## 2 1789 Washington George none citizens 
## 3 1789 Washington George none senate 
## 4 1789 Washington George none house 
## 5 1789 Washington George none representatives
## 6 1789 Washington George none vicissitudes 
## 7 1789 Washington George none incident 
## 8 1789 Washington George none life 
## 9 1789 Washington George none event 
## 10 1789 Washington George none filled 
## # … with 50,955 more rows</code></pre>
We could then, for example, see how the appearance of a word changes
over time:
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>inaug_freq &lt;- inaug_words %&gt;%
<a href="#cb35-2" aria-hidden="true" tabindex="-1"></a> count(Year, word) %&gt;%
<a href="#cb35-3" aria-hidden="true" tabindex="-1"></a> complete(Year, word, fill = list(n = 0)) %&gt;%
<a href="#cb35-4" aria-hidden="true" tabindex="-1"></a> group_by(Year) %&gt;%
<a href="#cb35-5" aria-hidden="true" tabindex="-1"></a> mutate(year_total = sum(n),
<a href="#cb35-6" aria-hidden="true" tabindex="-1"></a> percent = n / year_total) %&gt;%
<a href="#cb35-7" aria-hidden="true" tabindex="-1"></a> ungroup()
<a href="#cb35-8" aria-hidden="true" tabindex="-1"></a>
<a href="#cb35-9" aria-hidden="true" tabindex="-1"></a>inaug_freq</code></pre></div>
<pre><code>## # A tibble: 514,834 × 5
## Year word n year_total percent
## &lt;int&gt; &lt;chr&gt; &lt;int&gt; &lt;int&gt; &lt;dbl&gt;
## 1 1789 1 0 529 0 
## 2 1789 1,000 0 529 0 
## 3 1789 100 0 529 0 
## 4 1789 100,000,000 0 529 0 
## 5 1789 108 0 529 0 
## 6 1789 11 0 529 0 
## 7 1789 120,000,000 0 529 0 
## 8 1789 125 0 529 0 
## 9 1789 13 0 529 0 
## 10 1789 14th 1 529 0.00189
## # … with 514,824 more rows</code></pre>
For example, we can use the broom package to perform logistic
regression on each word.
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>library(broom)
<a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>models &lt;- inaug_freq %&gt;%
<a href="#cb37-3" aria-hidden="true" tabindex="-1"></a> group_by(word) %&gt;%
<a href="#cb37-4" aria-hidden="true" tabindex="-1"></a> filter(sum(n) &gt; 50) %&gt;%
<a href="#cb37-5" aria-hidden="true" tabindex="-1"></a> do(tidy(glm(cbind(n, year_total - n) ~ Year, .,
<a href="#cb37-6" aria-hidden="true" tabindex="-1"></a> family = &quot;binomial&quot;))) %&gt;%
<a href="#cb37-7" aria-hidden="true" tabindex="-1"></a> ungroup() %&gt;%
<a href="#cb37-8" aria-hidden="true" tabindex="-1"></a> filter(term == &quot;Year&quot;)
<a href="#cb37-9" aria-hidden="true" tabindex="-1"></a>
<a href="#cb37-10" aria-hidden="true" tabindex="-1"></a>models</code></pre></div>
<pre><code>## # A tibble: 115 × 6
## word term estimate std.error statistic p.value
## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
## 1 act Year 0.00645 0.00207 3.11 1.85e- 3
## 2 action Year 0.00154 0.00186 0.825 4.09e- 1
## 3 administration Year -0.00696 0.00182 -3.83 1.29e- 4
## 4 america Year 0.0202 0.00147 13.7 6.29e-43
## 5 american Year 0.00854 0.00122 6.99 2.71e-12
## 6 americans Year 0.0310 0.00321 9.65 5.01e-22
## 7 authority Year -0.00616 0.00229 -2.69 7.11e- 3
## 8 business Year 0.00271 0.00194 1.40 1.63e- 1
## 9 called Year -0.00158 0.00198 -0.799 4.24e- 1
## 10 century Year 0.0145 0.00231 6.27 3.58e-10
## # … with 105 more rows</code></pre>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>models %&gt;%
<a href="#cb39-2" aria-hidden="true" tabindex="-1"></a> filter(term == &quot;Year&quot;) %&gt;%
<a href="#cb39-3" aria-hidden="true" tabindex="-1"></a> arrange(desc(abs(estimate)))</code></pre></div>
<pre><code>## # A tibble: 115 × 6
## word term estimate std.error statistic p.value
## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
## 1 americans Year 0.0310 0.00321 9.65 5.01e-22
## 2 america Year 0.0202 0.00147 13.7 6.29e-43
## 3 democracy Year 0.0156 0.00223 6.99 2.70e-12
## 4 children Year 0.0149 0.00246 6.06 1.36e- 9
## 5 century Year 0.0145 0.00231 6.27 3.58e-10
## 6 god Year 0.0135 0.00179 7.58 3.36e-14
## 7 live Year 0.0128 0.00232 5.50 3.70e- 8
## 8 powers Year -0.0125 0.00196 -6.38 1.76e-10
## 9 revenue Year -0.0122 0.00250 -4.87 1.11e- 6
## 10 foreign Year -0.0120 0.00191 -6.31 2.73e-10
## # … with 105 more rows</code></pre>
You can show these models as a volcano plot, which compares the
effect size with the significance:
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>library(ggplot2)
<a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>
<a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>models %&gt;%
<a href="#cb41-4" aria-hidden="true" tabindex="-1"></a> mutate(adjusted.p.value = p.adjust(p.value)) %&gt;%
<a href="#cb41-5" aria-hidden="true" tabindex="-1"></a> ggplot(aes(estimate, adjusted.p.value)) +
<a href="#cb41-6" aria-hidden="true" tabindex="-1"></a> geom_point() +
<a href="#cb41-7" aria-hidden="true" tabindex="-1"></a> scale_y_log10() +
<a href="#cb41-8" aria-hidden="true" tabindex="-1"></a> geom_text(aes(label = word), vjust = 1, hjust = 1,
<a href="#cb41-9" aria-hidden="true" tabindex="-1"></a> check_overlap = TRUE) +
<a href="#cb41-10" aria-hidden="true" tabindex="-1"></a> xlab(&quot;Estimated change over time&quot;) +
<a href="#cb41-11" aria-hidden="true" tabindex="-1"></a> ylab(&quot;Adjusted p-value&quot;)</code></pre></div>
<img 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" />
We can also use the ggplot2 package to display the top 6 terms that
have changed in frequency over time.
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>library(scales)
<a href="#cb42-2" aria-hidden="true" tabindex="-1"></a>
<a href="#cb42-3" aria-hidden="true" tabindex="-1"></a>models %&gt;%
<a href="#cb42-4" aria-hidden="true" tabindex="-1"></a> top_n(6, abs(estimate)) %&gt;%
<a href="#cb42-5" aria-hidden="true" tabindex="-1"></a> inner_join(inaug_freq) %&gt;%
<a href="#cb42-6" aria-hidden="true" tabindex="-1"></a> ggplot(aes(Year, percent)) +
<a href="#cb42-7" aria-hidden="true" tabindex="-1"></a> geom_point() +
<a href="#cb42-8" aria-hidden="true" tabindex="-1"></a> geom_smooth() +
<a href="#cb42-9" aria-hidden="true" tabindex="-1"></a> facet_wrap(~ word) +
<a href="#cb42-10" aria-hidden="true" tabindex="-1"></a> scale_y_continuous(labels = percent_format()) +
<a href="#cb42-11" aria-hidden="true" tabindex="-1"></a> ylab(&quot;Frequency of word in speech&quot;)</code></pre></div>
<img 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" 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