cnlp_utils_pca

Takes a matrix and returns a data frame with the top principal components
extracted. This is a simple but powerful technique for visualizing a corpus
of documents.

Provides a set of fast tools for converting a textual corpus into
a set of normalized tables. Users may make use of the 'udpipe' back end with
no external dependencies, or a Python back ends with 'spaCy' <https://spacy.io>.
Exposed annotation tasks include tokenization, part of speech tagging, named
entity recognition, and dependency parsing.

Taylor Arnold

cleanNLP

A Tidy Data Model for Natural Language Processing

cnlp_utils_pca function

<dl><dt>x</dt>
<dd>a matrix object to pass to <code>prcomp</code></dd>
<dt>k</dt>
<dd>integer. The number of components to include in the output.</dd>
<dt>center</dt>
<dd>logical. Should the data be centered?</dd>
<dt>scale</dt>
<dd>logical. Should the data be scaled? Note that this will
need to be set to false if any columns in <code>x</code> are
constant if <code>center</code> is also true.</dd></dl>

Arguments

Compute Principal Components and store as a Data Frame — cnlp_utils_pca

<dl>

<dt>x</dt>
<dd>a matrix object to pass to <code>prcomp</code></dd>


<dt>k</dt>
<dd>integer. The number of components to include in the output.</dd>


<dt>center</dt>
<dd>logical. Should the data be centered?</dd>


<dt>scale</dt>
<dd>logical. Should the data be scaled? Note that this will
need to be set to false if any columns in <code>x</code> are
constant if <code>center</code> is also true.</dd>

</dl>

cnlp_utils_pca: Compute Principal Components and store as a Data Frame

Description

Usage

Value

Arguments