tm_clean

This function processes the <code>Subject</code> column in a Meeting Query by applying
tokenisation using<code><a href="/link/tidytext%3A%3Aunnest_tokens()?package=wpa&version=1.9.0" data-mini-rdoc="wpa::tidytext::unnest_tokens()">tidytext::unnest_tokens()</a></code>, and removing any stopwords
supplied in a data frame (using the argument <code>stopwords</code>). This is a
sub-function that feeds into <code>tm_freq()</code>, <code>tm_cooc()</code>, and <code>tm_wordcloud()</code>.
The default is to return a data frame with tokenised counts of words or
ngrams.

Opinionated functions that enable easier and faster
analysis of Viva Insights data. There are three main types of functions in 'wpa':
(i) Standard functions create a 'ggplot' visual or a summary table based on a specific
Viva Insights metric; (2) Report Generation functions generate HTML reports on
a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover
more specific applications (e.g. Subject Line text mining) of Viva Insights data.
This package adheres to 'tidyverse' principles and works well with the pipe syntax.
'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for
simplicity.

Martin Chan

Tools for Analysing and Visualising Viva Insights Data

Carlos Morales

Mark Powers

Ainize Cidoncha

Rosamary Ochoa Vargas

Tannaz Sattari

Lucas Hogner

Jasminder Thind

Simone Liebal

Aleksey Ashikhmin

Ellen Trinklein

Microsoft Corporation 

tm_clean function

<dl><dt>data</dt>
<dd>A Meeting Query dataset in the form of a data frame.</dd>
<dt>token</dt>
<dd>A character vector accepting either <code>"words"</code> or <code>"ngrams"</code>,
determining type of tokenisation to return.</dd>
<dt>stopwords</dt>
<dd>A character vector OR a single-column data frame labelled
<code>'word'</code> containing custom stopwords to remove.</dd>
<dt>...</dt>
<dd>Additional parameters to pass to <code><a href="/link/tidytext%3A%3Aunnest_tokens()?package=wpa&version=1.9.0" data-mini-rdoc="wpa::tidytext::unnest_tokens()">tidytext::unnest_tokens()</a></code>.</dd></dl>

Arguments

This function processes the <code>Subject</code> column in a Meeting Query by applying
tokenisation using<code><a href='https://rdrr.io/pkg/tidytext/man/unnest_tokens.html'>tidytext::unnest_tokens()</a></code>, and removing any stopwords
supplied in a data frame (using the argument <code>stopwords</code>). This is a
sub-function that feeds into <code>tm_freq()</code>, <code>tm_cooc()</code>, and <code>tm_wordcloud()</code>.
The default is to return a data frame with tokenised counts of words or
ngrams.

Clean subject line text prior to analysis — tm_clean

<dl>

<dt>data</dt>
<dd>A Meeting Query dataset in the form of a data frame.</dd>


<dt>token</dt>
<dd>A character vector accepting either <code>"words"</code> or <code>"ngrams"</code>,
determining type of tokenisation to return.</dd>


<dt>stopwords</dt>
<dd>A character vector OR a single-column data frame labelled
<code>'word'</code> containing custom stopwords to remove.</dd>


<dt>...</dt>
<dd>Additional parameters to pass to <code><a href='https://rdrr.io/pkg/tidytext/man/unnest_tokens.html'>tidytext::unnest_tokens()</a></code>.</dd>

</dl>

tm_clean: Clean subject line text prior to analysis

Description

Usage

Value

Arguments

See Also

Examples