prep_query

For applying to data frames that are read into R using any
other method other than <code>import_query()</code>, this function cleans variable
names by replacing special characters and converting the relevant variable
types so that they are compatible with the rest of the functions in
vivainsights.

Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.

Martin Chan

vivainsights

Analyze and Visualize Data from 'Microsoft Viva Insights'

Carlos Morales

prep_query function

<dl><dt>data</dt>
<dd>A Standard Person Query dataset in the form of a data frame. You
should pass the data frame that is read into R using any other method
other than <code>import_query()</code>, as <code>import_query()</code> automatically performs the
same variable operations.</dd>
<dt>convert_date</dt>
<dd>Logical. Defaults to <code>TRUE</code>. When set to <code>TRUE</code>, any
variable that matches true with <code>is_date_format()</code> gets converted to a Date
variable. When set to <code>FALSE</code>, this step is skipped.</dd>
<dt>date_format</dt>
<dd>String specifying the date format for converting any
variable that may be a date to a Date variable. Defaults to <code>"%m/%d/%Y"</code>.</dd></dl>

Arguments

The following shows when and how to use <code>prep_query()</code>:
<div class="sourceCode r"></div><pre><code> pq_df &lt;- read.csv("path_to_query.csv")
 cleaned_df &lt;- pq_df |&gt; prep_query()
</code></pre>
You can then run checks to see that the variables are of the correct type:
<div class="sourceCode r"></div><pre><code>dplyr::glimpse(cleaned_df)
</code></pre>

Examples

Prepare variable names and types in query data frame for analysis — prep_query

<dl>

<dt>data</dt>
<dd>A Standard Person Query dataset in the form of a data frame. You
should pass the data frame that is read into R using any other method
other than <code>import_query()</code>, as <code>import_query()</code> automatically performs the
same variable operations.</dd>


<dt>convert_date</dt>
<dd>Logical. Defaults to <code>TRUE</code>. When set to <code>TRUE</code>, any
variable that matches true with <code>is_date_format()</code> gets converted to a Date
variable. When set to <code>FALSE</code>, this step is skipped.</dd>


<dt>date_format</dt>
<dd>String specifying the date format for converting any
variable that may be a date to a Date variable. Defaults to <code>"%m/%d/%Y"</code>.</dd>

</dl>

The following shows when and how to use <code>prep_query()</code>:
<div class="sourceCode r"><pre><code> pq_df &lt;- read.csv("path_to_query.csv")
 cleaned_df &lt;- pq_df |&gt; prep_query()
</code></pre></div>
You can then run checks to see that the variables are of the correct type:
<div class="sourceCode r"><pre><code>dplyr::glimpse(cleaned_df)
</code></pre></div>

prep_query: Prepare variable names and types in query data frame for analysis

Description

Usage

Value

Arguments

Examples

See Also