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<img src="../logo.png" class="logo" alt=""><h1>FreesearchR</h1>
<small class="dont-index">Source: <a href="https://github.com/agdamsbo/FreesearchR/blob/main/vignettes/FreesearchR.Rmd" class="external-link"><code>vignettes/FreesearchR.Rmd</code></a></small>
<div class="d-none name"><code>FreesearchR.Rmd</code></div>
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<div class="section level2">
<h2 id="getting-started-with-freesearchr">Getting started with <strong><em>FreesearchR</em></strong><a class="anchor" aria-label="anchor" href="#getting-started-with-freesearchr"></a>
</h2>
<p>Below is a simple walk-trough and basic instructions for the
functions on the FreesearchR app.</p>
<div class="section level3">
<h3 id="launching">Launching<a class="anchor" aria-label="anchor" href="#launching"></a>
</h3>
<p>The easiest way to get started is to launch <a href="https://agdamsbo.shinyapps.io/freesearcheR/" class="external-link">the hosted version of
the app on shinyapps.io (click this link)</a>.</p>
<p>Additionally you have the option to run the app locally with access
to any data in your current working environment.</p>
<p>To do this, open <em>R</em> (or RStudio or similar), and run the
following code to install the latest version of
<strong><em>FreesearchR</em></strong> and launch the app:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://pak.r-lib.org/" class="external-link">"pak"</a></span><span class="op">)</span></span>
<span><span class="fu">pak</span><span class="fu">::</span><span class="fu"><a href="https://pak.r-lib.org/reference/pak.html" class="external-link">pak</a></span><span class="op">(</span><span class="st">"agdamsbo/FreesearchR"</span><span class="op">)</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/agdamsbo/FreesearchR" class="external-link">FreesearchR</a></span><span class="op">)</span></span>
<span><span class="fu">FreesearchR</span><span class="fu">::</span><span class="fu"><a href="../reference/launch_FreesearchR.html">launch_FreesearchR</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p>As a small note, a standalone Windows app version is on the drawing
board as well, but no time frame is available.</p>
</div>
<div class="section level3">
<h3 id="importing-data">Importing data<a class="anchor" aria-label="anchor" href="#importing-data"></a>
</h3>
<p>Once in the app and in the “<strong>Import</strong>”, you have three
options available for importing data: file upload, REDCap server export
and local or sample data.</p>
<p>After choosing a data source, you can set a threshold to filter data
be completenes and further manually specify variables to include for
analyses.</p>
<div class="section level4">
<h4 id="file-upload">File upload<a class="anchor" aria-label="anchor" href="#file-upload"></a>
</h4>
<p>Currently several data file formats are supported for easy import
(csv, txt, xls(x), ods, rds, dta). If importing workbooks (xls(x) or
ods), you are prompted to specify sheet(s) to import. If choosing
multiple sheets, these are automatically merged by common variable(s),
so please make sure that key variables are correctly named
identically.</p>
<p>A notification is posted with error or success. After succesfull
import data can be previewed directly by clicking “click to see data” in
the notification.</p>
</div>
<div class="section level4">
<h4 id="redcap-server-export">REDCap server export<a class="anchor" aria-label="anchor" href="#redcap-server-export"></a>
</h4>
<p>Export data directly from a REDCap server. You need to first generate
an API-token (<a href="https://confluence.research.cchmc.org/pages/viewpage.action?pageId=50987698" class="external-link">see
these instruction</a>) in REDCap. Make sure you have the necessary
rights to do so.</p>
<p>Please don’t store the API-key on your device unless encrypted or in
a keyring, as this may compromise data safety. Log in to your REDCap
server and retrieve the token when needed.</p>
<p>Type the correct webaddress of your REDCap server.</p>
<p>The module will validate the information and you can click
“Connect”.</p>
<p>This will unfold options to preview your data dictionary (the main
database metadata), choose fields/variables to download as well as
filtering options.</p>
</div>
<div class="section level4">
<h4 id="local-or-sample-data">Local or sample data<a class="anchor" aria-label="anchor" href="#local-or-sample-data"></a>
</h4>
<p>When opening the online hosted app, this is mainly for testing
purposes. When running the app locally from <em>R</em> on your own
computer, you will find all data.frames in the current environment here.
This extends the possible uses of this app to allow for quick and easy
data insights and code generation for basic plotting to fine tune.</p>
</div>
</div>
<div class="section level3">
<h3 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a>
</h3>
<p>This is the panel to get a good overview of your data, check data is
classed and formatted correctly, perform simple modifications and filter
data.</p>
<div class="section level4">
<h4 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a>
</h4>
<p>Here, the data variables can be inspected with a simple visualisation
and a few key measures. Also, data filtering is available at two
levels:</p>
<ul>
<li><p>Data type filtering allows to filter by variable <a href="https://agdamsbo.github.io/FreesearchR/articles/data-types.html">data
type</a></p></li>
<li><p>Observations level filtering allow to filter data by
variable</p></li>
</ul>
</div>
<div class="section level4">
<h4 id="modify">Modify<a class="anchor" aria-label="anchor" href="#modify"></a>
</h4>
<p>Re-class, rename, and relabel variables. Subset data, create new
variables and reorder factor levels. Also, compare the modified dataset
to the original and restore the original data.</p>
</div>
</div>
<div class="section level3">
<h3 id="evaluate">Evaluate<a class="anchor" aria-label="anchor" href="#evaluate"></a>
</h3>
<p>This panel allows for basic data evaluation.</p>
<div class="section level4">
<h4 id="characteristics">Characteristics<a class="anchor" aria-label="anchor" href="#characteristics"></a>
</h4>
<p>Create a classical baseline characteristics table with optional data
stratification and comparisons.</p>
</div>
<div class="section level4">
<h4 id="correlation-matrix">Correlation matrix<a class="anchor" aria-label="anchor" href="#correlation-matrix"></a>
</h4>
<p>Visualise variable correlations and get suggestions to exclude highly
correlated variables.</p>
</div>
</div>
<div class="section level3">
<h3 id="visuals">Visuals<a class="anchor" aria-label="anchor" href="#visuals"></a>
</h3>
<p>There are a number of plotting options to visualise different aspects
of the data.</p>
<p>Below are the available plot types listed.</p>
<table class="table">
<colgroup>
<col width="7%">
<col width="14%">
<col width="77%">
</colgroup>
<thead><tr class="header">
<th align="left">Data type</th>
<th align="left">Plot type</th>
<th align="left">Description</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">continuous</td>
<td align="left">Violin plot</td>
<td align="left">A modern alternative to the classic boxplot to
visualise data distribution</td>
</tr>
<tr class="even">
<td align="left">continuous</td>
<td align="left">Scatter plot</td>
<td align="left">A classic way of showing the association between to
variables</td>
</tr>
<tr class="odd">
<td align="left">continuous</td>
<td align="left">Box plot</td>
<td align="left">A classic way to plot data distribution by groups</td>
</tr>
<tr class="even">
<td align="left">dichotomous</td>
<td align="left">Stacked horizontal bars</td>
<td align="left">A classical way of visualising the distribution of an
ordinal scale like the modified Ranking Scale and known as Grotta
bars</td>
</tr>
<tr class="odd">
<td align="left">dichotomous</td>
<td align="left">Violin plot</td>
<td align="left">A modern alternative to the classic boxplot to
visualise data distribution</td>
</tr>
<tr class="even">
<td align="left">dichotomous</td>
<td align="left">Sankey plot</td>
<td align="left">A way of visualising change between groups</td>
</tr>
<tr class="odd">
<td align="left">dichotomous</td>
<td align="left">Box plot</td>
<td align="left">A classic way to plot data distribution by groups</td>
</tr>
<tr class="even">
<td align="left">dichotomous</td>
<td align="left">Euler diagram</td>
<td align="left">Generate area-proportional Euler diagrams to display
set relationships</td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">Stacked horizontal bars</td>
<td align="left">A classical way of visualising the distribution of an
ordinal scale like the modified Ranking Scale and known as Grotta
bars</td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">Violin plot</td>
<td align="left">A modern alternative to the classic boxplot to
visualise data distribution</td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">Sankey plot</td>
<td align="left">A way of visualising change between groups</td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">Box plot</td>
<td align="left">A classic way to plot data distribution by groups</td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">Euler diagram</td>
<td align="left">Generate area-proportional Euler diagrams to display
set relationships</td>
</tr>
</tbody>
</table>
<p>Export the plots directly from the sidebar with easily adjusted plot
dimensions for your next publication.</p>
<p>Also copy the code to generate the plot in your own R-environment and
fine tune all the small details.</p>
</div>
<div class="section level3">
<h3 id="regression">Regression<a class="anchor" aria-label="anchor" href="#regression"></a>
</h3>
<p>This section is only intended for very simple explorative analyses
and as a proof-of-concept for now. If you are doing complex regression
analyses you should probably just write the code yourself.</p>
<p>Below are the available regression types listed.</p>
<table class="table">
<colgroup>
<col width="16%">
<col width="46%">
<col width="15%">
<col width="21%">
</colgroup>
<thead><tr class="header">
<th align="left">Data type</th>
<th align="left">Regression model</th>
<th align="left">Function</th>
<th align="left">Study design</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">continuous</td>
<td align="left">Linear regression model</td>
<td align="left">stats::lm</td>
<td align="left">cross-sectional</td>
</tr>
<tr class="even">
<td align="left">dichotomous</td>
<td align="left">Logistic regression model</td>
<td align="left">stats::glm</td>
<td align="left">cross-sectional</td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">Ordinal logistic regression model</td>
<td align="left">MASS::polr</td>
<td align="left">cross-sectional</td>
</tr>
</tbody>
</table>
<div class="section level4">
<h4 id="table">Table<a class="anchor" aria-label="anchor" href="#table"></a>
</h4>
<p>Generate simple regression models and get the results in a nice
table. This will also be included in the exported report.</p>
</div>
<div class="section level4">
<h4 id="plots">Plots<a class="anchor" aria-label="anchor" href="#plots"></a>
</h4>
<p>Plot the coefficients from the regression models in a forest plot.
Choose which model(s) to include.</p>
</div>
<div class="section level4">
<h4 id="checks">Checks<a class="anchor" aria-label="anchor" href="#checks"></a>
</h4>
<p>Check model assumptions visually. Supported checks can be chosen.</p>
</div>
</div>
<div class="section level3">
<h3 id="download">Download<a class="anchor" aria-label="anchor" href="#download"></a>
</h3>
<div class="section level4">
<h4 id="report">Report<a class="anchor" aria-label="anchor" href="#report"></a>
</h4>
<p>Download a nice report with baseline characteristics and regression
model results. Choose between MS Word or LibreOffice format.</p>
</div>
<div class="section level4">
<h4 id="data-1">Data<a class="anchor" aria-label="anchor" href="#data-1"></a>
</h4>
<p>Export the modified dataset in different formats.</p>
</div>
<div class="section level4">
<h4 id="code">Code<a class="anchor" aria-label="anchor" href="#code"></a>
</h4>
<p>See all the code snippets from the different steps in your data
evaluation.</p>
</div>
</div>
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