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<h1 id="reproducible-data-analysis-with-r">Reproducible Data Analysis with R</h1>
<ul>
<li><a href="#the-default-report">The default report</a></li>
<li><a href="#other-output-formats">Other output formats</a></li>
<li><a href="#include-ggplot2-output">Include ggplot2 output</a></li>
<li><a href="#toc">TOC</a></li>
<li><a href="#chuck-settings">Chuck settings</a>
<ul>
<li><a href="#global-is-in-the-top-of-the-document">Global is in the top of the document</a></li>
<li><a href="#local-is-in-the-top-of-each-chunck">Local is in the top of each chunck</a></li>
</ul></li>
<li><a href="#tables">Tables</a></li>
<li><a href="#nicer-tables">Nice'r tables</a></li>
<li><a href="#a-presentation">A presentation</a></li>
</ul>
<p>Exercises for <em>Reproducible data analysis using R</em> at Dept FOOD UPCH.</p>
<p>This course is a code-along 2-hours short course, where we try to learn functionallity by solving some problems.</p>
<p>We are not going to focus on the specifics of the data analysis. But it would be very useful if you have a data analysis task which you have done using an R-script with data import, figure production and analysis. Then the last part of combining these outputs pieces into a report is what we will focus on here.</p>
<p>You will see that it is basically some copy-pasting from the R-script into a Rmarkdown document and adding some narrative and structure around it!</p>
<p>The intention is that you should see what is possible, and be able to do some task your self. However, most of the functionallity will need a bit of googleling... so please try to use this resourse efficiently when you do data analysis.</p>
<h1 id="the-default-report">The default report</h1>
<p>Make the report with default to html. I.e. File -> New File -> R Markdown...</p>
<h1 id="other-output-formats">Other output formats</h1>
<p>Try to exchange / add other output formats like word and pdf. Be aware that this might need additional installations, such as an latex engine. If not doable, then stick with html.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="op">---</span>
title<span class="op">:</span><span class="st"> "Vision report FOOD 2020"</span>
author<span class="op">:</span><span class="st"> "Anna Haldrup"</span>
date<span class="op">:</span><span class="st"> "today in 2019"</span>
output<span class="op">:</span><span class="st"> </span>
<span class="st"> </span>pdf_document
<span class="op">---</span></code></pre></div>
<h1 id="include-ggplot2-output">Include ggplot2 output</h1>
<p>Extend the report to include data import and vizualisation using ggplot2.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(rio)
<span class="kw">library</span>(ggplot2)
X <-<span class="st"> </span><span class="kw">import</span>(<span class="st">'data/cheese_aromas.xlsx'</span>)
<span class="kw">ggplot</span>(<span class="dt">data =</span> X, <span class="kw">aes</span>(time_culture,<span class="st">`</span><span class="dt">1Butanol</span><span class="st">`</span>, <span class="dt">fill =</span> maturation_culture)) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">geom_boxplot</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">ylab</span>(<span class="st">'Response - 1-Butanol'</span>) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">14</span>))</code></pre></div>
<p><img 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" /></p>
<h1 id="toc">TOC</h1>
<p>Add headings #, ##, ### .. and figure out how to modify the title/authour/output part to include a table of content (toc)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="op">---</span>
title<span class="op">:</span><span class="st"> "My report"</span>
author<span class="op">:</span><span class="st"> "Anna Haldrup"</span>
date<span class="op">:</span><span class="st"> "today in 2019"</span>
output<span class="op">:</span><span class="st"> </span>
<span class="st"> </span>html_document<span class="op">:</span><span class="st"> </span>
<span class="st"> </span>toc<span class="op">:</span><span class="st"> </span>true
toc_depth<span class="op">:</span><span class="st"> </span><span class="dv">5</span>
<span class="op">---</span></code></pre></div>
<h1 id="chuck-settings">Chuck settings</h1>
<p>Global and individual chunk settings. default might not allways be the nicer option. For instance, the figures are left aligned.</p>
<h2 id="global-is-in-the-top-of-the-document">Global is in the top of the document</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#```{r setup, include=FALSE}</span>
<span class="co"># knitr::opts_chunk$set(echo = TRUE)</span>
<span class="co">#```</span></code></pre></div>
<h2 id="local-is-in-the-top-of-each-chunck">Local is in the top of each chunck</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#```{r, ...., fig.height = ??, include = ??,....}</span></code></pre></div>
<ul>
<li>Try to align figures such that all are centered</li>
<li>Try to turn of all the code in the final report. That is, the code is executed but not shown.</li>
<li>Try to modify a local chunk such that it is shown, but not executed.</li>
</ul>
<h1 id="tables">Tables</h1>
<p>Install (if it is not already!) and use the knitr package to produce tables with the kable() function. Include caption and number of digits.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(knitr)
df <-<span class="st"> </span><span class="kw">do.call</span>(data.frame,<span class="kw">aggregate</span>(X<span class="op">$</span><span class="st">`</span><span class="dt">1Butanol</span><span class="st">`</span>,
<span class="kw">list</span>(X<span class="op">$</span>time_weeks,X<span class="op">$</span>maturation_culture),
<span class="cf">function</span>(x) <span class="kw">c</span>(<span class="kw">length</span>(x), <span class="kw">mean</span>(x), <span class="kw">sd</span>(x), <span class="kw">min</span>(x), <span class="kw">max</span>(x))))
<span class="kw">colnames</span>(df) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">'week'</span>,<span class="st">'culture'</span>,<span class="st">'n'</span>,<span class="st">'mean'</span>,<span class="st">'sd'</span>,<span class="st">'min'</span>,<span class="st">'max'</span>)
<span class="kw">kable</span>(df, <span class="dt">digits =</span> <span class="dv">1</span>)</code></pre></div>
<table>
<thead>
<tr class="header">
<th align="right">week</th>
<th align="left">culture</th>
<th align="right">n</th>
<th align="right">mean</th>
<th align="right">sd</th>
<th align="right">min</th>
<th align="right">max</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">4</td>
<td align="left">culture</td>
<td align="right">4</td>
<td align="right">7.6</td>
<td align="right">0.0</td>
<td align="right">7.5</td>
<td align="right">7.6</td>
</tr>
<tr class="even">
<td align="right">7</td>
<td align="left">culture</td>
<td align="right">5</td>
<td align="right">7.4</td>
<td align="right">0.0</td>
<td align="right">7.3</td>
<td align="right">7.4</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="left">culture</td>
<td align="right">6</td>
<td align="right">7.3</td>
<td align="right">0.1</td>
<td align="right">7.2</td>
<td align="right">7.4</td>
</tr>
<tr class="even">
<td align="right">4</td>
<td align="left">none</td>
<td align="right">6</td>
<td align="right">7.6</td>
<td align="right">0.0</td>
<td align="right">7.5</td>
<td align="right">7.6</td>
</tr>
<tr class="odd">
<td align="right">7</td>
<td align="left">none</td>
<td align="right">6</td>
<td align="right">7.7</td>
<td align="right">0.1</td>
<td align="right">7.7</td>
<td align="right">7.8</td>
</tr>
<tr class="even">
<td align="right">10</td>
<td align="left">none</td>
<td align="right">5</td>
<td align="right">7.9</td>
<td align="right">0.1</td>
<td align="right">7.8</td>
<td align="right">8.0</td>
</tr>
</tbody>
</table>
<h1 id="nicer-tables">Nice'r tables</h1>
<p>If you really want to make tables looking nice, then check out the kableExtra package, and its functionallity. simply try to copy paste some of the codes here into your document see <a href="https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html" class="uri">https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html</a></p>
<p>Try to do something like this and see what it produces:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(kableExtra)
<span class="kw">library</span>(tidyverse)
<span class="kw">kable</span>(df,<span class="dt">digits =</span> <span class="dv">1</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable_styling</span>(<span class="st">"striped"</span>, <span class="dt">full_width =</span> F) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">2</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">3</span>,<span class="dv">4</span>,<span class="dv">6</span>), <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"#D7261E"</span>)</code></pre></div>
<p>By the way: Find your favorite color here: <a href="https://www.what-if.com/colours/standard-216-colour-palette/" class="uri">https://www.what-if.com/colours/standard-216-colour-palette/</a></p>
<p>...Or maybe you have a picture with a color, and want to figure out its HEX-code: <a href="https://www.colorcodepicker.com/" class="uri">https://www.colorcodepicker.com/</a></p>
<p>For instance, the HEX-color: <em>#45743d</em> is the main color in UCPH-Science logo.</p>
<h1 id="a-presentation">A presentation</h1>
<p>In the initialization of the document, you can select several output styles.</p>
<ul>
<li>Try and make a presentation instead of a report.</li>
<li>Try to move some of the code from your report into the presentation, and see how it turns out.</li>
<li>Do you want to modify things?, Try to browse how to do it!</li>
</ul>
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