fix boolean typo OR AND

mmiots9 · Feb 23, 2024 · 75a95dc · 75a95dc
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diff --git a/07-Booleans.Rmd b/07-Booleans.Rmd
@@ -94,7 +94,7 @@ expr <- 50
 </ul>
 An easy trick to remember is: if at least one is TRUE, then the result is TRUE, otherwise if are all FALSE, the result is FALSE.
 <br>
-In R, AND operator is the `|`, some examples:
+In R, OR operator is the `|`, some examples:
 ```{r}
 expr <- 50
 

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diff --git a/docs/404.html b/docs/404.html
@@ -24,7 +24,7 @@
 <meta name="author" content="Matteo Miotto" />
 
 
-<meta name="date" content="2023-12-09" />
+<meta name="date" content="2024-02-23" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1" />
   <meta name="apple-mobile-web-app-capable" content="yes" />
@@ -33,7 +33,7 @@
 
 
 
-<script src="libs/header-attrs-2.25/header-attrs.js"></script>
+<script src="libs/header-attrs-2.11/header-attrs.js"></script>
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@@ -51,9 +51,8 @@
 
 
 
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-<link href="libs/anchor-sections-1.1.0/anchor-sections-hash.css" rel="stylesheet" />
-<script src="libs/anchor-sections-1.1.0/anchor-sections.js"></script>
+<link href="libs/anchor-sections-1.0.1/anchor-sections.css" rel="stylesheet" />
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 <meta property="og:description" content="To you, who never gives up, for your future. You can make it!">
 <meta name="keywords" content="R, biology, data analysis">
 <meta name="author" content="Matteo Miotto">
@@ -71,7 +70,7 @@
 
 <style type="text/css">
 pre > code.sourceCode { white-space: pre; position: relative; }
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diff --git a/docs/correlation.html b/docs/correlation.html
@@ -24,7 +24,7 @@
 <meta name="author" content="Matteo Miotto" />
 
 
-<meta name="date" content="2023-12-09" />
+<meta name="date" content="2024-02-23" />
 
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@@ -51,9 +51,8 @@
 
 
 
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-<link href="libs/anchor-sections-1.1.0/anchor-sections-hash.css" rel="stylesheet" />
-<script src="libs/anchor-sections-1.1.0/anchor-sections.js"></script>
+<link href="libs/anchor-sections-1.0.1/anchor-sections.css" rel="stylesheet" />
+<script src="libs/anchor-sections-1.0.1/anchor-sections.js"></script>
 <meta property="og:description" content="To you, who never gives up, for your future. You can make it!">
 <meta name="keywords" content="R, biology, data analysis">
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@@ -71,7 +70,7 @@
 
 <style type="text/css">
 pre > code.sourceCode { white-space: pre; position: relative; }
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+pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
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@@ -384,8 +383,8 @@ <h1>
           <div class="page-inner">
 
             <section class="normal" id="section-">
-<div id="correlation" class="section level1 hasAnchor" number="19">
-<h1><span class="header-section-number">19</span> Correlation<a href="correlation.html#correlation" class="anchor-section" aria-label="Anchor link to header"></a></h1>
+<div id="correlation" class="section level1" number="19">
+<h1><span class="header-section-number">19</span> Correlation</h1>
 <p>Here we are to one of the most controversial statistical test: the correlation. Why controversial? “Pull off the band-aid” and let’s answer this question at first: usually correlation is used by some scientist as a synonym of <em>causation</em>, but it is not like this! To express this concept, we have to understand what correlation is.
 Correlation is the measure of the relation of changes of data between two variables; it can answer these questions: “If A changes, does B do the same?”, “If A increases, does B increase? Or decrease?” So, many people then say “An increase in A leads to an increase of B”, but it is totally <strong>wrong</strong> if we do not know and demonstrates that B is caused by A.</p>
 <p><strong>Remember:</strong> correlation is not causation.
@@ -411,25 +410,25 @@ <h1><span class="header-section-number">19</span> Correlation<a href="correlatio
 </ul>
 <p>We’ll see in detail the Pearson test, as in our example we have two continuous variables, then we will briefly see the other two tests.</p>
 <p>Let’s load the data:</p>
-<div class="sourceCode" id="cb587"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb587-1"><a href="correlation.html#cb587-1" tabindex="-1"></a><span class="co"># 1. Load packages </span></span>
-<span id="cb587-2"><a href="correlation.html#cb587-2" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(tidyverse))</span>
-<span id="cb587-3"><a href="correlation.html#cb587-3" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(ggplot2))</span>
-<span id="cb587-4"><a href="correlation.html#cb587-4" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(ggpubr))</span>
-<span id="cb587-5"><a href="correlation.html#cb587-5" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(gginnards))</span>
-<span id="cb587-6"><a href="correlation.html#cb587-6" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(glue))</span>
-<span id="cb587-7"><a href="correlation.html#cb587-7" tabindex="-1"></a></span>
-<span id="cb587-8"><a href="correlation.html#cb587-8" tabindex="-1"></a><span class="co"># 2. Load data</span></span>
-<span id="cb587-9"><a href="correlation.html#cb587-9" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;data/Stat-test-dataset.csv&quot;</span>)</span>
-<span id="cb587-10"><a href="correlation.html#cb587-10" tabindex="-1"></a></span>
-<span id="cb587-11"><a href="correlation.html#cb587-11" tabindex="-1"></a><span class="co"># 3. Change come column types</span></span>
-<span id="cb587-12"><a href="correlation.html#cb587-12" tabindex="-1"></a>df <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span></span>
-<span id="cb587-13"><a href="correlation.html#cb587-13" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="st">&quot;sex&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(sex),</span>
-<span id="cb587-14"><a href="correlation.html#cb587-14" tabindex="-1"></a>         <span class="st">&quot;treatment&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(treatment, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;T1&quot;</span>, <span class="st">&quot;Untreated&quot;</span>)),</span>
-<span id="cb587-15"><a href="correlation.html#cb587-15" tabindex="-1"></a>         <span class="st">&quot;Task1&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(Task1, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">0</span>)),</span>
-<span id="cb587-16"><a href="correlation.html#cb587-16" tabindex="-1"></a>         <span class="st">&quot;Task2&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(Task2, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">0</span>)),</span>
-<span id="cb587-17"><a href="correlation.html#cb587-17" tabindex="-1"></a>         )</span>
-<span id="cb587-18"><a href="correlation.html#cb587-18" tabindex="-1"></a></span>
-<span id="cb587-19"><a href="correlation.html#cb587-19" tabindex="-1"></a><span class="fu">str</span>(df)</span></code></pre></div>
+<div class="sourceCode" id="cb587"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb587-1"><a href="correlation.html#cb587-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 1. Load packages </span></span>
+<span id="cb587-2"><a href="correlation.html#cb587-2" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(tidyverse))</span>
+<span id="cb587-3"><a href="correlation.html#cb587-3" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(ggplot2))</span>
+<span id="cb587-4"><a href="correlation.html#cb587-4" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(ggpubr))</span>
+<span id="cb587-5"><a href="correlation.html#cb587-5" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(gginnards))</span>
+<span id="cb587-6"><a href="correlation.html#cb587-6" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressPackageStartupMessages</span>(<span class="fu">library</span>(glue))</span>
+<span id="cb587-7"><a href="correlation.html#cb587-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb587-8"><a href="correlation.html#cb587-8" aria-hidden="true" tabindex="-1"></a><span class="co"># 2. Load data</span></span>
+<span id="cb587-9"><a href="correlation.html#cb587-9" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;data/Stat-test-dataset.csv&quot;</span>)</span>
+<span id="cb587-10"><a href="correlation.html#cb587-10" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb587-11"><a href="correlation.html#cb587-11" aria-hidden="true" tabindex="-1"></a><span class="co"># 3. Change come column types</span></span>
+<span id="cb587-12"><a href="correlation.html#cb587-12" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span></span>
+<span id="cb587-13"><a href="correlation.html#cb587-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="st">&quot;sex&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(sex),</span>
+<span id="cb587-14"><a href="correlation.html#cb587-14" aria-hidden="true" tabindex="-1"></a>         <span class="st">&quot;treatment&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(treatment, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;T1&quot;</span>, <span class="st">&quot;Untreated&quot;</span>)),</span>
+<span id="cb587-15"><a href="correlation.html#cb587-15" aria-hidden="true" tabindex="-1"></a>         <span class="st">&quot;Task1&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(Task1, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">0</span>)),</span>
+<span id="cb587-16"><a href="correlation.html#cb587-16" aria-hidden="true" tabindex="-1"></a>         <span class="st">&quot;Task2&quot;</span> <span class="ot">=</span> <span class="fu">factor</span>(Task2, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">0</span>)),</span>
+<span id="cb587-17"><a href="correlation.html#cb587-17" aria-hidden="true" tabindex="-1"></a>         )</span>
+<span id="cb587-18"><a href="correlation.html#cb587-18" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb587-19"><a href="correlation.html#cb587-19" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(df)</span></code></pre></div>
 <pre class="code-out"><code>&#39;data.frame&#39;:   600 obs. of  7 variables:
  $ sex      : Factor w/ 2 levels &quot;Female&quot;,&quot;Male&quot;: 2 1 2 1 2 1 2 1 2 1 ...
  $ age      : int  3 3 3 3 3 3 3 3 3 3 ...
@@ -438,18 +437,18 @@ <h1><span class="header-section-number">19</span> Correlation<a href="correlatio
  $ Task1    : Factor w/ 2 levels &quot;1&quot;,&quot;0&quot;: 1 2 2 2 1 1 1 1 2 1 ...
  $ Task2    : Factor w/ 2 levels &quot;1&quot;,&quot;0&quot;: 1 2 2 1 1 2 2 1 1 1 ...
  $ Task3    : num  222.4 202.3 36.7 221.8 178.8 ...</code></pre>
-<div id="pearsons-test" class="section level2 unnumbered hasAnchor">
-<h2>Pearson’s test<a href="correlation.html#pearsons-test" class="anchor-section" aria-label="Anchor link to header"></a></h2>
+<div id="pearsons-test" class="section level2 unnumbered">
+<h2>Pearson’s test</h2>
 <p>We want to know if weight and Task3 are correlated in both males and females.
 The first thing to do is, as always, plot the variables to have a look at them:</p>
-<div class="sourceCode" id="cb589"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb589-1"><a href="correlation.html#cb589-1" tabindex="-1"></a><span class="fu">ggplot</span>(df) <span class="sc">+</span></span>
-<span id="cb589-2"><a href="correlation.html#cb589-2" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> weight, <span class="at">y =</span> Task3, <span class="at">color =</span> sex)) <span class="sc">+</span></span>
-<span id="cb589-3"><a href="correlation.html#cb589-3" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Task3 (s)&quot;</span>, <span class="at">x =</span> <span class="st">&quot;Weight (g)&quot;</span>, <span class="at">color =</span> <span class="st">&quot;Sex&quot;</span>) <span class="sc">+</span></span>
-<span id="cb589-4"><a href="correlation.html#cb589-4" tabindex="-1"></a>  <span class="fu">theme_classic</span>()</span></code></pre></div>
+<div class="sourceCode" id="cb589"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb589-1"><a href="correlation.html#cb589-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df) <span class="sc">+</span></span>
+<span id="cb589-2"><a href="correlation.html#cb589-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> weight, <span class="at">y =</span> Task3, <span class="at">color =</span> sex)) <span class="sc">+</span></span>
+<span id="cb589-3"><a href="correlation.html#cb589-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Task3 (s)&quot;</span>, <span class="at">x =</span> <span class="st">&quot;Weight (g)&quot;</span>, <span class="at">color =</span> <span class="st">&quot;Sex&quot;</span>) <span class="sc">+</span></span>
+<span id="cb589-4"><a href="correlation.html#cb589-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_classic</span>()</span></code></pre></div>
 <p><img src="_main_files/figure-html/pearson-1.svg" width="85%" style="display: block; margin: auto;" /></p>
 <p>Just visually, we can say that there is no positive or negative correlation between them. To claim it, let’s apply the Pearson’s test: the function to use is <code>cor.test()</code>, specifying <code>method = "pearson"</code>; the order of the variables is not important.</p>
-<div class="sourceCode" id="cb590"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb590-1"><a href="correlation.html#cb590-1" tabindex="-1"></a>pearson_res <span class="ot">&lt;-</span> <span class="fu">cor.test</span>(df<span class="sc">$</span>weight, df<span class="sc">$</span>Task3, <span class="at">method =</span> <span class="st">&quot;pearson&quot;</span>)</span>
-<span id="cb590-2"><a href="correlation.html#cb590-2" tabindex="-1"></a>pearson_res</span></code></pre></div>
+<div class="sourceCode" id="cb590"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb590-1"><a href="correlation.html#cb590-1" aria-hidden="true" tabindex="-1"></a>pearson_res <span class="ot">&lt;-</span> <span class="fu">cor.test</span>(df<span class="sc">$</span>weight, df<span class="sc">$</span>Task3, <span class="at">method =</span> <span class="st">&quot;pearson&quot;</span>)</span>
+<span id="cb590-2"><a href="correlation.html#cb590-2" aria-hidden="true" tabindex="-1"></a>pearson_res</span></code></pre></div>
 <pre class="code-out"><code>
     Pearson&#39;s product-moment correlation