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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Programación orientada a objetos</title>
<meta name="apple-mobile-web-app-capable" content="yes" />
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
<!-- web fonts -->
<style type="text/css">
@font-face {
font-family: 'Lato';
font-style: normal;
font-weight: 400;
src: local('Lato Regular'), local('Lato-Regular'), url('Lato-Regular.ttf') format('truetype');
}
@font-face {
font-family: 'Lato';
font-style: normal;
font-weight: 700;
src: local('Lato Bold'), local('Lato-Bold'), url('Lato-Bold.ttf') format('truetype');
}
@font-face {
font-family: 'Lato';
font-style: italic;
font-weight: 400;
src: local('Lato Italic'), local('Lato-Italic'), url('Lato-Italic.ttf') format('truetype');
}
@font-face {
font-family: 'Lato';
font-style: italic;
font-weight: 700;
src: local('Lato Bold Italic'), local('Lato-BoldItalic'), url('Lato-BoldItalic.ttf') format('truetype');
}
@font-face {
font-family: 'News Cycle';
font-style: normal;
font-weight: 400;
src: local('News Cycle Regular'), local('NewsCycle-Regular'), url('NewsCycle-Regular.ttf') format('truetype');
}
@font-face {
font-family: 'News Cycle';
font-style: normal;
font-weight: 700;
src: local('News Cycle Bold'), local('NewsCycle-Bold'), url('NewsCycle-Bold.ttf') format('truetype');
}
</style>
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<style type="text/css" >
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</style>
<style type="text/css" >
/**
* A simple theme for reveal.js presentations, similar
* to the default theme. The accent color is darkblue.
*
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
*/
/*********************************************
* GLOBAL STYLES
*********************************************/
body {
background: white;
background-color: white; }
.reveal {
font-family: "Lato", sans-serif;
font-size: 36px;
font-weight: 200;
letter-spacing: -0.02em;
color: black; }
::selection {
color: white;
background: rgba(0, 0, 0, 0.99);
text-shadow: none; }
/*********************************************
* HEADERS
*********************************************/
.reveal h1,
.reveal h2,
.reveal h3,
.reveal h4,
.reveal h5,
.reveal h6 {
margin: 0 0 20px 0;
color: black;
font-family: "News Cycle", Impact, sans-serif;
line-height: 0.9em;
letter-spacing: 0.02em;
text-transform: none;
text-shadow: none; }
.reveal h1 {
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
/*********************************************
* LINKS
*********************************************/
.reveal a:not(.image) {
color: darkblue;
text-decoration: none;
-webkit-transition: color .15s ease;
-moz-transition: color .15s ease;
-ms-transition: color .15s ease;
-o-transition: color .15s ease;
transition: color .15s ease; }
.reveal a:not(.image):hover {
color: #0000f1;
text-shadow: none;
border: none; }
.reveal .roll span:after {
color: #fff;
background: #00003f; }
/*********************************************
* IMAGES
*********************************************/
.reveal section img {
margin: 15px 0px;
background: rgba(255, 255, 255, 0.12);
border: 4px solid black;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
-webkit-transition: all .2s linear;
-moz-transition: all .2s linear;
-ms-transition: all .2s linear;
-o-transition: all .2s linear;
transition: all .2s linear; }
.reveal a:hover img {
background: rgba(255, 255, 255, 0.2);
border-color: darkblue;
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
/*********************************************
* NAVIGATION CONTROLS
*********************************************/
.reveal .controls div.navigate-left,
.reveal .controls div.navigate-left.enabled {
border-right-color: darkblue; }
.reveal .controls div.navigate-right,
.reveal .controls div.navigate-right.enabled {
border-left-color: darkblue; }
.reveal .controls div.navigate-up,
.reveal .controls div.navigate-up.enabled {
border-bottom-color: darkblue; }
.reveal .controls div.navigate-down,
.reveal .controls div.navigate-down.enabled {
border-top-color: darkblue; }
.reveal .controls div.navigate-left.enabled:hover {
border-right-color: #0000f1; }
.reveal .controls div.navigate-right.enabled:hover {
border-left-color: #0000f1; }
.reveal .controls div.navigate-up.enabled:hover {
border-bottom-color: #0000f1; }
.reveal .controls div.navigate-down.enabled:hover {
border-top-color: #0000f1; }
/*********************************************
* PROGRESS BAR
*********************************************/
.reveal .progress {
background: rgba(0, 0, 0, 0.2); }
.reveal .progress span {
background: darkblue;
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
</style>
<style type="text/css">
.reveal h1 {
font-size: 2.5em;
}
.reveal h1,
.reveal h2,
.reveal h3,
.reveal h4,
.reveal h5,
.reveal h6 {
margin-bottom: .6em;
}
.reveal p,
.reveal table {
margin-bottom: 1em;
}
.reveal li {
margin-bottom: .4em;
}
.reveal ul ul,
.reveal ul ol,
.reveal ol ol,
.reveal ol ul {
margin-top: .4em;
}
.reveal .slides {
text-align: left;
}
.reveal small {
font-size: 0.85em;
}
.reveal pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
margin-bottom: 1em;
}
.reveal pre code {
display: block; padding: 0.5em;
font-size: 1.6em;
line-height: 1.1em;
background-color: white;
overflow: visible;
max-height: none;
word-wrap: normal;
}
.reveal code {
overflow: visible;
max-height: none;
}
.reveal code.r {
background-color: #F8F8F8;
}
.reveal code.cpp {
background-color: #F8F8F8;
}
.reveal section del {
text-decoration: none;
color: #AAB1BA;
}
.reveal section img {
border: none;
}
.reveal section .fieldError {
margin-bottom: 25px;
}
.reveal section .fieldError span {
color: red;
}
.prompt .reveal .state-background {
background: #C6D7DC;
}
.quiz-multichoice .reveal .state-background {
background: rgba(254,220,179,1);
}
.quiz-multichoice .reveal ul {
list-style-type: none;
margin-bottom: 30px;
}
.quiz-multichoice .reveal li {
margin-top: 15px;
}
.quiz-multichoice .reveal .quizFeedback {
margin-bottom: 30px;
}
.quiz-multichoice .reveal .quizFeedback img {
border: none;
box-shadow: none;
background: transparent;
float: left;
margin-top: -15px;
}
.quiz-multichoice .reveal .quizFeedback span {
font-size: 1.4em;
margin-left: 12px;
}
.section .reveal .state-background {
background: #96A2B6;
}
.section .reveal h1,
.section .reveal h2,
.section .reveal p {
color: white;
margin-top: 50px;
}
.sub-section .reveal .state-background {
background: #E7E8EA
}
.sub-section .reveal h2,
.sub-section .reveal p {
color: #63717B;
margin-top: 50px;
}
.reveal strong {
color: #25679E;
}
.reveal .controls {
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<h1>Programación orientada a objetos</h1><p>Alejandro Reyes</p>
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<section data-transition="linear" data-transition-speed="default">
<h3>¿Qué es programación orientada a objetos?</h3>
<div class="slideContent" >
<p>El paradigma basado en, precisamente, “objetos”, que constan de:</p>
<ul>
<li>Datos (“fields”)</li>
<li>Procedimientos (“métodos”)</li>
</ul>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>¿Para qué es útil la programación orientada a objetos?</h3>
<div class="slideContent" >
<ul>
<li>Generación de nuevos métodos de objetos existentes </li>
<li>Las clases permiten que otros escriban métodos fácilmente</li>
</ul>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Programación orientada a objetos en R</h3>
<div class="slideContent" >
<ul>
<li>S3 (<a href="https://adv-r.hadley.nz/s3.html">https://adv-r.hadley.nz/s3.html</a>)</li>
<li>S4 (<a href="https://adv-r.hadley.nz/s4.html">https://adv-r.hadley.nz/s4.html</a>)</li>
<li>R6 (<a href="https://adv-r.hadley.nz/r6.html">https://adv-r.hadley.nz/r6.html</a>)</li>
</ul>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Motivación: función `plot`</h3>
<div class="mediaInline slideContent" >
<p>Un claro ejemplo es el uso de la función <code>plot()</code>:</p>
<pre><code class="r">x <- rnorm(100)
y <- x + rnorm(100)
class( x )
</code></pre>
<pre><code>[1] "numeric"
</code></pre>
<pre><code class="r">plot( x, y )
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Motivación: función `plot`</h3>
<div class="mediaInline slideContent" >
<p>Sin embargo, la función <code>plot()</code> también puede recibir una clase <code>lm</code> como input:</p>
<pre><code class="r">fit <- lm( x~y )
class( fit )
</code></pre>
<pre><code>[1] "lm"
</code></pre>
<pre><code class="r">plot( fit, 1 )
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-2"/></p>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Otro ejemplo: función `summary` </h3>
<div class="slideContent" >
<pre><code class="r">class(x)
</code></pre>
<pre><code>[1] "numeric"
</code></pre>
<pre><code class="r">summary(x)
</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
-2.14850 -0.64310 0.06988 0.11312 0.79790 2.38394
</code></pre>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Otro ejemplo: función `summary` </h3>
<div class="slideContent" >
<pre><code class="r">class(fit)
</code></pre>
<pre><code>[1] "lm"
</code></pre>
<pre><code class="r">summary(fit)
</code></pre>
<pre><code>
Call:
lm(formula = x ~ y)
Residuals:
Min 1Q Median 3Q Max
-2.03211 -0.40821 0.02086 0.37229 1.99048
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.03222 0.07401 -0.435 0.664
y 0.49644 0.05270 9.420 2.2e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7238 on 98 degrees of freedom
Multiple R-squared: 0.4752, Adjusted R-squared: 0.4698
F-statistic: 88.73 on 1 and 98 DF, p-value: 2.202e-15
</code></pre>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Definición de funciones S3</h3>
<div class="slideContent" >
<p>Todas estas funciones tienen el mismo contenido con una llamada a <code>UseMethod()</code>. Esto indica que son funciones <em>S3 generic</em>. <code>UseMethod()</code> busca qué funciones usar, dependiendo del input. </p>
<pre><code class="r">summary
</code></pre>
<pre><code>function (object, ...)
UseMethod("summary")
<bytecode: 0x7fb5194fad30>
<environment: namespace:base>
</code></pre>
<pre><code class="r">head( methods("summary") )
</code></pre>
<pre><code>[1] "summary.aov" "summary.aovlist"
[3] "summary.aspell" "summary.check_packages_in_dir"
[5] "summary.connection" "summary.data.frame"
</code></pre>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Definición de funciones S3</h3>
<div class="slideContent" >
<p>Un método <em>S3</em> consta en el nombre de una función genérica, un “.” y el nombre de la clase que también es el primer argumento de la función. Cuando hacemos:</p>
<pre><code class="r">summary( fit )
</code></pre>
<p>En realidad estamos invocando:</p>
<pre><code class="r">summary.lm( fit )
</code></pre>
<pre><code>
Call:
lm(formula = x ~ y)
Residuals:
Min 1Q Median 3Q Max
-2.03211 -0.40821 0.02086 0.37229 1.99048
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.03222 0.07401 -0.435 0.664
y 0.49644 0.05270 9.420 2.2e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7238 on 98 degrees of freedom
Multiple R-squared: 0.4752, Adjusted R-squared: 0.4698
F-statistic: 88.73 on 1 and 98 DF, p-value: 2.202e-15
</code></pre>
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<h3>Tres funciones útiles del paquete "sloop"</h3>
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<p>Tipo de función:</p>
<pre><code class="r">sloop::ftype(summary)
</code></pre>
<pre><code>[1] "S3" "generic"
</code></pre>
<p>Clase S3: </p>
<pre><code class="r">sloop::s3_class(fit)
</code></pre>