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jrtechs-NodeJSBlog
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<head>  <meta charset="utf-8">  <title>Introduction to R</title>      <meta name="apple-mobile-web-app-capable" content="yes" />  <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
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<section data-state="section" data-transition="linear" data-transition-speed="default"><h1>Introduction to R</h1><p>Jeffery Russell<br/>9-30-19</p>
<div class="mediaInline slideContent" ><h2>RIT Scientific Computing Group</h2>
<p><img src="data:image/png;base64,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</section><section data-transition="linear" data-transition-speed="default"><h3>History</h3><div class="mediaInline slideContent" ><ul><li>Back in the day (1976) Bell Laboratories created the S statistical programming language</li><li>People were sad because it was exclusively licensed by AT&amp;T</li><li>During the 90&#39;s a group of people developed a S replacement called R and it was licensed under GNU</li></ul>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABBgAAAQACAQAAADhbSIUAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAAmJLR0QA/4ePzL8AAAAHdElNRQfhCAoJGSmYYsPZAACAAElEQVR42uxdd5wUxRL+LhOOnHPOOYgkJYokyTmDgIqKTxAQREEEVFDBCAIGRIIgCggIEgSRnHPOGQ44wuVQ7w+O83anZ3dC9+zsbn/ze09uZqe7p2N1ddVXAZDwRoQiN3Igq8OVEeEIQVYEIzPCkAFARoS6SCMGsQAiQYhHFIBHSEAy7qc8uYdHeIQoROIhHuERHuABHiFWVr2EhISEfyJAVoHtkQ6FURiFUQC5kAd5kQt5kc1DZUlEJG7jNm7iJm7jFm6k/P9D2UwSEhISUmCQsBZBKIJSKI3iKIxCKIw8XlDmWNzGZVxMuS7gHOJkQ0pISEhIgUGCN7KgEsqiNEqhNEq6PEjwDiTjAk7hJE7iJA4hQjawhISEhBQYJIwiECVQBVVQCZVRzKe/9CoO4hAOYC/OyGaXkJCQkAKDhDaUwtOohadQCRn97ttvYwd2YBt2I0p2BAkJCQkpMEgokRNPoxZqoRayy8pAAnZgI9ZjJxJkZUhI+AjSISOyIBwZEI4nXlohCAdwH8kpI/9Rym8fIQHAIzzEg9R7ElJg8HPkQQM0QEOUl1XBQBQ2YQVW4IasCgkJr0EO5ENh5EN+ZEcO5Ej5/6wINJgeIRIP8BAP8RD3cR8P8QC3cB23cRvX8UBWuBQYfB250QANpaCgCcnYjWX4HSdlVUhI2BAZURIlUBIlUQKFUQDprcw8ODFLXK74LNF3j57dl3wTV3ARl3BTNooUGHwBwaiDlmiOKp6sW1c0DfH2NR/Yg5/xi9Q2SEjYAGEoj0qohCqogPy2K10sLuA0TuAkTuAE7sjmkgKDtyEvWqAFnkNWcVnkRm5kRTZkQ7aU/6ZHVqRDBmRGOoQjEFl0jLgYANGIw5PjxAdIQhTicQ/xiMIjxOE+YhGDB4jBPdzFPUSLr8UkrMMP+F3aNkhIeGTDUwm1URs1UAbBXlPqaziAg9iPAzibYishIQUG26IyOqI1qvGtzSwojCIoinzIj9zIhzzI7fERHJsiODz+311cxxVcxmX+RI/XMQff4qrsWhISliATGuAZ1EYNL/fduo9d2IGd2CnZX6TAYD/UQCd0RCk+TVEIpVEGZVAURVBYpKJCwCi9gku4isu4hCs4jUsg84kmYjmmYqfsZBISwhCCp9EUTfG0F+kTtOEUNmEjNklrBykw2KHenkZHdDRLuBSIkqiC8iiLMijtQ7QMMTiFUziJwziIM0gyk9Tf+BDrZIeTkOCMnGiNdmiCcJ/+SsJRbMRGbEakbHIpMHgC5dEbPVHIeAIZUQlVUBVVUMnHRysAROMIDmAPtuOY0ePFPZiAP2THk5DggsJoh3Z4FkF+9M1J2I+/sRFbJGWcFBisQi50Rx/UMPp6GdRGHdRFeb8aqf/hAXZhO/7BVsTof3krRmGr7IISEiaQDd3QB7XNJ5QeuZAHuZETuZEHuZAeWRGIrAhGJoQ66UpDEIB4AE+Mq4F7SMAjRCMO95GAB3iAe4hMve4hVlwNxGIDluMP6YslBQaRSIcX0BvNEWLk1dqoh9qogxyyHlPG7HZswHrs0XtYsQKjcUzWn4SEbgThefRFG6QzmkAelEbx1Cuv4K3FY86mG7iF27iKK7jM1xQhGbuwHMtxXHYMKTDwRnm8gl76bRCDUB1N0AT1rGU58SLcxkr8gb/06AgT8Q3GydNICQkdyIshGIh8+l8MRllUSTk+ze3xbcZlXMFlXMR5nMEpPgLEaSzHCmwzZ2YlBQaJxwhFB7yMBnpfK43n0AQNXdInSTxBDNbjFyzTLjbcwhj8IL2tJSQ0oDLeRHeE6XspO+qiHuqjpnGFhHA8xGmcwWmcxhEcM3LImXbnshw/Y4ucU6TAYBxFMBgvIo8+nUIdtEFblJa1pxtRWIb5+IuStPXI3XgNu2StSUi4QF2Mx3N6XsiCJmiG+ijvZQtDEs7iEI7gCA7inNF1/zIWYj4OyW4jBQa9dfI8XkULPZaJGdAML+AF5JK1ZwrX8R2+jruhZT+UjB8xGrdknUlIMFAdE9BK648DUR3Pozlq+wAZwyPsw27sxi6cN/L6YczHAlyWHUgKDFoQih4YjoraX0iPNuiOZtJOgeNuYRWm3tualdz3zUiMx9dIlHUmIZEGZTERHbTN7SFoiA5oa8TAwfaIwG7sxjZs1Rs9OxlbMB+/4p7sShLqyIpRuArSegVTC5pHD0hCBI5Th5uBCRra4QAPJzEJCR9BdnyBeC3zVxi1pZ/orh/MJQm0g6ZQK8pM0HPFYiEayi21BAtF8BkeaO1KAVSPvqJbclUXjMs04GpQtNv2SMIMaV8qIYEQvIE77uevQGpEs/1CVHBEIu2hT+g5CtMjNhzDG17F0i8hHBUwHwlaO1AeepvOyrXcMkTQgCuBcW7b5QZ6yI4s4ddoguPu56+yNJWu+Pmc8ohW0CtUVLvQEIXvUUt2MAmgAhYhSateoSktpni5hntA09D5RkCi2xZaxycImISE1yEfFrgbH6GJfegfOZmkwTH6lJ5JCEzWKDbsxSA/YPKXUEU5LNQqLOSmkXTaj4dWJC2intSIxtJquu+REpyg52/D3dCOwXt6Pc4lJLwcQRiKSNcjI3v0R8n3pITAxA36hmo/CNC2EtzHF2YDDkp4p7CwQKuwUIMWUJzfDqezNJ2aUEia+giiKvQaLaDLlpdlNz3t/oT2BBrK7i3hN6iK3a5HRJG7PyTFSbnADW7R1zFVr2nQYxISsADVZMfzH5TBfG3CQgC1pI1+OoCSaCu9TRVcT0XUk76hw5RkYbk2UPn7btotGT8gp+zmEj6P9PjItfVV0Tu/JyVLaUAzbtLEG3mua9pIrkVT2QF9H/nxrTYDxzDqT0ds1p2TaSGNpj8E2zc/pF+pH+XSYUmcjVrRZNpCMRbVwlzKEeumTLfRVzpESfg0muCMqzFQOOI3KSwY02TGdTwdosVnbg+6+mngYb9AFkxClLYFcDRds1033kA1UzUfFegl+om7t8ZF+pqe1+d05CRk1aNRtILuCK+LBzSKQtxpiTZKjm4JH0V2/ODKoifXg/mJUlgwg1j6KaLiOQ2a6DMYIln7fA9hGIYILYteFhpHkbbrvgepObO0+akzTac9lGByz76LxlJVw4KC8ijnsUBzRuhhxWl6wT31yjhpBCnhc+iOm+q9PmPsJ0mxcsXnglNJPS4Fu2eCuYqXESK7pa8gEH1wQctCF05jLNgd69/396VAtyVvSuPoL928k1G0nAZRPm6igvOVjipRBxpFc2gz3RBQN2uorHsjyCZyCEj4DIpilQtKpuRXE+/KdZ4r7tOUB7ki3c51p9EDgbJ7+sJJ30EtS1sGesuG3I13aQSl07FAB1F1Gkq/aKBmuUrfUmtKL0xUYF2ZqQZ1o/foZ9pJ9zjVUDx9Rlnc5bwQ+eVAkPB6BGOEq0PVOkkHLJ6fImgpvUYt6Ru67uMm4CuozgMNNPWtZSf1ZpTCYi0LWSgNpqu266Rx9K0u00PHK1/KYYXyUOAIfUT1KMBSUYFtKVKDOtMomkt7TEbkuE693X3PI4xHqBwQEl6M6q4cKLMnT7fQW+khraNRVCON5jOQ6tF0H+eQPEAd4t3Om9vRSHZVb0RWfAr3lMIUSL3pog0l2rlUmMuynJVa0STaTNEUQ3/SK1TI44IC+ypEjekl+oRW0AlDrBebqaJ7+b+uHBYSXjqbfaNugBdEr3PT2LkTFFbTCKpJQaqzaV36zIbzKT/spXbuN1urUVl2We9S3b2C21qWqSa0z4ad8k+qwn1BDqWMNhUVWHFAS1BzGkoLdR5OfEqZ3DE0fIdccnhIeBm64pp6r65H4g8iomkdjaG6FKzR6LkWTaFz3DdR++gz6krrbKBp6OjOqiwRXyG77LjegaY4oqVbV6BVNhQW9lATr1nY
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</section><section data-transition="linear" data-transition-speed="default"><h3>Why use R</h3><div class="slideContent" ><ul><li>Statistics and data analysis</li><li>Machine learning</li><li>Fast prototyping</li><li>Creating graphs</li><li>Writing research papers and reports</li></ul>
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</section><section data-transition="linear" data-transition-speed="default"><h3>R Markdown</h3><div class="slideContent" ><p>Using R markdown you can easily create reports and presentations by embedding your code in the report.This has major advantages.</p>
<ul><li>Easier to reproduce other people&#39;s research</li><li>Easy to re-run your analysis if the data changes &ndash; no more re-generating 10 charts and re-computing statistical tests</li><li> Easy to export to other formats</li></ul>
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</section><section data-transition="linear" data-transition-speed="default"><h3>Embedding Code Output in a Document</h3><div class="slideContent" ><pre><code class="r">summary(cars)</code></pre>
<pre><code>     speed           dist        Min.   : 4.0   Min.   :  2.00   1st Qu.:12.0   1st Qu.: 26.00   Median :15.0   Median : 36.00   Mean   :15.4   Mean   : 42.98   3rd Qu.:19.0   3rd Qu.: 56.00   Max.   :25.0   Max.   :120.00  </code></pre>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Embedding Graphs in a Document</h3><div class="slideContent" ><pre><code class="r">plot(mtcars$wt, mtcars$mpg, main=&quot;Weight vs MPG&quot;, xlab = &quot;weight&quot;, ylab=&quot;MPG&quot;)</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Syntax</h3><div class="slideContent" ><ul><li>Syntax of R is C-esk with its use of curly braces</li><li>Variables are similar to python since it infers your data type used</li><li>Type system is rather weird, the base unit for everything is a vector&ndash; even integers</li></ul>
<pre><code class="r">x &lt;- 0if (x &lt; 0) {  print(&quot;Negative number&quot;)} else if (x &gt; 0) {  print(&quot;Positive number&quot;)} else{  print(&quot;Zero&quot;)}</code></pre>
<pre><code>[1] &quot;Zero&quot;</code></pre>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Syntax: For Loops</h3><div class="slideContent" ><pre><code class="r">for(i in 1:5){  print(i)}</code></pre>
<pre><code>[1] 1[1] 2[1] 3[1] 4[1] 5</code></pre>
<pre><code class="r">for(i in (1:5)*2){  print(i)}</code></pre>
<pre><code>[1] 2[1] 4[1] 6[1] 8[1] 10</code></pre>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Syntax: While Loops</h3><div class="slideContent" ><pre><code class="r">x &lt;- 2
while(x == 2){  print(&quot;Stonks&quot;)  x = x + 1}</code></pre>
<pre><code>[1] &quot;Stonks&quot;</code></pre>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Syntax cont</h3><div class="mediaOnly slideContent" ><p><img src="data:image/jpeg;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Syntax: Arrays</h3><div class="slideContent" ><p>Arrays are 1 indexed.</p>
<pre><code class="r">for(i in c(1,4,5)){  print(i)}</code></pre>
<pre><code>[1] 1[1] 4[1] 5</code></pre>
<pre><code class="r">ar &lt;- c(1,3,9)
print(ar[2])</code></pre>
<pre><code>[1] 3</code></pre>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Getting Help</h3><div class="slideContent" ><p>Using the built in help command, you can view documentation for any function. </p>
<pre><code class="r">help(plot)</code></pre>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Making Graphs</h3><div class="slideContent" ><pre><code class="r">plot(x=1:10, y=(1:10)^2, xlab = &quot;x&quot;, ylab = &quot;y&quot;, main=&quot;Ez&quot;)</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>ML Example pt: 1</h3><div class="slideContent" ><pre><code class="r">ggplot(data = iris) +   theme(plot.title = element_text(hjust = 0.5)) +   ggtitle(&quot;Iris Flower Set&quot;) +   geom_point(mapping = aes(x=Sepal.Length, y=Petal.Length, color = Species)) +   labs(x = &quot;Sepal Length&quot;, y = &quot;Petal Length&quot;, color=&quot;Phase&quot;) +  theme_bw()</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>ML Example pt: 2</h3><div class="slideContent" ><pre><code class="r">ggplot(data = iris) + theme(plot.title = element_text(hjust = 0.5)) +   ggtitle(&quot;Iris Flow Length&quot;) +   geom_boxplot(mapping = aes(y=Petal.Length, x = Species), outlier.colour = &quot;red&quot;, outlier.shape = 1) +   labs(x = &quot;Flower Type&quot;, y = &quot;Petal Length&quot;) +  coord_flip() +  theme_bw()</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Super Cool ML Example pt: 3</h3><div class="slideContent" ><pre><code class="r">sc &lt;- spark_connect(master = &quot;local&quot;)
iris_tbl &lt;- sdf_copy_to(sc, iris, name = &quot;iris_tbl&quot;, overwrite = TRUE)
partitions &lt;- iris_tbl %&gt;%  sdf_partition(training = 0.7, test = 0.3, seed = 1111)
iris_training &lt;- partitions$trainingiris_test &lt;- partitions$test
dt_model &lt;- iris_training %&gt;%  ml_decision_tree(Species ~ .)
pred &lt;- ml_predict(dt_model, iris_test)
ml_multiclass_classification_evaluator(pred)</code></pre>
<pre><code>[1] 0.9451737</code></pre>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Resources</h3><div class="slideContent" ><ul><li><a href="https://jrtechs.net/data-science/r-programming-language">R Language Overview</a></li><li><a href="https://www.rstudio.com/">R Studio</a></li><li><a href="https://r4ds.had.co.nz/">R 4 Data Science</a></li></ul>
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Questions?</h3><div class="mediaOnly slideContent" ><p><img src="data:image/jpeg;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Workshop</h3><div class="mediaInline slideContent" ><p>Visualize the built in &ldquo;mpg&rdquo; data-set from the tidyverse library.</p>
<p>Step 1: Install Tidyverse package in R and include it in your R Script</p>
<pre><code># Install tidyverseinstall.packages(&quot;tidyverse&quot;)
# Include tidyverse in project
library(tidyverse)</code></pre>
<p><img src="data:image/jpeg;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Engine Size Vs MPG Normal Plot</h3><div class="mediaInline slideContent" ><pre><code class="r">plot(x = mpg$displ, y=mpg$hwy, main=&quot;Engine Size(Liters) vs MPG&quot;)</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Engine Size Vs MPG</h3><div class="mediaInline slideContent" ><pre><code class="r">ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy))</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Engine Size Vs MPG with color Mappings</h3><div class="mediaInline slideContent" ><pre><code class="r">ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy, color=class))</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAIAAAApSmgoAAAACXBIWXMAAAsSAAALEgHS3X78AAAgAElEQVR4nOzdeXxTVfo/8Odu2dO9hUIXWmnZN1mlioqgoOIoCCrOoDCuOI58/c3oqOOoo6ODOjriPrgAAm6IIIsooCAgOyh7oexd6b5ku0nu/f0RTFtIQtLmtsnl8/6D10k49+RJ2j45ObnnuYwsywQAAOrFtncAAACgLCR6AACVQ6IHAFA5JHoAAJXj2+ZhbDabJEn+/pdlWVmWFfpamGEYnuedTqcSgxMRx3Fut1uhwVmWZVnW5XIpND6CDzA4ESk6PoL3ied5SZICpIvzsSyr1+sVikcd2ijR2+32AL8ZOp1OFMWQfrTBEwRBq9XW1dUpMTgRGQwGm82m0OBarVan0yk3vtFoVG5wnU7HsmyUBm8wGBiGidLgTSaTJElRGnxMTIzb7bbb7cEfwnEcEn1gWLoBAFA5JHoAAJVDogcAUDkkegAAlUOiBwBQOSR6AACVY9qmqFngk7F4nne73QpFwrKsRqMJ6WytkAiCoOhJ+jzPOxwOhcZXNHie51mWFUVRofEVDV4QBCJSdHxFB5dlWbkdDIoGr9FoJEkKNXicXhnYRXEePc/zFotFicGJyGAwWK1WhQbXarUMwygXvNFoVG5wnU4nCEKUBu85jz5Kg/ecR6/cr6WiwXMcJ4oizqMPLyzdAACoHBI9AIDKIdEDAKgcEj0AgMoh0QMAqBwSPQCAyiHRAwCoHBI9AIDKIdEDAKhcG+2MbRs1RaeP7qohoqxL4xI6p7d3OAAAEUE9iV60NGz7foCnXfk9XTnxmNYU074hAQBEAvUs3VQWFjW9WXGqyF9PAICLinoSfUxSQrObKQn+egIAXFTUk+iNick5fX7wtLv2WmtO6tC+8QAARAj1rNETUdagvlmDKoiIqF87hwIAEDHUM6MHAACfkOgBAFQOiR4AQOWQ6AEAVA6JHgBA5ZDoAQBUDokeAEDlkOgBAFQOiR4AQOWQ6AEAVA6JHgBA5ZDoAQBUDokeAEDlkOgBAFQOiR4AQOWQ6AEAVA6JHgBA5ZDoAQBUDokeAEDlkOgBAFQOiR4AQOWQ6AEAVA6JHgBA5ZDoAQBUDokeAEDlkOgBAFQOiR4AQOWQ6AEAVA6JHgBA5ZDoAQBUDokeAEDlkOgBAFQOiR4AQOWQ6AEAVI5v7wDC6dQB3aGtJiLqPsyS0cPW3uEAAEQE9czobfWcJ8sT0aEtRmsd177xAABECPUkeru12XOxNajnqQEAtIZ6smFMgqvpzdhkl7+eAAAXFfWs0XOCPGJSVWG+jojSutl5QW7viAAAIoJ6Ej0R6YxS10ut7R0FAEBkUc/SDQAA+IREDwCgckj0AAAqh0QPAKBySPQAACqHRA8AoHJhOL2yvr7ebDa73e7XX3/dYrFkZGRMnTq19cMCAEBYtHZG/+OPP86ePZuItmzZkpaW9swzz5SWlhYWFoYjNgAACINWJfqysrL169d72gUFBdnZ2USUnZ1dUFAQhtAAACAcWr50I0nSnDlz/vCHPyxdupSIGhoakpKSiCgxMbGhocHT59///veiRYuI6IsvvvC8DbQXT2wKMRgMyg1OCgev1+uVG5yItFqtcoMrHbyi4ysdvKK/looGr9FoTCZT8P3dbrdywahDyxP9okWLrr32WrPZ7LlpNBorKyuzs7MrKytTUlI8d86YMWP69OlE5HK5Kisr/Q2l0+lEUZQkqcXBBMDzvMlkqqmpUWJwIjIYDFarUnUXNBqNTqerq6tTaHyj0WixWBQaXKfTCYJQX1+v0PiKBm8wGBiGUW58RYM3Go2SJNlsSl2SQdHgzWazKIoOhyP4QziOi4uLUygedWh5ordarcuXL3c4HEVFRd9++21OTs7x48cHDx588uTJvLw8Tx+dTqfT6Yiouro6QB6Xf9PiYC5IucGVjpyiNnilf6xKD05R+8p7H0K5kSMqeKWDUYGWJ/q7776biM6cOTN//vyxY8e63e4333zzlVde6dixY1paWtgCBACA1mnt6ZUpKSmPPvooEXEcN2PGjHCEBAAA4YQNUwAAKodEDwCgckj0AAAqh0QPAKBySPQAACqHRA8AoHJI9AAAKodEDwCgckj0AAAqh0QPAKBySPQAACqHRA8AoHJI9AAAKodEDwCgckj0AAAqh0QPAKBySPQAACqHRA8AoHJI9AAAKodEDwCgckj0AAAqh0QPAKBySPQAACqHRA8AoHJI9AAAKodEDwCgckj0AAAqh0QPAKBySPQAACqHRA8AoHJI9AAAKodEDwCgctGd6N2OCllytncUAAARjW/vAFpIcllKtt/XUPq9TJQ2bL4p9br2jggAIEJF64y+5vjchtLviYghKtzy+/YOBwAgckVroneL1d42Q0Qkt1soAACRLVoTfWz6eG877pJ7z2Z7AAA4T7Su0WtiemRfu62h9HvBkIkFegCAAKI10RORYMyKv+T+9o4CACDSRevSDQAABAmJHgBA5ZDoAQBUDokeAEDlkOgBAFQOiR4AQOWQ6AEAVC5az6OXJNq2Mq6unNfqpR55DSnpYntHBAAQoaJ1Rn/wZ1NdOU9EDhu7f6OpvcMBAIhc0ZroLTWNn0Wc9mh9FgAAbSBaU2RShsPb1hqkdowEACDCResafXZfm9POlp3Uag1S3xF17R0OAEDkitZET0Tdhli6DbG0dxQAAJEuWpduAAAgSEj0AAAqh0QPAKBySPQAACqHRA8AoHJI9AAAKodEDwCgclGc6Mv3P3fsu4Enf7pRtJ1u71gAACJXtCb6ysOzqg6/5bSeslduPb1+XHuHAwAQudpoZ6xOpwsUBM/zPC/LcvADni5b5W27bEVGo97fmxbLsizLGo3G4AcPiSAIDMMoNDjHcRzHKRq8coPzPK/0K6/o4EQUpcF7/pqiNHiO47RaLcdxCo1/cWqjRG+3291ut7//1el0oihKUgi1yTSmbtaK7Z42p020WGz+egqCwPO8xaJUsQSDwWC1WhUaXKvVMgyjXPBGo1G5wXU6nSAIURq8wWCI3lfeZDJJkqTcr6WiwXMcJ4qi3W4P6RC9Xq9QPOoQrUs3Hfq9oo/vT0S8LqXjgNfbOxwAgMgVtUXNWD7jqtXtHQQAQBSI1hk9AAAECYkeAEDlkOgBAFQOiR4AQOWQ6AEAVA6JHgBA5ZDoAQBULooTva2eO31QX1Goae9AAAAiWrRumKqv5jYvife0M3raug9VakM2AEC0i9YZfekxrbd96gDKXAAA+BWtiV7QhlDqEgDgYhatiT6tmz0pTfS0+19T177BAABEsmhdo+cF+dLRdQ4bK2gkFpWrAQD8i9ZE76HVh1DCHgDg4hStSzcAABAkJHoAAJVDogcAUDkkegAAlUOiBwBQOSR6AACVi9BEb2vg6qt4OeDuV1kS7TW/uuxlbRUUAEBUisTz6I/sNBzfYyCi5HSx/8h6hvWR792OiuLt91rLNxJRx0v/G5t5Z1tHCQAQJSJuRu90MJ4sT0TlpzVnTvmuQlx7cr4nyxNR6a4ZbRQcAKjU2rVrJ06c2N5RKCXiEr0sM01vSn62vkpu8ZzjlAoIACDKRVyi1+ikzjl2Tzsh1ZmcLvrsFpt5u7ed3OspIsZnNwAAn/7+97/n5OTk5OQsWLCg6f2PPvpoVlZWr169ZsyYIctyXV3djTfemJ6enpOTs2bNGiI6/57IF4lr9L0ub+iU43CJTGInJ8v5nqoLhoyuNxy2VmzWGNO1sX3aOEIAiGqLFy/esmXL/v37y8vLBw0aNGHCBM/9J06cyM/PP3LkiCzLvXv3fvDBB3/44YfExMTTp0+vW7du5cqVo0aNWrBgwTn3tO9zCUbEzeg94js4k9NFf1neg9PEmztdjywPAKFavXr13XffrdFoOnfufOTIEY3m7HeBXbp0ee+995YsWfL0008XFxfb7fYrrrhi48aNf/3rX91u98yZM4no/HsiX4QmegAA5bhcrrq6s9exqKiocDgcnvaGDRvGjh1bXV09derUIUOGEFHv3r337NnT
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Engine Size Vs MPG with shape Mappings</h3><div class="mediaInline slideContent" ><pre><code class="r">ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy, shape=class))</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Engine Size Vs MPG For each Class</h3><div class="mediaInline slideContent" ><pre><code class="r">ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy)) + facet_wrap(~ class, nrow=3)</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Engine Size Vs MPG With Line of Best Fit</h3><div class="mediaInline slideContent" ><pre><code class="r">ggplot(data = mpg) +   geom_point(aes(x=displ, y = hwy, color=class))+  geom_smooth(aes(x=displ, y = hwy))</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section><section data-transition="linear" data-transition-speed="default"><h3>Engine Size vs MPG With Line of Best Fit</h3><div class="mediaInline slideContent" ><pre><code class="r">ggplot(data = mpg, aes(x=displ, y = hwy, color=class)) +   geom_point()+  geom_smooth()</code></pre>
<p><img src="data:image/png;base64,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
</div>
</section>

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