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<title>Beer & Wine Comparison among Canada, Mexico, and USA</title>
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<h1 class="title toc-ignore">Beer & Wine Comparison among Canada, Mexico, and USA</h1>
<h4 class="author"><em>Jeremy Kaufman, Alicia Ortigoza, Alexandra Reyes, and Daisy Rizo</em></h4>
<h4 class="date"><em>March 23, 2017</em></h4>
<div id="this-vignette-is-based-on-data-collected-from-package-fivethirtyeight-named-how-baby-boomers-get-high-by-mona-chalabi-available-here" class="section level2">
<h2>This vignette is based on data collected from package fivethirtyeight named, “How Baby Boomers Get High” by Mona Chalabi available <a href="http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/">here</a></h2>
<p>First, we load the required packages to reproduce the analysis.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(fivethirtyeight)
<span class="kw">library</span>(dplyr)
<span class="kw">library</span>(ggplot2)
<span class="kw">library</span>(readr)
<span class="kw">library</span>(knitr)
<span class="kw">library</span>(tidyr)
<span class="kw">library</span>(ggthemes)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">drink_Tidy <-<span class="st"> </span>drinks %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(country, beer_servings, wine_servings) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">gather</span>(-country, <span class="dt">key =</span> <span class="st">"drinks"</span>, <span class="dt">value =</span> <span class="st">"servings"</span>) </code></pre></div>
<!-- CHESTER
Again, I'm not sure why my suggestions weren't included and I wasn't asked any questions.
-->
<p>We first examined how many servings of wine and beer were consumed per person in each country, in the year 2010. During our analysis we focused on the consumption of alcohol by country, as well as the alcohol of choice between wine and beer. Our analysis only focuses on Canada, Mexico and the United States.</p>
<p>Our second analysis compares the number of liters of pure alcohol consumed by the selected countries. In order to provide a thorough analysis, a tidy data set was created.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">drink_UMC <-<span class="st"> </span>drink_Tidy %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">filter</span>(country %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"USA"</span>,<span class="st">"Mexico"</span>,<span class="st">"Canada"</span>))</code></pre></div>
</div>
<div id="how-many-servings-of-beer-and-wine-were-consumed-between-mexico-canada-and-the-united-states" class="section level2">
<h2>How many servings of beer and wine were consumed between Mexico, Canada, and the United States?</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> drink_UMC, <span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> country, <span class="dt">y =</span> servings))+
<span class="st"> </span><span class="kw">geom_col</span>(<span class="kw">aes</span>(<span class="dt">fill =</span> drinks) ,<span class="dt">position =</span> <span class="st">"dodge"</span>) +
<span class="st"> </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"blue"</span>, <span class="st">"red"</span>))+
<span class="st"> </span><span class="kw">labs</span>(<span class="dt">x=</span> <span class="st">"Countries"</span>, <span class="dt">Y =</span> <span class="st">"Servings "</span>, <span class="dt">title =</span><span class="st">"Country Comsumption Of Beer and Wine"</span>,<span class="dt">caption =</span> <span class="st">"Data from FiveThirtyEight"</span>)+
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>,
<span class="dt">plot.title =</span> <span class="kw">element_text</span>(<span class="dt">face =</span> <span class="st">"bold"</span>, <span class="dt">size =</span> <span class="dv">12</span>),
<span class="dt">plot.caption =</span> <span class="kw">element_text</span>(<span class="dt">hjust =</span> <span class="dv">1</span>, <span class="dt">size =</span> <span class="dv">10</span>))</code></pre></div>
<p><img 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" style="display: block; margin: auto;" /></p>
<p>Our findings show beer is the favored alcoholic beverage amongst all three countries being observed. Canada and Mexico consume about the same amount of beer, while the U.S. consumes more. Wine is the least consumed alcohol type amongst all three countries. In addition, Mexico is the country that drinks wine the least. The variable <code>beer_servings</code> corresponds to the number of beer servings, based on a 12-ounce can, whereas <code>wine_servings</code> corresponds to a glass of wine valued at 3.4-5 ounces. These are on a “per person” scale.</p>
<!-- CHESTER
These are per person values. It really isn't clear without including that.
-->
<p>This plot distinctly differentiates the most consumed alcohol types between wine and beer. Because the data is tidy, we can see the difference in alcohol consumption between all three countries.</p>
</div>
<div id="total-liters-of-pure-alcohol-consumed-per-continent" class="section level2">
<h2>Total Liters of Pure Alcohol Consumed Per Continent</h2>
<p>We want to figure out which country consumes the most liters of pure alcohol. We chose these three countries because they were the ones in our region.</p>
<!-- CHESTER
I'm really just not seeing significant revisions done on these two vignettes. It seems my requests were ignored?
-->
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">drink_tidy_con <-<span class="st"> </span>drinks %>%
<span class="st"> </span><span class="kw">filter</span>(country %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"Australia"</span>, <span class="st">"Belize"</span>, <span class="st">"Ethiopia"</span>, <span class="st">"France"</span>, <span class="st">"Japan"</span>, <span class="st">"USA"</span>))
<span class="kw">ggplot</span>(<span class="dt">data =</span> drink_tidy_con, <span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> country, <span class="dt">y =</span> total_litres_of_pure_alcohol)) +
<span class="st"> </span><span class="kw">geom_col</span>(<span class="kw">aes</span>(<span class="dt">fill =</span> country)) +
<span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">"Continents"</span>, <span class="dt">y =</span> <span class="st">"Liters"</span>, <span class="dt">title=</span><span class="st">"Liters Consumed By Contients"</span>,<span class="dt">caption =</span> <span class="st">"Data from FiveThirtyEight"</span>)+
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>,
<span class="dt">plot.title =</span> <span class="kw">element_text</span>(<span class="dt">face =</span> <span class="st">"bold"</span>, <span class="dt">size =</span> <span class="dv">16</span>),
<span class="dt">plot.caption =</span> <span class="kw">element_text</span>(<span class="dt">hjust =</span> <span class="dv">1</span>, <span class="dt">size =</span> <span class="dv">10</span>))</code></pre></div>
<p><img 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" style="display: block; margin: auto;" /></p>
<p>Our data set utilized six continents, excluding Antarctica. France, Australia, and the United States are amongst the top three continents that consume the most liters of pure alcohol. Japan, Belize, and Ethiopia are amongst the lowest three continents that consume liters of pure alcohol. While Australia and the U.S are fairly similar in alcohol consumption, so are Belize and Japan. Ethiopia is by far the lowest in pure alcohol consumption among the six continents observed. The low number of recorded liters of pure alcohol consumed by Ethiopia stands out quite a bit.</p>
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