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<h1 class="title">R_Basic_Patterns</h1>
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</header>
<section id="common-patterns-for-data-analysis-workflows" class="level2">
<h2 class="anchored" data-anchor-id="common-patterns-for-data-analysis-workflows">3 Common Patterns for Data Analysis Workflows</h2>
<p>Here are 3 basic patterns using <code>dplyr</code> functions that you can use for practice. These patterns combine different verbs into common data analysis workflows.</p>
<p>For these examples, we will use the built-in <code>mtcars</code> dataset, which contains information about various car models. You can load it and the <code>dplyr</code> library with the following code:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>
Attaching package: 'dplyr'</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>The following objects are masked from 'package:stats':
filter, lag</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>The following objects are masked from 'package:base':
intersect, setdiff, setequal, union</code></pre>
</div>
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tibble)</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(mtcars)</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mtcars)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000 </code></pre>
</div>
</div>
</section>
<section id="pattern-1-basic-summary-and-filtering" class="level2">
<h2 class="anchored" data-anchor-id="pattern-1-basic-summary-and-filtering"><strong>Pattern 1: Basic summary and filtering</strong></h2>
<p>This is a fundamental pattern for exploring and summarizing a dataset. It involves:</p>
<ol type="1">
<li><p><strong>Filtering</strong> rows based on a condition.</p></li>
<li><p><strong>Selecting</strong> a few key columns.</p></li>
<li><p><strong>Grouping</strong> the data.</p></li>
<li><p><strong>Summarizing</strong> the groups.</p></li>
</ol>
<p><strong>The task:</strong> Find the average miles per gallon (MPG) and horsepower for 4 and 6-cylinder cars.</p>
<p><strong>The pattern:</strong></p>
<div class="cell">
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>mtcars <span class="sc">%>%</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(cyl <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">6</span>)) <span class="sc">%>%</span></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(mpg, hp, cyl) <span class="sc">%>%</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(cyl) <span class="sc">%>%</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> <span class="at">avg_mpg =</span> <span class="fu">mean</span>(mpg),</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> <span class="at">avg_hp =</span> <span class="fu">mean</span>(hp)</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 2 × 3
cyl avg_mpg avg_hp
<dbl> <dbl> <dbl>
1 4 26.7 82.6
2 6 19.7 122. </code></pre>
</div>
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mtcars)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000 </code></pre>
</div>
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(mtcars)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...</code></pre>
</div>
</div>
<p><strong>What is happening?</strong></p>
<ul>
<li><p><code>filter(cyl %in% c(4, 6))</code>: Filters the dataset to include only rows where the <code>cyl</code> (number of cylinders) is either 4 or 6.</p></li>
<li><p><code>select(mpg, hp, cyl)</code>: Keeps only the columns for MPG, horsepower, and cylinders. This is a good practice to avoid carrying unnecessary columns.</p></li>
<li><p><code>group_by(cyl)</code>: Prepares the data for aggregation by <code>cyl</code>, so the next operation will be performed separately for the 4-cylinder and 6-cylinder groups.</p></li>
<li><p><code>summarize(...)</code>: Calculates the <code>avg_mpg</code> and <code>avg_hp</code> for each group, collapsing the data into a single row per group.</p></li>
</ul>
</section>
<section id="pattern-2-creating-new-variables-and-sorting" class="level2">
<h2 class="anchored" data-anchor-id="pattern-2-creating-new-variables-and-sorting">Pattern 2: Creating new variables and sorting</h2>
<p>This pattern demonstrates how to transform data by creating new columns and then ordering the results. It involves:</p>
<ol type="1">
<li><p><strong>Creating</strong> a new column with <code>mutate()</code>.</p></li>
<li><p><strong>Filtering</strong> for a specific condition on the new column.</p></li>
<li><p><strong>Arranging</strong> the results.</p></li>
<li><p><strong>Renaming</strong> a column for better clarity.</p></li>
</ol>
<p><strong>The task:</strong> Create a new column for “weight per horsepower,” and find the top 5 cars with the lowest ratio for 8-cylinder engines.</p>
<p><strong>The pattern:</strong></p>
<div class="cell">
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>mtcars <span class="ot"><-</span> tibble<span class="sc">::</span><span class="fu">rownames_to_column</span>(mtcars, <span class="at">var =</span> <span class="st">"car_model"</span>)</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>mtcars <span class="sc">%>%</span></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">weight_per_hp =</span> wt <span class="sc">/</span> hp) <span class="sc">%>%</span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(cyl <span class="sc">==</span> <span class="dv">8</span>) <span class="sc">%>%</span></span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(weight_per_hp) <span class="sc">%>%</span></span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(car_model, weight_per_hp, hp) <span class="sc">%>%</span></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">head</span>(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> car_model weight_per_hp hp
1 Maserati Bora 0.01065672 335
2 Ford Pantera L 0.01200758 264
3 Duster 360 0.01457143 245
4 Camaro Z28 0.01567347 245
5 Hornet Sportabout 0.01965714 175</code></pre>
</div>
</div>
<p><strong>What is happening?</strong></p>
<p>The first function is to create a column out of the names of the type of car model.</p>
<ul>
<li><p>First we used the assignment operator to call the tibble library which was loaded at the top of our page. Then we used the rownames_to_column(mtcars, var = “car_model”) function to convert the row of names into a column and variable we called ‘car_model’ from the mtcars data set.</p></li>
<li><p><code>mutate(weight_per_hp = wt / hp)</code>: Creates the new <code>weight_per_hp</code> variable.</p></li>
<li><p><code>filter(cyl == 8)</code>: Focuses the analysis on 8-cylinder cars.</p></li>
<li><p><code>arrange(weight_per_hp)</code>: Sorts the rows in ascending order based on the new variable.</p></li>
<li><p><code>select(...)</code>: Selects the new variable and other relevant columns to present in the result.</p></li>
<li><p><code>head(5)</code>: Displays only the top 5 rows of the sorted result.</p></li>
</ul>
<p><strong>Pattern 3: Summarizing with unique values</strong></p>
<p>This is a common workflow for checking the unique combinations of categories in a dataset and then summarizing a numerical variable within those unique combinations. It involves:</p>
<ol type="1">
<li><p><strong>Grouping</strong> by multiple variables.</p></li>
<li><p><strong>Summarizing</strong> to get a count of unique combinations.</p></li>
<li><p><strong>Grouping</strong> again, this time for a deeper summary.</p></li>
<li><p><strong>Summarizing</strong> with another statistical function.</p></li>
<li><p><strong>Arranging</strong> to display the result clearly.</p></li>
</ol>
<p><strong>The task:</strong> Find the number of car models for each unique combination of cylinders and gear, and then calculate the average MPG for each of those groups.</p>
<p><strong>The pattern:</strong></p>
<div class="cell">
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>mtcars <span class="sc">%>%</span> </span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">distinct</span>(cyl, gear) <span class="sc">%>%</span> <span class="co"># to find the unique combinations of cylinders and gears</span></span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(cyl, gear) <span class="sc">%>%</span></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">print</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> cyl gear
1 4 3
2 4 4
3 4 5
4 6 3
5 6 4
6 6 5
7 8 3
8 8 5</code></pre>
</div>
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>mtcars <span class="sc">%>%</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(cyl, gear) <span class="sc">%>%</span></span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(</span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> <span class="at">model_count =</span> <span class="fu">n</span>(),</span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="at">avg_mpg =</span> <span class="fu">mean</span>(mpg),</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="at">.groups =</span> <span class="st">"drop"</span> <span class="co"># Drop grouping for subsequent operations</span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(cyl, gear)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 8 × 4
cyl gear model_count avg_mpg
<dbl> <dbl> <int> <dbl>
1 4 3 1 21.5
2 4 4 8 26.9
3 4 5 2 28.2
4 6 3 2 19.8
5 6 4 4 19.8
6 6 5 1 19.7
7 8 3 12 15.0
8 8 5 2 15.4</code></pre>
</div>
</div>
<p><strong>What is happening?</strong></p>
<ul>
<li><p><code>distinct(cyl, gear)</code>: The first block of code finds and prints all unique combinations of <code>cyl</code> and <code>gear</code> in the data, which can be a good way to understand the structure of your categorical variables.</p></li>
<li><p><code>group_by(cyl, gear)</code>: In the second block, it groups the data by both <code>cyl</code> and <code>gear</code> for the deeper summary.</p></li>
<li><p><code>summarize(...)</code>:</p>
<ul>
<li><p><code>model_count = n()</code>: Uses the special <code>n()</code> function to count the number of rows (i.e., car models) within each <code>cyl</code> and <code>gear</code> group.</p></li>
<li><p><code>avg_mpg = mean(mpg)</code>: Calculates the average MPG for each group.</p></li>
</ul></li>
<li><p><code>arrange(cyl, gear)</code>: Sorts the final table by <code>cyl</code> and <code>gear</code> for easy readability.</p></li>
</ul>
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