<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/reshape-2-exercises/"></div></div><div><a data-url="https://www.r-bloggers.com/reshape-2-exercises/" data-counturl="https://www.r-bloggers.com/reshape-2-exercises/" data-count="horizontal" data-text="Reshape 2 Exercises" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://r-exercises.com/2016/08/26/reshape-2-exercises/"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a target="_blank" href="http://r-exercises.com/wp-content/uploads/2016/08/ice-cubes-1224804_960_720.jpg?132f69"><img sizes="(max-width: 150px) 100vw, 150px" alt="ice-cubes-1224804_960_720" class="wp-image-1338" src="https://i1.wp.com/r-exercises.com/wp-content/uploads/2016/08/ice-cubes-1224804_960_720-150x150.jpg?resize=150%2C150" data-recalc-dims="1"></a>The <code>Reshape 2</code> package is based on differentiating between identification variables, and measurement variables. The functions of the <code>Reshape 2</code> package then “<code>melt</code>” datasets from wide to long format, and “<code>cast</code>” datasets from long to wide format.</p>
<p>Required package:<br>
<code>library(reshape2)</code></p>
<p>Answers to the exercises are available <a target="_blank" href="http://r-exercises.com/2016/08/26/reshape-2-solutions/">here</a>.</p>
<p><strong>Exercise 1</strong></p>
<p>Set a variable called “<code>moltenMtcars</code>“, by using the <code>melt()</code> function to format “<code>mtcars</code>” to long format using the id variables, “<code>cyl</code>” and “<code>gear</code>“.</p>
<p><strong>Exercise 2</strong></p>
<p>Set a variable, “<code>CarSurvey</code>“, using <code>dcast()</code> to reformat “<code>moltenMtcars</code>” to wide format, with “<code>cyl</code>” and “<code>gear</code>” in the first two columns. The aggregation function is “<code>mean</code>“.</p>
<p><strong>Exercise 3</strong></p>
<p>Using the <code>melt()</code> function, format “<code>airquality</code>” with 1 measurement per Month/Day date. Set a variable called “<code>weatherSurvey</code>“.</p>
<p><strong>Exercise 4</strong></p>
<p>Specify the name of “<code>weatherSurvey</code>” column 4 as “<code>Condition</code>“, and the name of column 5 as “<code>Measurement</code>“, using the <code>melt()</code> formula in Exercise 3.</p>
<p><strong>Exercise 5</strong></p>
<p>Use <code>dcast()</code> to format “<code>weatherSurvey</code>” from long to wide, with <code>Month</code> and <code>Day</code> as the first 2 columns. Set a new variable, “<code>airqualityEdit</code>“.</p>
<p><strong>Exercise 6</strong></p>
<p><code>acast()</code> converts a long-format “<code>molten</code>” data frame into a wide-format vector/matrix/array.</p>
<p>Set a new variable, “<code>AirQualityArray</code>“, via using <code>acast()</code> to re-format, “<code>weatherSurvey</code>“, by <code>Day</code>, <code>Month</code>, and <code>Condition</code>.</p>
<p><strong>Exercise 7</strong></p>
<p>Use the <code>acast()</code> function to get the means of “<code>weatherSurvey</code>” measurement variables by month. Also, remove not available values.</p>
<p><strong>Exercise 8</strong></p>
<p>Use the “<code>margins =</code>” parameter of <code>acast()</code> in order to include the means of all measurement variables in the formula from Exercise 7.</p>
<p><strong>Exercise 9</strong></p>
<p>Use the <code>recast()</code> function to combine the <code>melt()</code> operation from Exercise 1, and the <code>dcast()</code> operation from Exercise 2.</p>
<p><strong>Exercise 10</strong></p>
<p>Use the <code>recast()</code> function to combine the <code>melt()</code> operation from Exercise 4, and the <code>dcast()</code> operation from Exercise 5. Return the first 5 rows.</p>
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