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(This article was first published on <strong><a href="http://r-exercises.com/2016/06/16/summary-statistics-with-aggregate/"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="http://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a target="_blank" href="http://i1.wp.com/r-exercises.com/wp-content/uploads/2016/06/Addition_tricky_4.svg_.png"><img alt="Addition_tricky_4.svg" class="wp-image-875" src="http://i1.wp.com/r-exercises.com/wp-content/uploads/2016/06/Addition_tricky_4.svg_.png?resize=85%2C100" data-recalc-dims="1"></a>The <code>aggregate()</code> function subsets dataframes, and time series data, then computes summary statistics. The structure of the <code>aggregate()</code> function is <code>aggregate(x, by, FUN)</code>.</p>
<p>Answers to the exercises are available <a target="_blank" href="http://r-exercises.com/2016/6/16/summary-statistics-with-aggregate-solutions/">here</a>.</p>
<p><strong>Exercise 1</strong><br>
Aggregate the “<code>airquality</code>” data by “<code>airquality$Month</code>“, returning means on each of the numeric variables. Also, remove “<code>NA</code>” values.</p>
<p><strong>Exercise 2</strong><br>
Aggregate the “<code>airquality</code>” data by the variable “<code>Day</code>“, remove “<code>NA</code>” values, and return means on each of the numeric variables.</p>
<p><strong>Exercise 3</strong><br>
Aggregate “<code>airquality$Solar.R</code>” by “<code>Month</code>“, returning means of “<code>Solar.R</code>“. The header of column 1 should be “<code>Month</code>“. Remove “<code>not available</code>” values.</p>
<p><strong>Exercise 4</strong><br>
Apply the standard deviation function to the data aggregation from Exercise 3.</p>
<p><strong>Exercise 5</strong><br>
The structure of the <code>aggregate()</code> formula interface is <code>aggregate(formula, data, FUN)</code>.</p>
<p>The structure of the formula is <code>y ~ x</code>. The “<code>y</code>” variables are numeric data. The “<code>x</code>” variables, usually factors, are grouping variables, that subset the “<code>y</code>” variables.</p>
<p><code>aggregate.formula</code> allows for one-to-one, one-to-many, many-to-one, and many-to-many aggregation.</p>
<p>Therefore, use <code>aggregate.formula</code> for a one-to-one aggregation of “<code>airquality</code>” by the mean of “<code>Ozone</code>” to the grouping variable “<code>Day</code>“.</p>
<p><strong>Exercise 6</strong><br>
Use <code>aggregate.formula</code> for a many-to-one aggregation of “<code>airquality</code>” by the mean of “<code>Solar.R</code>” and “<code>Ozone</code>” by grouping variable, “<code>Month</code>“.</p>
<p><strong>Exercise 7</strong><br>
Dot notation can replace the “<code>y</code>” or “<code>x</code>” variables in <code>aggregate.formula</code>. Therefore, use “<code>.</code>” dot notation to find the means of the numeric variables in <code>airquality</code>“, with the grouping variable of “<code>Month</code>“.</p>
<p><strong>Exercise 8</strong><br>
Use dot notation to find the means of the “<code>airquality</code>” variables, with the grouping variables of “<code>Day</code>” and “<code>Month</code>“. Display only the first 6 resulting observations.</p>
<p><strong>Exercise 9</strong><br>
Use dot notation to find the means of “<code>Temp</code>“, with the remaining “<code>airquality</code>” variables as grouping variables.</p>
<p><strong>Exercise 10</strong><br>
<code>aggregate.ts</code> is the time series method for <code>aggregate()</code>.</p>
<p>Using <code>R</code>‘s built-in time series dataset, “<code>AirPassengers</code>“, compute the average annual standard deviation.</p>
<p>Image by Averater (Own work) [<a target="_blank" href="http://creativecommons.org/licenses/by-sa/3.0">CC BY-SA 3.0</a>], <a target="_blank" href="https://commons.wikimedia.org/wiki/File:Addition_tricky_4.svg">via Wikimedia Commons</a>.</p>
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