<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/descriptive-analytics-part-6-interactive-dashboard-12/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://r-exercises.com/2017/01/03/descriptive-analytics-part-6-interactive-dashboard-12/"> 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" rel="nofollow" href="https://i0.wp.com/r-exercises.com/wp-content/uploads/2016/10/download.jpg?resize=278%2C181"><img alt="download" class="wp-image-2350" src="https://i0.wp.com/r-exercises.com/wp-content/uploads/2016/10/download.jpg?resize=278%2C181" data-recalc-dims="1"></a>Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”.As this series of exercises comes to an end, the last part is going to be the development of a data product. Not everybody is able to code in R, so it is useful to be able to make GUIs in order to share your work with non-technical people. This part may be a little challenging, since it requires some basic knowledge of the <code>shiny</code> package. The outcome of this set of exercises will be almost like <a target="_blank" rel="nofollow" href="https://vasileiostsakalos.shinyapps.io/descriptive_analysis_pt1/">this</a> web app (some variables are missing because I had to reduce the size of the data set). </p>
<p>In order to be able to solve this set of exercises you should have solved the <a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/10/19/descriptive-analytics-part-0-data-exploration/">part 0</a>, <a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/10/26/descriptive-analytics-part-1-data-formatting/">part 1</a>, <a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/11/02/descriptive-analytics-part-2-data-imputation/">part 2</a>,<a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/11/10/descriptive-analytics-part-3-outlier-treatment/">part 3</a>, and <a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/11/21/descriptive-analytics-part-4-data-manipulation/">part 4</a> of this series but also you should run this <a target="_blank" rel="nofollow" href="https://github.com/VasTsak/r-exercises/blob/master/descriptive-analytics-part-3-completed.R">script</a> which contain some more data cleaning. In case you haven’t, run this <a target="_blank" rel="nofollow" href="https://github.com/VasTsak/r-exercises/blob/master/data_visualisation_prerequisite.R"> script</a> in your machine which contains the lines of code we used to modify our data set. This is the ninth set of exercise of a series of exercises that aims to provide a descriptive analytics solution to the ‘2008’ data set from <a target="_blank" rel="nofollow" href="http://stat-computing.org/dataexpo/2009/the-data.html">here</a>. This data set which contains the arrival and departure information for all domestic flights in the US from 2008 has become the “iris” data set for Big Data. The goal of Descriptive analytics is to inform the user about what is going on at the dataset. Before proceeding, it might be helpful to look over the help pages for the <code> fluidPage</code>, <code>pageWithSidebar</code>, <code> headerPanel</code> , <code> sidebarPanel</code>, <code> selectInput</code>, <code> mainPanel</code>, <code> tabPanel</code>, <code> verbatimTextOutput</code>, <code> renderPrint</code>, <code> shinyApp</code>.</p>
<p>For this set of exercises you will need to install and load the package <code> shiny</code>.</p>
<p><code>install.packages('shiny')</code><br>
<code>library(shiny)</code></p>
<p>I have also changed the values of the <code>DaysOfWeek</code> variable, if you wish to do that as well the code for that is :<br>
<code>install.packages('lubridate')</code><br>
<code>library(lubridate)</code><br>
<code>flights$DayOfWeek &lt;- wday(as.Date(flights$Full1_Date,'%m/%d/%Y'), label=TRUE)</code></p>
<p>Because the app requires some time to run, I have also removed the rows with missing values from the data set just to save some time.</p>
<p><code>flights &lt;-flights[which(!is.na(flights['WeatherDelay'])),]</code><br>
<code>flights &lt;-flights[which(!is.na(flights['ArrDelay'])),] </code></p>
<p>Answers to the exercises are available <a target="_blank" rel="nofollow" href="http://r-exercises.com/2017/01/03/descriptive-analytics-part-6-interactive-dashboard-12-solutions">here</a>.</p>
<p>If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.</p>
<p><strong>Exercise 1</strong></p>
<p>Create the user interface and set as the header of the web app : “Descriptive Analysis” </p>
<p><strong>Exercise 2</strong></p>
<p>Create a side panel.</p>
<p><strong>Exercise 3</strong></p>
<p>Create a select list input control that contains the functions : <code>summary</code>, <code>str</code>, <code>head</code>, <code>tail</code>, <code>names</code>, <code>summary</code>.</p>
<p><strong>Exercise 4</strong></p>
<p>Create a select list input control that contains the functions : <code>mean</code>, <code>median</code>, <code>max</code>, <code>min</code>, <code>range</code>, <code>sd</code>.</p>
<p><strong>Exercise 5</strong></p>
<p>Create a select list input control that contains the variables : <code>ActualElapsedTime</code>, <code>CRSElapsedTime</code>, <code>AirTime</code>, <code>ArrDelay</code>, <code>DepDelay</code>, <code>TaxiIn</code>, <code>TaxiOut</code>.</p>
<p><strong>Exercise 6</strong></p>
<p>Create a main panel.</p>
<p><strong>Exercise 7</strong></p>
<p>Create in the main panel two tabs named “Content” and “Measures” that will contain the output of the functions of exercise 3 and exercise 4 respectively.</p>
<p><strong>Exercise 8</strong></p>
<p>Now that we are done with the user interface, create the server side of the app and the output that is supposed to print the functions of the exercise 3. (please check out the first tab of the app, to make things more clear). </p>
<p><strong>Exercise 9</strong></p>
<p>Create the output of the second tab, combining the functions of exercise 4 and the variables from the exercise 5.(please check out the second tab of the app, to make things more clear).</p>
<p><strong>Exercise 10</strong></p>
<p>Launch the app. </p>
<p>
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