<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-5-data-visualisation-spatial-data/"></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/2016/12/26/descriptive-analytics-part-5-data-visualisation-spatial/"> 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?”.</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 eighth 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.In order to solve this set of exercises, you have to download <a target="_blank" rel="nofollow" href="http://stat-computing.org/dataexpo/2009/airports.csv">this</a> data set which provide us the coordinates of each airport.Please find the script used to create a merged dataset <a target="_blank" rel="nofollow" href="https://github.com/VasTsak/r-exercises/blob/master/spatial_data.R">here</a> . I don’t expect you to do the pre-processing yourself since it is beyond the scope of this set but I highly encourage you to give it a try, in case you did that with a better or more efficient way than I did, please post your solution at the comment section(it will be highly appreciated). Moreover we will remove the rows with missing values (various delays) because the methods that we will use are computationally expensive so having a big data set is just a waste of time. The goal of Descriptive analytics is to inform the user about what is going on at the dataset. A great way to do that fast and effectively is by performing data visualisation. Data visualisation is also a form of art, it has to be simple, comprehended and full of information. On this set of exercises we will explore different ways of visualising spatial using the famous <code>ggmap</code> package. Before proceeding, it might be helpful to look over the help pages for the <code> get_map</code>, <code>ggmap</code>, <code> facet_wrap</code>.</p>
<p>For this set of exercises you will need to install and load the packages <code> ggplot2</code>, <code>dplyr</code>, and <code>ggmap</code>.</p>
<p><code>install.packages('ggplot2')</code><br>
<code>library(ggplot2)</code><br>
<code>install.packages('dplyr')</code><br>
<code>library(dplyr)</code><br>
<code>install.packages('ggmap')</code><br>
<code>library(ggmap)</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>Answers to the exercises are available <a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/12/26/descriptive-analytics-part-5-data-visualisation-spatial-data-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>Query the map of United States using the get_map function.<br>
It is recommended to experiment with the various types of maps and select the one that you think is the best. (I have used the toner-lite from Stamen Maps.)</p>
<p><strong>Exercise 2</strong></p>
<p>Print the map that you have selected.</p>
<p><strong>Exercise 3</strong></p>
<p>Modify the printed map in order to print out a bigger image( extent) and assign it to a m object. </p>
<p><strong>Exercise 4</strong></p>
<p>Plot the destination airports of the flights on the map.</p>
<p><strong>Exercise 5</strong></p>
<p>Plot the destination airports of the flights on the map, the size of the points should be based on the number of flights that arrived to the destination airports.</p>
<p><strong>Exercise 6</strong></p>
<p>Plot the destination airports of the flights on the map, the colour of the points should be based on the number of flights that arrived to the destination airport. Make it a bit prettier, use the <code>scale_colour_gradient</code> and set the lows and the highs of your preferences.</p>
<p><strong>Exercise 7</strong></p>
<p>Plot the destination airports of the flights on the map, the colour of the points should be based on the number of flights that arrived to the destination airport and the size of the points should be based on the total delay of arrival of the flights that arrived at the destination airport.<br>
Something is not right here, right? </p>
<p><strong>Exercise 8</strong></p>
<p>Plot the destination airports of the flights on the map, the colour of the points should be based on the number of flights that arrived to the destination airport and the size of the points should be based on the total delay of arrival divided by the number of flights per destination.</p>
<p><strong>Exercise 9</strong></p>
<p>Plot the destination airports for everyday of the week (hint : <code>facet_wrap</code> ) </p>
<p><strong>Exercise 10</strong><br>
Plot the destination airports of the flights on the map, the colour of the points should be based on the number of flights that arrived to the destination airports, the size of the points should be based on the total delay of arrival of the flights that arrived at the destination airport for everyday of the week.<br>
(This may be a bit more challenging , if you can’t solve it go to the solutions and try to understand the reason I did what I did, if you have any questions please post them at the comment section).</p>
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