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(This article was first published on <strong><a href="http://www.r-exercises.com/2017/06/01/canada-labour-maket-future-perspectives-exercises/?utm_source=rss&utm_medium=rss&utm_campaign=canada-labour-maket-future-perspectives-exercises"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a rel="nofollow" href="https://i0.wp.com/www.r-exercises.com/wp-content/uploads/2017/05/glowing-216042__340.jpg" target="_blank"><img sizes="(max-width: 150px) 100vw, 150px" src="https://i0.wp.com/www.r-exercises.com/wp-content/uploads/2017/05/glowing-216042__340-150x150.jpg?resize=150%2C150" alt="" class="wp-image-6152" data-recalc-dims="1"></a>For anyone looking for job opportunities, it is nice to have an idea how the job market will perform in the future for your chosen career or industry. Many countries have open data sets that offer this kind of data. In these exercises we will use R to analyze the future perspective of Canadian labour market. </p>
<p>Answers to the exercises are available <a rel="nofollow" href="http://www.r-exercises.com/2017/06/01/canada-labour-market-future-perspectives-solutions/" target="_blank">here</a>.</p>
<p><strong>Exercise 1</strong><br>
Download and read into R all data sets from <a rel="nofollow" href="http://open.canada.ca/data/en/dataset/e80851b8-de68-43bd-a85c-c72e1b3a3890" target="_blank">Canadian Occupational Projection System (COPS) – 2015 to 2024 projections</a>.</p>
<p><strong>Exercise 2</strong><br>
Load library tidyr. Use <code>gather</code> to rearrange any occupation related data set that present time series data into a tidy data format. </p>
<p><strong>Exercise 3</strong><br>
Use <code>gather</code> to rearrange ALL other occupation related data sets that present time series data into a tidy data format, and pile out them in a unique data frame. </p>
<p><strong>Exercise 4</strong><br>
Remove lines that present NA values, columns in French, and the “X” in front every year. Take a look at your tidy data set. </p>
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<div><strong>Learn more</strong> about Data manipulation in the online course <a rel="nofollow" href="http://www.r-exercises.com/product/r-complete-data-analysis-solutions/" target="_blank"> R: Complete Data Analysis Solutions</a>. In this course you will learn how to:
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<p><strong>Exercise 5</strong><br>
Let’s do the same with industries data sets. Start by taking one of the industries data sets that present data in a time series an use <code>gather</code>. </p>
<p><strong>Exercise 6</strong><br>
Do the same procedure of exercise 5 to all other industries data sets. Pile out them in a new data frame. </p>
<p><strong>Exercise 7</strong><br>
Remove NAs, and French columns. In addition, set year and value as numeric, and take a look at your new tidy data set about industries.</p>
<p><strong>Exercise 8</strong><br>
Find out the industries that have que lowest number of jobseekers, and create a new data set by sub setting the previous one.</p>
<p><strong>Exercise 9</strong><br>
Plot the recently create data set using a line for each industry.</p>
<p><strong>Exercise 10</strong><br>
Create a similar plot for the top 5 occupations in terms of low amount of jobseekers.</p>
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<h3>Related exercise sets:</h3>
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<li><a rel="nofollow" href="http://www.r-exercises.com/2016/08/26/reshape-2-exercises/" title="Reshape 2 Exercises" target="_blank">Reshape 2 Exercises </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/2016/07/14/as-date-exercises/" title="As.Date() Exercises" target="_blank">As.Date() Exercises </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/2016/11/28/data-frame-exercises-vol-2/" title="Data frame exercises Vol. 2" target="_blank">Data frame exercises Vol. 2 </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/start-here-to-learn-r/" target="_blank">Explore all our (>1000) R exercises</a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/r-courses/" target="_blank">Find an R course using our R Course Finder</a> directory</li>
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