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(This article was first published on <strong><a href="https://fronkonstin.com/2018/06/17/how-much-money-should-machines-earn/"> R – Fronkonstin</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<blockquote>
<p>Every inch of sky’s got a star<br>
Every inch of skin’s got a scar<br>
(Everything Now, Arcade Fire)</p>
</blockquote>
<p>I think that a very good way to start with R is doing an interactive visualization of some open data because you will train many important skills of a data scientist: loading, cleaning, transforming and combinig data and performing a suitable visualization. Doing it interactive will give you an idea of the power of R as well, because you will also realise that you are able to <em>handle</em> indirectly other programing languages such as JavaScript.</p>
<p>That’s precisely what I’ve done today. I combined two interesting datasets:</p>
<ul>
<li>The probability of computerisation of 702 detailed occupations, obtained by Carl Benedikt Frey and Michael A. Osborne from the University of Oxford, using a Gaussian process classifier and published <a rel="nofollow" href="https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf" target="_blank">in this paper</a> in 2013.</li>
<li>Statistics of jobs from (employments, median annual wages and typical education needed for entry) from the US Bureau of Labor, available <a rel="nofollow" href="https://www.bls.gov/emp/ind-occ-matrix/occupation.xlsx" target="_blank">here</a>.</li>
</ul>
<p>Apart from using <code>dplyr</code> to manipulate data and <code>highcharter</code> to do the visualization, I used <code>tabulizer</code> package to extract the table of probabilities of computerisation from the <code>pdf</code>: it makes this task extremely easy.</p>
<p>This is the resulting plot:</p>
<p><br>
<iframe src="https://fronkonstin.com/wp-content/uploads/2018/06/machines_wage.html" width="100%" height="500"></iframe></p>
<p>If you want to examine it in depth, <a rel="nofollow" href="https://fronkonstin.com/wp-content/uploads/2018/06/machines_wage.html" target="_blank">here</a> you have a full size version.</p>
<p>These are some of my insights (its corresponding figures are obtained directly from the dataset):</p>
<ul>
<li>There is a <em>moderate</em> negative correlation between wages and probability of computerisation.</li>
<li>Around 45% of US employments are <em>threatened</em> by machines (have a computerisation probability higher than 80%): half of them do not require formal education to entry.</li>
<li>In fact, 78% of jobs which do not require formal education to entry are <em>threatened</em> by machines: 0% which require a master’s degree are.</li>
<li>Teachers are absolutely irreplaceable (0% are <em>threatened</em> by machines) but they earn a 2.2% less then the average wage (unfortunately, I’m afraid this phenomenon occurs in many other countries as well).</li>
<li>Don’t study for librarian or archivist: it seems a bad way to invest your time</li>
<li>Mathematicians will survive to machines</li>
</ul>
<p>The code of this experiment is available <a rel="nofollow" href="https://github.com/aschinchon/machines-and-wages" target="_blank">here</a>.</p>
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