<p>The <a href="https://earlconf.com/sanfrancisco/">EARL SF 2017</a> conference was just held June 5 - 7 in San Francisco, CA. There were some amazing presentations illustrating how R is truly being embraced in enterprises. We gave a <strong>three-part presentation</strong> on <code>tidyquant</code> for financial data science at scale, <code>timekit</code> for time series machine learning, and Business Science enterprise applications. We’ve uploaded the <a href="https://www.youtube.com/watch?v=8BYjv1AKI1Y">EARL presentation to YouTube</a>. Check out the presentation, and don’t forget to <a href="http://www.business-science.io/#announcements">check out our announcements</a> and to <a href="http://www.business-science.io/#social">follow us on social media</a> to stay up on the latest Business Science news, events and information!</p>
<h1>EARL 2017 Presentation</h1>
<p>If you’re interested in financial analysis, forecasting, and business applications, check out our 30 minute presentation from EARL SF 2017! The presentation is three-in-one:</p>
<ol>
<li>Financial data science at scale with <code>tidyquant</code> (0:45)</li>
<li>Time series machine learning with <code>timekit</code> (9:10)</li>
<li>Enterprise applications with Business Science (23:00)</li>
</ol>
<h2>Forecasting daily CRAN downloads</h2>
<p>One of the big areas of interest on <a href="https://twitter.com/hadleywickham/status/870624661616209922">twitter</a> leading up to the presentation was this tweet from Hadley showing growth in daily CRAN downloads are up to 1.25M per day:</p>
<blockquote class="twitter-tweet">
<p lang="en" dir="ltr">Total daily CRAN downloads for the RStudio mirror for the last 3 years. <a href="https://twitter.com/hashtag/rstats?src=hash">#rstats</a> <a href="https://t.co/Wo5zz3xZyc">pic.twitter.com/Wo5zz3xZyc</a></p>— Hadley Wickham (@hadleywickham) <a href="https://twitter.com/hadleywickham/status/870624661616209922">June 2, 2017</a>
</blockquote>
<p>…and our response showing that it’s quite possible to <strong>exceed 2M downloads per day</strong> by end of the year!</p>
<blockquote class="twitter-tweet">
<p lang="en" dir="ltr">What the future may bring... <a href="https://t.co/NObnNTXDcv">pic.twitter.com/NObnNTXDcv</a></p>— Matt Dancho (@mdancho84) <a href="https://twitter.com/mdancho84/status/870737108729835521">June 2, 2017</a>
</blockquote>
<h2>How we made the CRAN daily download forecast graph</h2>
<p>Several in the <a href="https://twitter.com/search?q=%23rstats&src=tyah">#rstats</a> community wanted to know how <a href="https://twitter.com/mdancho84/status/870737108729835521">this forecast</a> was made:</p>
<p></p>
<p>It turns out that it’s actually a combination (or ensemble) of four separate predictions:</p>
<ol>
<li>
<code>prophet</code> with linear growth</li>
<li>
<code>prophet</code> with logistic growth</li>
<li>
<code>timekit</code> using a linear regression on the time series signature</li>
<li>
<code>timekit</code> using a spline first to track trend and then a linear regression on the augmented data frame including the times series signature and the spline</li>
</ol>
<p>We first made a log transformation and then calculated the for separate models. The key takeaway is that individually, none of the forecasts was a silver bullet! Each had issues with either the training set or the test set. The <code>prophet</code> models tended to detect trend better while the <code>timekit</code> models tended to detect pattern better.</p>
<p></p>
<p>However, when combined via a simple average of the models, the ensemble prediction exhibited both low training and test error.</p>
<p></p>
<p>If you’d like to take a deep dive into the code, the <a href="https://github.com/business-science/presentations/blob/master/EARL_SF_2017/cran_dload_prediction.R">cran_dload_prediction.R</a> file is available for download on the <a href="https://github.com/business-science">Business Science GitHub site</a>.</p>
<h1>Download Presentation and Code on GitHub</h1>
<p>The slide deck and code from the EARL SF 2017 presentation can be downloaded from the <a href="https://github.com/business-science/presentations/tree/master/EARL_SF_2017">Business Science GitHub site</a>.</p>
<p></p>
<a href="https://github.com/business-science/presentations/tree/master/EARL_SF_2017">Download the EARL SF 2017 Presentation Slides!</a>
<h1>Announcements <a></a>
</h1>
<ul>
<li>
<p>We have completed the new package, <code>sweep</code>, which aims at “tidying” up the <code>forecast</code> workflow by applying <code>broom</code> concepts to the various model functions (<code>auto.arima()</code>, <code>ets()</code>, etc) and <code>forecast()</code> output. It’s not on CRAN quite yet, but we are encouraging testing. You can download from github: <code>devtools::install_github(&quot;business-science/sweep&quot;)</code>. Please provide feedback on the <a href="https://github.com/business-science/sweep">sweep github site</a>!</p>
</li>
<li>
<p>We are working on a name change of the <code>timekit</code> package. While we love the name, there’s also <a href="https://www.timekit.io/">timekit.io</a> that shares a product by the same name. To make it easier differentiating the software products, we are considering the change to <code>timekitr</code>. This transition is expected to take place in July.</p>
</li>
</ul>
<h1>Follow Business Science on Social Media <a></a>
</h1>
<ul>
<li>
<a href="https://twitter.com/bizScienc">@bizScienc</a> is on <a href="https://twitter.com/bizScienc">twitter</a>!</li>
<li>Check us out on <a href="https://www.linkedin.com/company/business.science">LinkedIn</a>!</li>
<li>Sign up for <a href="http://www.business-science.io/">our insights blog</a> to stay updated!</li>
<li>If you like our software, <a href="https://github.com/business-science">star our GitHub packages</a> :)</li>
</ul>