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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2018/09/earl-london-2018.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>During the <a rel="nofollow" href="https://earlconf.com/2018/london/" target="_blank">EARL</a> (Enterprise Applications of the R Language) conference in London last week, the organizers asked me how I thought the conference had changed over the years. (This is the conference's fifth year, and I'd been to each one.) My response was that it reflected the increasing <em>maturity</em> of R in the enterprise. The early years featured many presentations that were about using R in research and the challenges (both technical and procedural) for integrating that research into the day-to-day processes of the business. This year though, just about every presentation was about R in <strong>production</strong>, as a mainstream part of the operational infrastructure for analytics. </p>
<p>That theme began in earnest with Garret Grolemund's <a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Garrett_Grolemund.pdf" target="_blank">keynote presentation</a> on the consequences of scientific research that can't be replicated independently. (This <a rel="nofollow" href="https://twitter.com/revodavid/status/1039808394025205760/photo/1" target="_blank">slide</a>, based on this <a rel="nofollow" href="https://www.ncbi.nlm.nih.gov/pubmed/26978209" target="_blank">2016 JAMA paper</a> was an eye-opener for me.) The R language has been at the forefront of providing the necessary tools and infrastructure to remove barriers to reproducible research in science, and the <a rel="nofollow" href="https://rmarkdown.rstudio.com/" target="_blank">RMarkdown</a> package and its streamlined integration in RStudio, is particularly helpful. Garret's keynote was recorded but the video isn't yet available; when it's published I highly recommend taking the time to watch this excellent speech.</p>
<p>The rest of the program was jam-packed with a variety of applications of R in industry as well. I couldn't see them all (it was a three-track conference), but every one I attended demonstrated mature, production-scale applications of R to solve difficult business problems with data. Here are just a few examples:</p>
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<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Martin_Chan.pdf" target="_blank">Rainmakers</a>, a market research firm, uses R to estimate the potential market for new products. They make use of the officer package to automate the generation of Powerpoint reports.</li>
<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Neil_Farricker.pdf" target="_blank">Geolytix</a> uses R to help companies choose locations for new stores that maximize profitability while reducing the risk of cannibalizing sales from other nearby locations. They use SQL Server ML Services to deploy these models to their clients.</li>
<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Dan_Erben.pdf" target="_blank">Dyson</a> uses R (and the prophet package) to forecast the expected sales of new models of vacuum cleaners, hand dryers, and other products, so that the manufacturing plants can ramp up (or down) production as needed.</li>
<li><a rel="nofollow" href="https://earlconf.com/2018/london/#eric-bahna" target="_blank">Google</a> uses R to design and analyze the results of customer surveys, to make the best decisions of which product features to invest in next.</li>
<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Chris_Billingham.pdf" target="_blank">N Brown Group</a>, a fashion retailer, uses R to analyze online product reviews from customers.</li>
<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Catie_Gamble.pdf" target="_blank">Marks and Spencer</a> uses R to increase revenues by optimizing the products shown and featured in the online store.  </li>
<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Alexis_Iglauer.pdf" target="_blank">PartnerRe</a>, the reinsurance firm, has build an analytics team around R, and uses Shiny to deploy R-based applications throughout the company.</li>
<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Dzidas_Martinatis.pdf" target="_blank">Amazon Web Services</a> uses containerized applications with R to identify customers who need additional onboarding assistance or who may be dissatisfied, and to detect fraud.</li>
<li><a rel="nofollow" href="https://earlconf.com/_downloads/london_speakers/EARL2018_-_London_-_Paul_Swiontkowski.pdf" target="_blank">Microsoft</a> uses R and Spark (via sparklyr in HDInsight) to support marketing efforts for Xbox, Windows and Surface with propensity modeling, to identify who is most likely to respond to an offer. </li>
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<p>You can see may more examples at the list of speakers linked below. (I <a rel="nofollow" href="http://blog.revolutionanalytics.com/2018/09/not-hotdog-a-shiny-app-using-the-custom-vision-api.html" target="_blank">blogged about my own talk at EARL</a> earlier this week.) Click through to see detailed summaries and (in most cases) a link to download slides.</p>
<p>EARL London 2018: <a rel="nofollow" href="https://earlconf.com/2018/london/#speakers" target="_blank">Speakers</a> </p>
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