In the corporate world, spreadsheets and PowerPoint presentations still dominate as the tools used for analyzing and sharing information. So, it is not at all surprising that even when business analysts use R for the analytical heavy lifting, they frequently revert to using spreadsheets and slide decks to share their results. This may seem like the easiest way to communicate with colleagues, but any modestly complicated project is likely to be error-prone and generate hours of unnecessary rework.

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In the last meeting of 2016, the 40th in Madrid’s R Users Group five-year history, we had the opportunity to listen (via Skype) to a very interesting talk by Javier Luraschi, the main author of the package sparklyr. In our previous meeting, a colleague of the Community (José Luis Cañadas) made a first introduction to sparklyr. José Luis presented the processing capacities of sparklyr on a Spark instance, as well as an interface with the h2o package.

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Welcome to the first installation of reproducible finance for 2017. It’s a new year, a new President takes office soon, and we could be entering a new political-economic environment. What better time to think about a popular topic over the last few years: equity correlations. Elevated correlations are important for several reasons - life is hard for active managers and diversification gains are vanishing - but I personally enjoy thinking about them more from an inference or data exploration perspective.

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Here are a few things of some interest in the R world: The Gordon and Betty Moore Foundation has joined the R Consortium! This is a very positive development for the R Consortium and the R Community in general. The Moore Foundation has been a positive force for supporting technology that can make a real difference in people’s lives since its foundation in 2000 with an endowment of over $6 billion.

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Last month, 217 new packages were submitted to CRAN. By my count this was the highest monthly value recorded for the past nine years. Below, are brief descriptions of fifty-two of these new-for-December packages grouped into six categories: Data, Data Science, Financial Analysis, Statistics, Utilities and Visualizations. The Financial Analysis category is noteworthy. It is unusual to have six interesting packages centered on some financial application in a given month.

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The R package system continues to expand as the number of packages on CRAN is about to blow through the 10,000 mark sometime soon. This is astonishing! Not only are new packages arriving at a rate of about 190 per month, but CRAN itself continues to tick along like a well-oiled machine, 24 by 7. We all owe a great deal of gratitude to the CRAN maintainers, and to the Bioconductor team as well, who are maintaining more than 1,200 packages.

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Recently, I had the opportunity to interview RStudio’s Joe Cheng. Joe, the inventor and lead developer for Shiny, was the first person that J.J. Allaire invited to join the RStudio IDE project. We talked about those early days, how Shiny got started, Joe’s background as a software developer, his take on the R language and more. What follows is an edited transcript of our conversation. JBR: Hello Joe, thank you for being with us today.

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The 2017 R Conference season will get off to an early start on January 13th and 14th with RStudio::Conf 2017 in Orlando, Florida. The schedule promises an intense but collegial experience with plenty of hands-on practice working with R and the RStudio tool chain of packages and products. To prepare for the conference, I thought I would try to extract the major themes from the list of talks and workshops and point out some resources to study ahead of time.

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At RStudio, we work with many companies interested in scaling R. They typically want to know: How can R scale for big data or big computation? How can R scale for a growing team of data scientists? This post provides a framework for answering both questions. Scaling R for Big Data or Big Computation The first step to scaling R is understanding what class of problems your organization faces.

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In a previous post, we built an R Notebook that laid the groundwork for a Shiny app that allows users to graph country ETFs by clicking on a world map. In today’s Fun Friday post, we’ll charge forth to build that app, again using a flexdashboard so that we can stay in the Rmarkdown schema. Devoted readers of this blog know that I have a predilection for the Notebook-to-flexdashboard workflow, for reasons of efficiency and reproducibility.

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