R / Finance 2017 starts next Friday, and once again, I am excited about going. It’s true that there are quite a few fun and informative R gatherings these days, but R / Finance is a “big deal” because it is the “real deal”. Finance has been, and remains, one of the driving applications underlying the R language. (A glance at the CRAN Task Views for Finance and Time Series will give you an idea of the depth and extent of the work done in this area since the early days of R.

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In previous posts, we built a map to access global ETFs and a simple Shiny app to import and forecast commodities data from Quandl. Today, we will begin a project that combines those previous apps. Our end goal is to build an interactive map to access macroeconomic data via Quandl, allowing the user to choose an economic indicator and click on a country to access that indicator’s time series.

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If you are a developer using the R environment to do your programming work, you are probably feeling left out and a bit segregated from the rest of the programming industry. It’s true that not many people know about the R language and what its uses are; however, things have been getting better in the past couple of years as more and more businesses are starting to implement R into their processes.

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Shiny Apps are becoming ubiquitous as a way for data scientists to present the results of an analysis, and also to engage with information consumers who may not be coders. The trend I see is that the greater the variety of skills and interests of the information consumers for any particular project, the more valued are interactive visualizations that can be integrated into enterprise-wide communication workflows. So, it is not surprising to see Shiny apps popping up in all manner of healthcare and medical applications.

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The 2017 New York R Conference was held last weekend in Manhattan. For the third consecutive year, the organizers - a partnership including Lander Analytics, The New York Meetup and Work-Bench - pulled off a spectacular event. There was a wide range of outstanding talks, some technical and others more philosophical, a palpable sense of community and inclusiveness, great food, beer and Bloody Marys. Fortunately, the talks were recorded. While watching the videos (which I imagine will be available in a couple of weeks) will be no substitute for the experience, I expect that the extended R community will find the talks valuable.

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an interactive Shiny app that lets the user choose a commodity, run a forecast and graph the results

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R’s startup behavior is incredibly powerful. R sets environment variables, loads base packages, and understands whether you’re running a script, an interactive session, or even a build command. Most R users will never have to worry about changing R’s startup process. In fact, for portability and reproducibility of code, we recommend that users do not modify R’s startup profile. But, for system administrators, package developers, and R enthusiasts, customizing the launch process can provide a powerful tool and help avoid common gotchas.

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Two hundred and sixteen new packages were added to CRAN in March. The following are my picks for the Top Forty, organized into five categories: Bioscience, Data, Data Science, Statistics and Utilities. Bioscience BioInstaller v0.0.3: Provides tools to install and download massive bioinformatics analysis software and database, such as NGS analysis tools with its required database or/and reference genome.

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Today, we will continue getting familiar with data from Quandl, but will also devote more time to expanding our dygraphs toolkit. We will be building up a data visualization in discrete pieces, which isn’t very efficient, but will make things easier when we move this project into production as a Shiny app. From a substantive perspective, we will examine the relationship between the price ratio of copper-gold and 10-year Treasure yields.

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At data.world, we are out to solve the “first mile problem of data science”: helping people obtain and understand the data sets they need. Everybody starts here whether they are analyzing their fantasy football league or working on the Zika pandemic. But unfortunately, as Professor Eric Schwartz at the University of Michigan said to me recently: “for so many people, the first mile of data science is the last mile, because people just quit when they see the data.

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