There are many ways to query data with R. This post shows you three of the most common ways: Using DBI Using dplyr syntax Using R Notebooks Background Several recent package improvements make it easier for you to use databases with R. The query examples below demonstrate some of the capabilities of these R packages. DBI. The DBI specification has gone through many recent improvements. When working with databases, you should always use packages that are DBI-compliant.

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Today, we go back a bit to where we probably should have started in the first place, but it wouldn’t have been as much fun. In our previous work on volatility, we zipped through the steps of data import, tidy and transformation. Let’s correct that oversight and do some spade work on transforming daily asset prices to monthly portfolio log returns. Our five-asset portfolio will consist of the following securities and weights:

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A recent paper in nature climate change: Less than 2°C warming by 2100 unlikely (Raftery et al. 2017), concludes that the goal of the Paris Agreement is unlikely to be met. Although the conclusion is disheartening, the paper advances the science of climate modeling by developing a joint Bayesian hierarchical model for Gross Domestic Product per capita and carbon intensity. This ensemble of models, in turn, depends on the availability of probabilistic population projections developed by the BayesPop Project at the University of Washington and available on CRAN.

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Motivation One day earlier this year, I was faced with the challenge of creating a report for management. It had to be an MS Word document (corporate requirement, you know). It was supposed to be polished and use many of the standard MS Word features like headers, footers, table of contents, and styles. I am not a Word guy, and besides, I wanted to make a reproducible document that would make it easy for me to include R code and plots in the text.

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August was a relatively slow month for new R packages; “only” 180 new packages stuck to CRAN. Here are my “Top 40” picks organized into seven categories: Data, Machine Learning, Miscellaneous, Science, Statistics, Utilities and Visualizations. Although they have been written for specialized audiences, I have included the three “Science” packages because, in my layman’s opinion, they not only seem to be useful, but they are each documented well enough to give an interested person some idea of what they do.

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With roots dating back to at least 1662 when John Graunt, a London merchant, published an extensive set of inferences based on mortality records, survival analysis is one of the oldest subfields of Statistics [1]. Basic life-table methods, including techniques for dealing with censored data, were discovered before 1700 [2], and in the early eighteenth century, the old masters - de Moivre working on annuities, and Daniel Bernoulli studying competing risks for the analysis of smallpox inoculation - developed the modern foundations of the field [2].

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Editors Note: It has been heartbreaking watching the images from México City. Teresa Ortiz, co-organizer of R-Ladies CDMX reports on efforts of data scientists to help. Our thoughts are with them, and with the people of México. It has been a hard couple of days around here. In less than 2 weeks, México has gone through two devastating earthquakes and the damages keep adding. Nevertheless, the response from the citizens has been outstanding and Mexican data-driven initiatives have not stayed behind.

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Inside the enterprise, a dashboard is expected to have up-to-the-minute information, to have a fast response time despite the large amount of data that supports it, and to be available on any device. An end user may expect that clicking on a bar or column inside a plot will result in either a more detailed report, or a list of the actual records that make up that number. This article will cover how to use a set of R packages, along with Shiny, to meet those requirements.

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In our previous portfolio volatility work, we covered how to import stock prices, convert to returns and set weights, calculate portfolio volatility, and calculate rolling portfolio volatility. Now we want to break that total portfolio volatility into its constituent parts and investigate how each asset contributes to the volatility.

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Inspired by the Community One of the themes at useR 2017 in Brussels was “Get involved”. People were encouraged to contribute to the community, even when they did not consider themselves R specialists (yet). This could be by writing a package or a blog post, but also by simply correcting typos through pull requests, or sending a tweet about a successful analysis. Bottom line: get your stuff out in the open.

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