Publications by R Views
Using Shiny with Scheduled and Streaming Data
Shiny applications are often backed by fluid, changing data. Data updates can occur at different time scales: from scheduled daily updates to live streaming data and ad-hoc user inputs. This article describes best practices for handling data updates in Shiny, and discusses deployment strategies for automating data updates. This post builds off o...
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October 2017 New Packages
Of the 182 new packages that made it to CRAN in October, here are my picks for the “Top 40”. They are organized into eight categories: Engineering, Machine Learning, Numerical Methods, Science, Statistics, Time Series, Utilities and Visualizations. Engineering is a new category, and its appearance may be an early signal for the expansion of R...
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CVXR: An R Package for Disciplined Convex Optimization
At long last, we are pleased to announce the release of CVXR! First introduced at useR! 2016, CVXR is an R package that provides an object-oriented language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to formulate convex optimization problems in a natural mathematical syntax, then automatically verifi...
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How to Show R Inline Code Blocks in R Markdown
Inline code with R Markdown R Markdown is a well-known tool for reproducible science in R. In this article, I will focus on a few tricks with R inline code. Some time ago, I was writing a vignette for my package WordR. I was using R Markdown. At one point I wanted to show `r expression` in the output, exactly as it is shown here, as an inline cod...
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Connecting R to Keras and TensorFlow
It has always been the mission of R developers to connect R to the “good stuff”. As John Chambers puts it in his book Extending R: One of the attractions of R has always been the ability to compute an interesting result quickly. A key motivation for the original S remains as important now: to give easy access to the best computations for und...
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Introduction to Skewness
In previous posts here, here, and here, we spent quite a bit of time on portfolio volatility, using the standard deviation of returns as a proxy for volatility. Today we will begin to a two-part series on additional statistics that aid our understanding of return dispersion: skewness and kurtosis. Beyond being fancy words and required vocabulary ...
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A Data Science Lab for R
In a previous post I described the role of analytic administrator as a data scientist who: onboards new tools, deploys solutions, supports existing standards, and trains other data scientists. In this post I will describe how someone in that role might set up a data science lab for R. Architecture A data science lab is an environment for develop...
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Nov 2017: New Package Picks
Two hundred thirty-seven new packages made it to CRAN in November. Here are my picks for the “Top 40” organized into the categories: Computational Methods, Data, Data Science, Science, Social Science, Utilities and Visualizations. Computational Methods CVXR v0.94-4: Implements an object-oriented modeling language for disciplined convex progr...
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Downtime Reading
Not everyone has the luxury of taking some downtime at the end the year, but if you do have some free time, you may enjoy something on my short list of downtime reading. The books and articles here are not exactly “light reading”, nor are they literature for cuddling by the fire. Nevertheless, you may find something that catches your eye. The...
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Introduction to Kurtosis
Happy 2018 and welcome to our first reproducible finance post of the year! What better way to ring in a new beginning than pondering/calculating/visualizing returns distributions. We ended 2017 by tackling skewness, and we will begin 2018 by tackling kurtosis. Kurtosis is a measure of the degree to which portfolio returns appear in the tails of o...
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