Two weeks ago, rstudio::conf 2018 was held in San Diego. We had 1,100 people attend the sold-out event. In this post, I summarize my experience of the talks on the topic of deep learning with R, including the keynote by J.J. Allaire. Keynote The keynote on the second day was ...

Introduction Companies using Google BigQuery for production analytics often run into the following problem: the company has a large user hit table that spans many years. Since queries are billed based on the fields accessed, and not on the date-ranges queried, queries on the table are billed for all available ...

Sometimes it appears to me that the invisible hand economists speak of guides the market for new R packages. Eight of the 129 new packages that stuck to CRAN in December fall under Computational Methods, a category I have only recently begun using. All of them made it into the list ...

Any programming environment should be optimized for its task, and not all tasks are alike. For example, if you are exploring uncharted mountain ranges, the portability of a tent is essential. However, when building a house to weather hurricanes, investing in a strong foundation is important. Similarly, when beginning a ...

In a recent post, I mentioned three avenues for working with TensorFlow from R: * The keras package, which uses the Keras API for building scaleable, deep learning models * The tfestimators package, which wraps Google’s Estimators API for fitting models with pre-built estimators * The tensorflow package, which provides an interface ...

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 ...

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 ...

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 programming (DCP) which allows ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

In a previous post, we reviewed how to import daily prices, build a portfolio, and calculate portfolio returns. Today, we will visualize the returns of our individual assets that ultimately get mashed into a portfolio. The motivation here is to make sure we have scrutinized our assets before they get ...

This guide shows how to automate the summary of surveys with R and R Markdown using RStudio. This is great for portions of the document that don’t change (e.g., “the survey shows substantial partisan polarization”). The motivation is really twofold: efficiency (maximize the reusabililty of code, minimize copying ...

It has never been easier to access data from R. Not only does there seem to be a constant stream of new packages that access the APIs of data providers, but it is also becoming popular for package authors to wrap up fairly large datasets into R packages. Below are 44 ...

There were so many interesting ideas among the 222 new packages that made it to CRAN in September that I found it exceptionally difficult to decide on the “Top 40” packages. In the end, I only managed to limit my selection to 40 by avoiding all packages that I would normally classify under “...