I have frequent conversations with R champions and Systems Administrators responsible for R, in which they ask how they can measure and analyze the usage of their servers. Among the many solutions to this problem, one of the my favourites is to use an RRD database and RRDtool. From Wikipedia: ...

The World Cup starts today! The tournament which runs from June 14 through July 15 is probably the most popular sporting event in the world. if you are a soccer fan, you know that learning about the players and their teams and talking about it all with your friends greatly enhances the ...

In a previous post, we reviewed how to set up and run a Monte Carlo (MC) simulation of future portfolio returns and growth of a dollar. Today, we will run that simulation many, many, times and then visualize the results. Our ultimate goal is to build a Shiny app that ...

Today, we change gears from our previous work on Fama French and run a Monte Carlo (MC) simulation of future portfolio returns. Monte Carlo relies on repeated, random sampling. We will sample based on two parameters: mean and standard deviation of portfolio returns. Our long-term goal (long-term == over the next ...

In a previous post, I showed a very simple example of using the R function tools::CRAN_package_db() to analyze information about CRAN packages. CRAN_package_db() extracts the metadata CRAN stores on all of its 12,000 plus packages and arranges it into a “database”, actually a complicated data frame ...

Below are my “Top 40” picks from the approximately 212 new packages that made it to CRAN in April. They are organized into ten categories: Computational Methods, Data, Data Science, Machine Learning, Music, Science, Statistics, Time Series, Utilities, and Visualizations. Computational Methods diffeqr v0.1.1: Provides an interface to DifferentialEquations.jl which offers ...

This is a second post in a series on enterprise dashboards. See our previous post, Enterprise-ready dashboards with Shiny Databases. We have been living with spreadsheets for so long that most office workers think it is obvious that spreadsheets generated with programs like Microsoft Excel make it easy to understand ...

rstudio::conf 2018 and the New York R Conference are both behind us, but we are rushing headlong into the season for conferences focused on the R Language and its applications. The European R Users Meeting (eRum) begins this coming Monday, May 14th, in Budapest with three days of workshops and ...

In a previous post, we reviewed how to import the Fama French 3-Factor data, wrangle that data, and then regress our portfolio returns on the factors. Please have a look at that previous post, as the following work builds upon it. For more background on Fama French, see the original ...

By my count, just over 200 new packages made it to CRAN and stuck during March. The trend for specialized, and sometimes downright esoteric science packages continues. I counted 40 new packages in this class. Most, but not all of these, are focused on bio-science applications. For example, the foreSIGHT package profiled ...

I was surprised by greta. I had assumed that the tensorflow and reticulate packages would eventually enable R developers to look beyond deep learning applications and exploit the TensorFlow platform to create all manner of production-grade statistical applications. But I wasn’t thinking Bayesian. After all, Stan is probably everything ...

RStudio recently announced the reticulate package, which is designed to help R users inter-operate with Python code. I was immediately excited by this announcement. In a past life, I worked with a team at the National Renewable Energy Lab (NREL) on vehicle simulations. Their models could predict MPG for vehicles ...

In two previous posts, we calculated and then visualized the CAPM beta of a portfolio by fitting a simple linear model. Today, we move beyond CAPM’s simple linear regression and explore the Fama French (FF) multi-factor model of equity risk/return. For more background, have a look at the ...

Here are my picks for the “Top 40” packages of the 171 new packages that made it to CRAN (and stuck) in February, organized into the following categories: Computational Methods, Data, Finance, Science, Statistics, Time Series, and Utilities. Computational Methods adnuts v1.0.0: Provides an implementation of the no-U-turn (NUTS) algorithm by Hoffman ...

Data scientists prefer using the latest R packages to analyze their data. To ensure a good user experience, you will need a recent version of R running on a modern operating system. If you run R on an production server – and especially if you use RStudio Connect – plan to support ...

Shiny’s Design Most Shiny apps out there have a similar design style. It is usually easy for a seasoned Shiny developer to tell the difference between a Shiny app and a standard website. Why is this? Shiny apps ARE websites for all intents and purposes. Why do they not ...

I have been searching for various ways to find information about R packages for some time now, but I only recently learned about the CRAN_package_db() function in the base tools package. If a colleague hadn’t pointed it out to me, I am sure I would never have ...

In a previous post, we covered how to calculate CAPM beta for our usual portfolio consisting of:

+ SPY (S&P500 fund) weighted 25%
+ EFA (a non-US equities fund) weighted 25%
+ IJS (a small-cap value fund) weighted 20%
+ EEM (an emerging-mkts fund) weighted 20%
+ AGG (a bond fund) weighted 10%

Today, we will move on to visualizing the CAPM beta and explore some ggplot and highcharter functionality, along with the broom package. Before we can do any of this CAPM work, we ...

Here are my “Top 40” picks from the two hundred or so new packages that stuck to CRAN in January, listed under seven categories: Data, Data Science, Science, Statistics, Time Series, Utilities and Visualizations (I say “stuck to” because I counted at least six packages that were accepted onto CRAN in ...