Blog Archives

Player Data for the 2018 FIFA World Cup

June 13, 2018
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Player Data for the 2018 FIFA World Cup

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 experience. In this post, I will show...

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Monte Carlo Part Two

June 12, 2018
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Monte Carlo Part Two

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 allows an end user to build a custom portfolio, simulate...

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Monte Carlo

June 4, 2018
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Monte Carlo

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 two or three blog posts) is to build...

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Exploring R Packages with cranly

May 30, 2018
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Exploring R Packages with cranly

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 in which some columns have vectors or lists as entries. It’s simple to run...

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April 2018: “Top 40” New Packages

May 23, 2018
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April 2018: “Top 40” New Packages

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 high performance methods for solving ordinary differential equations (ODE), stochastic...

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Enterprise Dashboards with R Markdown

May 15, 2018
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Enterprise Dashboards with R Markdown

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 data and communicate insights. Everyone in a business, from the...

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2018 R Conferences

May 10, 2018
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2018 R Conferences

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 talks. Headlined by R Core member Martin Mächler and fellow...

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Rolling Fama French

May 9, 2018
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Rolling Fama French

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 article published in The Journal of Financial Economics, Common...

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March 2018: “Top 40” New Package Picks

April 29, 2018
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March 2018: “Top 40” New Package Picks

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 below focuses on climate science. I was...

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An Introduction to Greta

April 22, 2018
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An Introduction to Greta

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 a Bayesian modeler could want. Stan is a powerful, production-level...

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