Publications by R Views
Fitting a TensorFlow Linear Classifier with tfestimators
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 to Google’...
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Package Management for Reproducible R Code
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 new data science programm...
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Dec 2017: “Top 40” New Package Picks
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 below of my “Top 40” picks. One day, I would like to go back ...
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Cost-Effective BigQuery with R
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 days and are increasingly wasteful. ...
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Calculating Beta in the Capital Asset Pricing Model
Today we will continue our portfolio fun by calculating the CAPM beta of our portfolio returns. That will entail fitting a linear model and, when we get to visualization next time, considering the meaning of our results from the perspective of asset returns. By way of brief background, the Capital Asset Pricing Model (CAPM) is a model, created by...
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Deep learning at rstudio::conf 2018
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 J.J. Allaire discussing “Machine Learning with Tensorflow and R”. ...
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Jan 2018: “Top 40” New Package Picks
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 January but removed within the month. ...
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Visualizing the Capital Asset Pricing Model
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 an...
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Analyzing Metadata for CRAN Packages
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 found it on my own. pdb <- tools:::CRAN_package_db() When invoked, this function goes...
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Alternative Design for Shiny
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 vary as greatly as the rest of the sites we encounter when surfing the web? ...
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