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Category: R

Forecasting Field Goal Percentages With Prophet

Published 2018-12-26 by Kevin Feasel

Marlon Ribunal uses the Prophet library in R to forecast critical information:

I’ve been looking for an easy way to get to learning predictive analysis and forecasting. Prophet provides that path. Prophet is released by Facebook’s Core Data Science Team.
“Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.”
Just to dip my toes into the waters, I tried Prophet’s Quick Start Guide in R.
Let’s forecast the Field Goal Percentage (FG%) of Kyle Kuzma of the Los Angeles Lakers for the next 6 Months.

It’d be critical and important if it were hockey data. Or football data or baseball data or maybe even cricket data (but I don’t understand cricket data and why is that guy still running didn’t he get thrown out or something I don’t get it?).

As far as Prophet goes, it’s a useful library and works well if you’re looking at seasonal time series data.

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Higher-Order Functions In R

Published 2018-12-21 by Kevin Feasel

Holger von Jouanne-Diedrich explains the concept of higher-order functions using R as an example:

The part that causes the biggest difficulties (especially for beginners of R) is that you state the name of the function at the beginning and use the assignment operator – as if functions were like any other data type, like vectors, matrices or data frames…

Congratulations! You just encountered one of the big ideas of functional programming: functions are indeed like any other data type, they are not special – or in programming lingo, functions are first-class members.

This is one of the core tenets of functional programming: functions are things you can pass around to other functions; they aren’t special, inviolate pieces of code but are just another thing. Click through for a couple good examples of what you get in a language which supports higher-order functions.

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Variable Screening With vtreat

Published 2018-12-19 by Kevin Feasel

John Mount explains how you can use vtreat for determining variable importance:

Part of the vtreat philosophy is to assume after the vtreat variable processing the next step is a sophisticated supervised machine learningmethod. Under this assumption we assume the machine learning methodology (be it regression, tree methods, random forests, boosting, or neural nets) will handle issues of redundant variables, joint distributions of variables, overall regularization, and joint dimension reduction.
However, an important exception is: variable screening. In practice we have seen wide data-warehouses with hundreds of columns overwhelm and defeat state of the art machine learning algorithms due to over-fitting. We have some synthetic examples of this (here and here).
The upshot is: even in 2018 you can not treat every column you find in a data warehouse as a variable. You must at least perform some basic screening.

Read on to see a couple quick functions which help with this screening.

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Reviewing Word Associations With R

Published 2018-12-18 by Kevin Feasel

Julia Silge does some exploratory analysis on the Small World of Words project:

The Small World of Words project focuses on word associations. You can try it out for yourself to see how it works, but the general idea is that the participant is presented with a word (from “telephone” to “journalist” to “yoga”) and is then asked to give their immediate association with that word. The project has collected more than 15 million responses to date, and is still collecting data. You can check out some pre-built visualizations the researchers have put together to explore the dataset, or you can download the data for yourself.

It’s an interesting analysis of the data set, mixed in with some good R code.

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Deploying An R Service To Azure Kubernetes Service

Published 2018-12-14 by Kevin Feasel

Hong Ooi shows us how we can use Azure Container Registry and Azure Kubernetes Service to deploy an R model via Plumber:

If you run this code, you should see a lot of output indicating that R is downloading, compiling and installing randomForest, and finally that the image is being pushed to Azure. (You will see this output even if your machine already has the randomForest package installed. This is because the package is being installed to the R session inside the container, which is distinct from the one running the code shown here.)
All docker calls in AzureContainers, like the one to build the image, return the actual docker commandline as the cmdline attribute of the (invisible) returned value. In this case, the commandline is docker build -t bos_rf . Similarly, the push() method actually involves two Docker calls, one to retag the image, and the second to do the actual pushing; the returned value in this case will be a 2-component list with the command lines being docker tag bos_rf deployreg.azurecr.io/bos_rf and docker push deployreg.azurecr.io/bos_rf.

I love this confluence of technologies and at the same time get a “descent into madness” feeling from the sheer number of worlds colliding.

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Road Construction Incentive Contracts And R

Published 2018-12-13 by Kevin Feasel

Sebastian Kranz promotes an interesting RTutor project:

Patrick Bajari and Gregory Lewis have collected a detailed sample of 466 road construction projects in Minnesota to study this question in their very interesting article Moral Hazard, Incentive Contracts and Risk: Evidence from Procurement in the Review of Economic Studies, 2014.
They estimate a structural econometric model and find that changes in contract design could substantially reduce the duration of road blockages and largely increase total welfare at only minor increases in the risk that road construction firms face.
As part of his Master Thesis at Ulm University, Claudius Schmid has generated a nice and detailed RTutor problem set that allows you to replicate the findings in an interactive fashion. You learn a lot about the structure and outcomes of the currently used contracts, the theory behind better contract design and how the structural model to assess the quantitative effects can be estimated and simulated. At the same time, you can hone your general data science and R skills.

Click through to a couple of ways to get to this RTutor project and learn a bit about building incentive contracts to modify behavior.  H/T R-Bloggers

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Analyzing Customer Churn With Keras And H2O

Published 2018-12-13 by Kevin Feasel

Shirin Glander has released code pertaining to a forthcoming book chapter:

This is code that accompanies a book chapter on customer churn that I have written for the German dpunkt Verlag. The book is in German and will probably appear in February: https://www.dpunkt.de/buecher/13208/9783864906107-data-science.html.
The code you find below can be used to recreate all figures and analyses from this book chapter. Because the content is exclusively for the book, my descriptions around the code had to be minimal. But I’m sure, you can get the gist, even without the book. 😉

Click through for the code.  This is using the venerable AT&T customer churn data set.

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Donating To The R Foundation

Published 2018-12-12 by Kevin Feasel

Mark Niemann-Ross explains how you can donate to the R Foundation:

I benefit from the work of the R Foundation. They oversee the language, but also encourage a healthy ecosystem. CRAN happens because of them. Updates to R happen because of them. useR! happens because of them. Every day, you and I are the recipients of some part of their time.
The least we can do is show them some appreciation. If you point your web browser at https://www.r-project.org/foundation/donations.html you’ll find a convenient (and surprisingly inexpensive) place to express your appreciation. As an individual, you can send these kind folks twenty-five euros to tell them you’re in favor of what they do.

But be sure to read the whole thing, especially if you are an American who wants the donation to be tax-deductible.  I believe that earmarking in this case is adding special instructions on SIAA’s PayPal page.

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