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Category: R

Mastering Tools

Published 2016-12-28 by Kevin Feasel

The folks at Sharp Sight Labs explain that future obsolescence of a tool does not mean you should not master it:

The heart of his critique is this: data science is changing very fast, and any tool that you learn will eventually become obsolete.

This is absolutely true.

Every tool has a shelf life.

Every. single. one.

Moreover, it’s possible that tools are going to become obsolete more rapidly than in the past, because the world has just entered a period of rapid technological change. We can’t be certain, but if we’re in a period of rapid technological change, it seems plausible that toolset-changes will become more frequent.

The thing I would tie it to is George Stigler’s paper on information theory.  There’s a cost of knowing—which the commenter notes—but there’s also a cost to search, given the assumption that you know where to look.  Being effective in any role, be it data scientist or anything else, involves understanding the marginal benefit of pieces of information.  This blog post gives you a concrete example of that in the realm of data science.

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vtreat

Published 2016-12-27 by Kevin Feasel

John Mount introduces vtreat, an R package for data preparation:

Our group is distributing a detailed write up of the theory and operation behind our R realization of a set of sound data preparation and cleaning procedures called vtreat here: arXiv:1611.09477 [stat.AP]. This is where you can find out what vtreat does, decide if it is appropriate for your problem, or even find a specification allowing the use of the techniques in non-R environments (such as Python/Pandas/scikit-learn, Spark, and many others).

We have submitted this article for formal publication, so it is our intent you can cite this article (as it stands) in scientific work as a pre-print, and later cite it from a formally refereed source.

Or alternately, below is the tl;dr (“too long; didn’t read”) form.

Read more about vtreat on the package page or the vtreat vignette.

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Functional Programming In R

Published 2016-12-27 by Kevin Feasel

Bruno Rodrigues is working on a book that hits two of my favorite topics:

The book I’ve been working on these pasts months (you can read about it here, and read it for free here) is now available on Leanpub! You can grab a copy and read it on your ebook reader or on your computer, and what’s even better is that it is available for free (but you can also decide to buy it if you really like it). Here is the link on Leanpub.

In the book, I show you the basics of functional programming, unit testing and package development for the R programming language. The end goal is to make your data tidy in a reproducible way!

Looks like I have a book to add to my queue.

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Azure Management Using R

Published 2016-12-22 by Kevin Feasel

Alan Weaver introduces AzureSMR:

The AzureSMR functions currently addresses the following Azure Services:

  • Azure Blob: List, Read and Write to Blob Services

  • Azure Resources: List, Create and Delete Azure Resource. Deploy ARM templates.

  • Azure VM: List, Start and Stop Azure VMs

  • Azure HDI: List and Scale Azure HDInsight Clusters

  • Azure Hive: Run Hive queries against a HDInsight Cluster

  • Azure Spark: List and create Spark jobs/Sessions against a HDInsight Cluster(Livy)

This can be useful for cases like when you need to ramp up the Spark cluster before running a particularly compute-intensive process.

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Mixed Integer Optimization

Published 2016-12-21 by Kevin Feasel

David Smith discusses the ompr package in R:

Counterintuitively, numerical optimizations are easiest (though rarely actually easy) when all of the variables are continuous and can take any value. When integer variables enter the mix, optimization becomes much, much harder. This typically happens when the optimization is constrained by a limited selection of objects, for example packages in a weight-limited cargo shipment, or stocks in a portfolio constrained by sector weightings and transaction costs. For tasks like these, you often need an algorithm for a specialized type of optimization: Mixed Integer Programming.

For problems like these, Dirk Schumacher has created the ompr package for R. This package provides a convenient syntax for describing the variables and contraints in an optimization problem. For example, take the classic “knapsack” problem of maximizing the total value of objects in a container subject to its maximum weight limit.

Read the whole thing.

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Multidplyr

Published 2016-12-19 by Kevin Feasel

Matt Dancho shows how to use multidplyr to perform parallel processing on data cleansing activities:

There’s nothing more frustrating than waiting for long-running R scripts to iteratively run. I’ve recently come across a new-ish package for parallel processing that plays nicely with the tidyverse: multidplyr. The package has saved me countless hours when applied to long-running, iterative scripts. In this post, I’ll discuss the workflow to parallelize your code, and I’ll go through a real world example of collecting stock prices where it improves speed by over 5X for a process that normally takes 2 minutes or so. Once you grasp the workflow, the parallelization can be applied to almost any iterative scripts regardless of application.

This is a longer article, but if you’re using dplyr with R today, it’s worth a read.

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R Links

Published 2016-12-16 by Kevin Feasel

Ginger Grant has some links on learning R in the context of Power BI:

Comprehensive Resource Archive Network [CRAN] is where one can download Open Source R, packages and contains lots of information about R.

Microsoft R Open which is a fully CRAN compatible version created using the Intel MKL for improved performance can be downloaded here.

One thing I would push a little bit on that list is R Tools for Visual Studio.  My default R IDE is still R Studio, but RTVS has made some nice improvements, and it’s worth checking out.

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Analyzing Taxi Data With Microsoft R Server

Published 2016-12-15 by Kevin Feasel

Ali Zaidi builds a Spark cluster to analyze 1.1 billion taxi cab rides using Microsoft R Server:

In a similar spirit to how sparklyr allowed us to reuse our functions from the dplyr package to manipulate Spark DataFrames, the RxSpark API allows a data scientist to develop code that can be deployed in a multitude of environments. This allows the developer to shift their focus from writing code that’s specific to a certain environment, and instead focus on the complex analysis of their data science problem. We call this flexibility Write Once, Deploy Anywhere, or WODA for the acronym lovers.

For a deeper dive into the RevoScaleR package, I recommend you take a look at the online course, Analyzing Big Data with Microsoft R Server. Much of this blogpost follows along the last section of the course, on deployment to Spark.

R isn’t just for small, one-off jobs anymore.

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Data Science Languages

Published 2016-12-12 by Kevin Feasel

Alessandro Piva provides preliminary metrics on language usage among self-described data scientists:

Programming is one of the five main competence areas at the base of the skill set for a Data Scientist, even if is not the most relevant in term of expertise (see What is the right mix of competences for Data Scientists?). Considering the results of the survey, that involved more than 200 Data Scientist worldwide until today, there isn’t a prevailing choice among the programming languages used during the data science’s activities. However, the choice appears to be addressed mainly to a limited set of alternatives: almost 96% of respondents affirm to use at least one of R, SQL or Python.

These results don’t surprise me much.  R has slightly more traction than Python, but the percentage of people using both is likely to increase.  SQL, meanwhile, is vital for getting data, and as we’re seeing in the Hadoop space, as data platform products get more mature, they tend to gravitate toward a SQL or SQL-like language.  Cf. Hive, Spark SQL, Phoenix, etc.

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Interactive Decision Trees

Published 2016-12-12 by Kevin Feasel

Longhow Lam describes the interactive decision tree in Microsoft R Server 9.0:

Despite all the more modern machine learning algorithms, a good old single decision tree can still be useful. Moreover, in a business analytics context they can still keep up in predictive power. In the last few months I have created different predictive response and churn models. I usually just try different learners, logistic regression models, single trees, boosted trees, several neural nets, random forests. In my experience a single decision tree is usually ‘not bad’, often only slightly less predictive power than the more fancy algorithms.

An important thing in analytics is that you can ‘sell‘ your predictive model to the business. A single decision tree is a good way to to do just that, and with an interactive decision tree (created by Microsoft R) this becomes even more easy.

I’d like the labels in Longhow’s tree to be a little clearer, but I do like this from the perspective of giving end users something to experience.

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