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Category: R

Finding Clusters Of Queries Using R

Published 2017-01-09 by Kevin Feasel

Tomaz Kastrun shows how to use R to find clusters of queries which behave similarly:

So the R code said that, there are three clusters generating And I used medians to generate data around it. In addition I have also tested the result with Partitioning around medoids (which is opposite to hierarchical clustering) and the results from both techniques yield clean clusters.

Clustering models can be powerful for discovering commonalities, and that might help you find a number of queries which all behave in some sub-optimal way without having to trawl through every procedure’s code.

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Pipelearner

Published 2017-01-06 by Kevin Feasel

Simon Jackson introduces pipelearner, a tool to help with creating machine learning pipelines:

This post will demonstrate some examples of what pipeleaner can currently do. For example, the Figure below plots the results of a model fitted to 10% to 100% (in 10% increments) of training data in 50 cross-validation pairs. Fitting all of these models takes about four lines of code in pipelearner.

Click through for some very interesting examples.

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Using RTVS

Published 2017-01-05 by Kevin Feasel

David Eldersveld gives three reasons why you might be interested in R Tools for Visual Studio:

2. Incorporate R projects as part of a broader Visual Studio solution
Many Visual Studio solutions end up being a collection of individual projects. More often than not, these projects are logically joined by virtue of being part of the same business solution, but each one can incorporate different components or languages. For example, you may architect a solution that involves separate projects for loading data­­ with Azure Data Factory, analysis with R, a front-end C# web app, etc. Rather than keep your R code siloed off in a separate solution, unite it with the rest of your code for development and source control.

This is my primary reason.  R Studio is still my go-to option, but RTVS is maturing fairly nicely.  It still feels slower than R Studio when displaying data on-screen (especially when you’re spitting out a couple hundred lines of text), but that Visual Studio integration will go far.  A fourth reason that David does not mention:  it generates the really ugly sp_execute_external_script code for SQL Server R Services.

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Ten Notes On SparkR

Published 2017-01-03 by Kevin Feasel

Neil Dewar has a notebook with ten important things when migrating from R to SparkR:

  1. Apache Spark Building Blocks. A high-level overview of Spark describes what is available for the R user.

  2. SparkContext, SQLContext, and SparkSession. In Spark 1.x, SparkContext and SQLContext let you access Spark. In Spark 2.x, SparkSession becomes the primary method.

  3. A DataFrame or a data.frame? Spark’s distributed DataFrame is different from R’s local data.frame. Knowing the differences lets you avoid simple mistakes.

  4. Distributed Processing 101. Understanding the mechanics of Big Data processing helps you write efficient code—and not blow up your cluster’s master node.

  5. Function Masking. Like all R libraries, SparkR masks some functions.

  6. Specifying Rows. With Big Data and Spark, you generally select rows in DataFrames differently than in local R data.frames.

  7. Sampling. Sample data in the right way, and use it as a tool for converting between big and small data.

  8. Machine Learning. SparkR has a growing library of distributed ML algorithms.

  9. Visualization.It can be hard to visualize big data, but there are tricks and tools which help.

  10. Understanding Error Messages. For R users, Spark error messages can be daunting. Knowing how to parse them helps you find the relevant parts.

I highly recommend checking out the notebook.

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Forecasting Restaurant Inspection Failures

Published 2016-12-30 by Kevin Feasel

David Smith writes about an R model which predicts which restaurants are more likely to fail inspection:

Chicago’s Department of Public Health used the R language to build and deploy the model, and made the code available as an open source project on GitHub. The reasons given are twofold:

An open source approach helps build a foundation for other models attempting to forecast violations at food establishments. The analytic code is written in R, an open source, widely-known programming language for statisticians. There is no need for expensive software licenses to view and run this code.

Read on for more details and check out their GitHub repo.

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7 Visualizations In R

Published 2016-12-30 by Kevin Feasel

Dikesh Jariwala provides sample R code for seven common visualizations:

In your day-to-day activities, you’ll come across the below listed 7 charts most of the time.

  1. Scatter Plot
  2. Histogram
  3. Bar & Stack Bar Chart
  4. Box Plot
  5. Area Chart
  6. Heat Map
  7. Correlogram

We’ll use ‘Big Mart data’ example as shown below to understand how to create visualizations in R. You can download the full dataset from here.

That’s a nice set of visuals, covering a broad swath of potential visualization scenarios.

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Basic Non-Linear Regression In R

Published 2016-12-29 by Kevin Feasel

Renata Ghisloti Duarte de Souza gives an example of running a non-linear regression in R:

Now, suppose you were able to find a good function to model your data. With that, we are able to predict future values for our small dataset.

One important thing about the predict() function in R is that it expects a similar dataframe with the same column name and type as the one you used in your model.

Click through for several examples.

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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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