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Category: R

RevoScaleR With Power BI

Published 2017-03-21 by Kevin Feasel

Tomaz Kastrun looks at several methods for using RevoScaleR packages in a Power BI dashboard:

I was invited to deliver a session for Belgium User Group on SQL Server and R integration. After the session – which we did online using web based Citrix  – I got an interesting question: “Is it possible to use RevoScaleR performance computational functions within Power BI?“. My first answer was,  a sceptical yes. But I said, that I haven’t used it in this manner yet and that there might be some limitations.

The idea of having the scalable environment and the parallel computational package with all the predictive analytical functions in Power BI is absolutely great. But something tells me, that it will not be that straight forward.

Read on for the rest of the story.

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Graphing R Package Dependencies

Published 2017-03-17 by Kevin Feasel

Tomaz Kastrun uses the igraph package to graph package dependencies in R:

With importing package tools, we get many useful functions to find additional information on packages.

Function package.dependencies() parses and check dependencies of a package in current environment. Function package_dependencies()  (with underscore and not dot) will find all dependent and reverse dependent packages.

This probably tilts more toward “fun” than “practical,” but this will let you see the full set of dependencies for a package if, for example, you need to grab all of these packages for upgrading an offline instance.

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Understanding Neural Nets

Published 2017-03-15 by Kevin Feasel

David Smith links to a video which explains how neural networks do their thing:

In R, you can train a simple neural network with just a single hidden layer with the nnet package, which comes pre-installed with every R distribution. It’s a great place to start if you’re new to neural networks, but the deep learning applications call for more complex neural networks. R has several packages to check out here, including MXNet, darch, deepnet, and h2o: see this post for a comparison. The tensorflow package can also be used to implement various kinds of neural networks.

R makes it pretty easy to run one, though it then becomes important to understand regularization as a part of model tuning.

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Analyzing The Ramones

Published 2017-03-10 by Kevin Feasel

Salvino Salvaggio uses R to analyze The Ramones:

Musical purists always reproached the Ramones for knowing a couple of chords only and making an excessive use of them. Data show that the band knew at least… 11 different chords (out of too-many-to-bother-counting possibilities) although 80% of their songs were built on no more than 6. And there is no evidence of a sophistication of the Ramones’ compositions over time.

It’s a fun analysis with all the R code attached.  This fun analysis, however, includes n-gram analysis, sentiment analysis, and token distribution analysis.

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Air Travel Route Maps With ggplot2

Published 2017-03-09 by Kevin Feasel

Peter Prevos wants to create a pretty map of flights he’s taken:

The first step was to create a list of all the places I have flown between at least once. Paging through my travel photos and diaries, I managed to create a pretty complete list. The structure of this document is simply a list of all routes (From, To) and every flight only gets counted once. The next step finds the spatial coordinates for each airport by searching Google Maps using the geocode function from the ggmap package. In some instances, I had to add the country name to avoid confusion between places.

The end result is imperfect (as Peter mentions, ggmap isn’t wrapping around), but does fit the bill for being eye-catching.

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replyr

Published 2017-03-08 by Kevin Feasel

John Mount shows off replyr, which is dplyr for remote, distributed data sets (think SparkR or sparklyr):

Suppose we had a large data set hosted on a Spark cluster that we wished to work with using dplyr and sparklyr (for this article we will simulate such using data loaded into Spark from the nycflights13 package).

We will work a trivial example: taking a quick peek at your data. The analyst should always be able to and willing to look at the data.

It is easy to look at the top of the data, or any specific set of rows of the data.

Read on for more details.

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R 3.3.3 Released

Published 2017-03-08 by Kevin Feasel

David Smith alerts us to R 3.3.3:

The R core group announced today the release of R 3.3.3 (code-name: “Another Canoe”). As the wrap-up release of the R 3.3 series, this update mainly contains minor bug-fixes. (Bigger changes are planned for R 3.4.0, expected in mid-April.) Binaries for the Windows version are already up on the CRAN master site, and binaries for all platforms will appear on your local CRAN mirror within the next couple of days.

For now, I’m holding out until R 3.4.0.

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Using Prophet For Stock Price Predictions

Published 2017-03-07 by Kevin Feasel

Marcelo Perlin looks at Facebook’s Prophet to see if it works well for predicting stock price movements:

The previous histogram shows the total return from randomly generated signals in 10^{4} simulations. The vertical line is the result from using prophet. As you can see, it is a bit higher than the average of the distribution. The total return from prophet is lower than the return of the naive strategy in 27.5 percent of the simulations. This is not a bad result. But, notice that we didn’t add trading or liquidity costs to the analysis, which will make the total returns worse.

The main results of this simple study are clear: prophet is bad at point forecasts for returns but does quite better in directional predictions. It might be interesting to test it further, with more data, adding trading costs, other forecasting setups, and see if the results hold.

This is a very interesting article, worth reading.  H/T R Bloggers

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htmlwidgets

Published 2017-03-06 by Kevin Feasel

David Smith writes about the htmlwidgets gallery in R:

While R’s base graphics library is almost limitlessly flexible when it comes to create static graphics and data visualizations, new Web-based technologies like d3 and webgl open up new horizons in high-resolution, rescalable and interactive charts. Graphics built with these libraries can easily be embedded in a webpage, can be dynamically resized while maintaining readable fonts and clear lines, and can include interactive features like hover-over data tips or movable components. And thanks to htmlwidgets for R, you can easily create a variety of such charts using R data and functions, explore them in an interactive R session, and include them in Web-based applications for others to experience.

There are some nice widgets in this set.

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