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Category: R

Running RStudio Server In Azure

Published 2019-01-23 by Kevin Feasel

David Smith notes that RStudio Server Pro is now available on Azure:

RStudio Server Pro is now available on the Azure Marketplace, the company announced on the RStudio Blog earlier this month. This means you can launch RStudio Server Pro on an virtual machine with the memory, disk, and CPU configuration of your choice, and pay by the minute for the VM instance plus a the RStudio software charge. Then, you can use a browser to access the remote RStudio Server (the interface is nigh-indistinguishable from the desktop version), with access to the commercial features of RStudio including support for multiple R version and concurrent R sessions, load-balancing and high availability instances, and enhanced security.

RStudio Server Pro and Microsoft R Server are both very nice for production-quality R servers. You can get away with the open source versions, but there are some good reasons to use the enterprise-grade products in an enterprise.

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Using Plotly In Power BI

Published 2019-01-22 by Kevin Feasel

Kara Annanie shows how you can R integration in Power BI to push Plotly visuals to users:

In the example, above, we’ve created a line chart visualization using Plotly and we’ve decided to put labels on the graph, but only on the first and last points of the line graph. This graph would be particularly useful to show 13 months of data overtime, where the left-most label shows January of last year, for example, and the right-most label shows January of this year, for example. The user could still view the trend across the year between both January data points.

Click through for a pair of videos and some notes on how to get started.

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Inline Operators In R With wrapr

Published 2019-01-18 by Kevin Feasel

John Mount shows how to use inline operators in R with the wrapr package:

The above code is assuming you have the wrapr package attached via already having run library('wrapr').

Notice we picked R-related operator names. We stayed away from overloading the + operator, as the arithmetic operators are somewhat special in how they dispatch in R. The goal wasn’t to make R more like Python, but to adapt a good idea from Python to improve R.

Also, it’s a little late to pick up the discount (though Manning has discounts pretty much every day so be patient and you’ll find 40+ percent off) but check out the second edition of Practical Data Science with R by Mount and Nina Zumel. I’ve held off on reading it so far because I want to wait until it’s closer to completion, but it is on my to-read list.

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Feature And Text Classification Using Naive Bayes In R

Published 2019-01-18 by Kevin Feasel

I wrap up my series on the Naive Bayes class of algorithms, finally writing some code along the way:

Now we’re going to look at movie reviews and predict whether a movie review is a positive or a negative review based on its words. If you want to play along at home, grab the data set, which is under 3MB zipped in 2000 reviews in total.

Unike last time, I’m going to break this out into sections with commentary in between. If you want the full script with notebook, check out the GitHub repo I put together for this talk.

Assuming I ever get a chance to do this talk again, I’m probably going to change the data sets in the example given how overplayed iris is.

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An Explanation Of Convolutional Neural Networks

Published 2019-01-18 by Kevin Feasel

Shirin Glander explains some of the mechanics behind Convolutional Neural Networks:

Convolutional Neural Nets are usually abbreviated either CNNs or ConvNets. They are a specific type of neural network that has very particular differences compared to MLPs. Basically, you can think of CNNs as working similarly to the receptive fields of photoreceptors in the human eye. Receptive fields in our eyes are small connected areas on the retina where groups of many photo-receptors stimulate much fewer ganglion cells. Thus, each ganglion cell can be stimulated by a large number of receptors, so that a complex input is condensed into a compressed output before it is further processed in the brain.

If you’re interested in understanding why a CNN will classify the way it does, chapter 5 of Deep Learning with R is a great reference.

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Getting CSV Row Counts

Published 2019-01-18 by Kevin Feasel

Dave Mason shares a few techniques for getting row counts of CSV files:

I was reminded of this recently as I was working with R, trying to read a nearly 2 GB data file. I wanted to read in 5% of the data and output it to a smaller file that would make the test code run faster. The particular function I was working with needed a row count as one of its parameters. For me, that meant I had to determine the number of rows in the source file and multiply by 0.05. I tied the code for all of those tasks into one script block.
Now, none to my surprise, it was slow. In my short experience, I’ve found R isn’t particularly snappy–even when the data can fit comfortably in memory. I was pretty sure I could beat R’s record count performance handily with C#. And I did. I found some related questions on StackOverflow. A small handful of answers discussed the efficiency of various approaches. I only tried two C# variations: my original attempt, and a second version that was supposed to be faster (the improvement was nominal).

To fact-check Dave (because this blog is about nothing other than making sure Dave is right), I checked the source code to the wc command. That command also streams through the entire file, so Dave’s premise looks good.

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Combining Plots In R With cowplot

Published 2019-01-17 by Kevin Feasel

Abdul Majed Raja shows how to use the cowplot library in R to merge together independent plots into a single image:

The way it works in cowplot is that, we have assign our individual ggplot-plots as an R object (which is by default of type ggplot). These objects are finally used by cowplot to produce a unified single plot.

In the below code, We will build three different histograms using the R’s in-built dataset iris and then assign one by one to an R object. Finally, we will use cowplot function plot_grid() to combine the two plots of our interest.

The only thing that disappointed me with cowplot is that its name has nothing to do with cattle.

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AzureR Packages In Cran

Published 2019-01-11 by Kevin Feasel

David Smith points out that the Azure packages for R are now in CRAN:

The suite of AzureR packages for interfacing with Azure services from R is now available on CRAN. If you missed the earlier announcements, this means you can now use the install.packages function in R to install these packages, rather than having to install from the Github repositories. Updated versions of these packages will also be posted to CRAN, so you can get the latest versions simply by running update.packages.

Read on for a summary of those packages.

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R htmlTable Updates

Published 2019-01-08 by Kevin Feasel

Max Gordon has some updates to the htmlTable package:

Even more common than grouping columns is probably grouping data by rows. The htmlTable allows you to do this by rgroup and tspanner. The most common approach is by using rgroupas the first row-grouping element but with larger tables you frequently want to separate concepts into separate sections. Here’s a more complex example. This has previously been a little cumbersome to to counting the rows of each tspanner but now you’re able to (1) leave out the last row, (2) specify the number of rgroups instead of the number of rows. The latter is convenient as the n.tspanner must align with the underlying rgroup. 

I haven’t used this package before, but it does look interesting. H/T R-bloggers

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The Basics Of Docker For R Users

Published 2019-01-07 by Kevin Feasel

Colin Fay explains some of the core principles behind Docker, containerizing some R code along the way:

Docker is designed to enclose environments inside an image / a container. What this allows, for example, is to have a Linux machine on a Macbook, or a machine with R 3.3 when your main computer has R 3.5. Also, this means that you can use older versions of a package for a specific task, while still keeping the package on your machine up-to-date.
This way, you can “solve” dependencies issues: if ever you are afraid dependencies will break your analysis when packages are updated, build a container that will always have the software versions you desire: be it Linux, R, or any package.

Click through for the details. H/T R-bloggers

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