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Category: R

Cognitive Services With R

Published 2017-03-01 by Kevin Feasel

Steph Locke shows how to use the Microsoft Cognitive Services Text Analytics API within R:

We have some different languages and we need to first do language detection before we can analyse the sentiment of our phrases

# Construct a request
response<-POST(cogapi, 
               add_headers(`Ocp-Apim-Subscription-Key`=cogapikey),
               body=toJSON(mydata))

Now we need to consume our response such that we can add the language code to our existing data.frame. The structure of the response JSON doesn’t play well with others so I use data.table’s nifty rbindlist. It is a **very good* candidate for purrr but I’m not up to speed on that yet.

Check out the whole post; Steph makes it look easy.

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Building A Neural Net

Published 2017-03-01 by Kevin Feasel

Shirin Glander has a great post on using Spark + sparklyr + h2o + rsparkling to build a neural net to study arrhythmia of the heart:

The data I am using to demonstrate the building of neural nets is the arrhythmia dataset from UC Irvine’s machine learning database. It contains 279 features from ECG heart rhythm diagnostics and one output column. I am not going to rename the feature columns because they are too many and the descriptions are too complex. Also, we don’t need to know specifically which features we are looking at for building the models. For a description of each feature, see https://archive.ics.uci.edu/ml/machine-learning-databases/arrhythmia/arrhythmia.names. The output column defines 16 classes: class 1 samples are from healthy ECGs, the remaining classes belong to different types of arrhythmia, with class 16 being all remaining arrhythmia cases that didn’t fit into distinct classes.

Very interesting post.

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ggraph

Published 2017-02-28 by Kevin Feasel

David Smith has a post on a new R package to display graphs:

A graph, a collection of nodes connected by edges, is just data. Whether it’s a social network (where nodes are people, and edges are friend relationships), or a decision tree (where nodes are branch criteria or values, and edges decisions), the nature of the graph is easily represented in a data object. It might be represented as a matrix (where rows and columns are nodes, and elements mark whether an edge between them is present) or as a data frame (where each row is an edge, with columns representing the pair of connected nodes).

The trick comes in how you represent a graph visually; there are many different options each with strengths and weaknesses when it comes to interpretation. A graph with many nodes and edges may become an unintelligible hairball without careful arrangement, and including directionality or other attributes of edges or nodes can reveal insights about the data that wouldn’t be apparent otherwise. There are many R packages for creating and displaying graphs (igraph is a popular one, and this CRAN task view lists many others) but that’s a problem in its own right: an important part of the data exploration process is trying and comparing different visualization options, and the myriad packages and interfaces makes that process difficult for graph data.

Click through for more information as well as a mesmerizing animated image.

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Four-And-Twenty R Users

Published 2017-02-27 by Kevin Feasel

Ginger Grant explains why SQL Server R Services creates a group with twenty separate external users:

There are many reasons why a DBA might want to not allow clients to access server memory as that will tax the server. Turning it off is relatively simple. Go to the SQL Server Management Console and select SQL Server Launchpad for the instance of SQL Server running R Server.

In the picture of the screen, the instance of SQL Server I have running R Services is in SS2016. Right click on the server and select Properties, then click on the Advanced tab. When looking at the number of external users allowed by default, the number might look familiar. The reason there are twenty User IDs created for R Server is because Launchpad allocates by default external twenty users to connect from SQL Server to run R. If you don’t want to allow external users to run on a server, you will need to prevent the users from connecting by not enabling them to run R. To run R, users need to have db_rrerole permissions. If they do not have that, they cannot run R. On the production server, it is probably best that this permission not be granted to non-system users.

Read on for more details.

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Handwriting Character Recognition

Published 2017-02-24 by Kevin Feasel

Tomaz Kastrun compares a few different libraries in terms of handwritten numeric character recognition:

Recently, I did a session at local user group in Ljubljana, Slovenija, where I introduced the new algorithms that are available with MicrosoftML package for Microsoft R Server 9.0.3.

For dataset, I have used two from (still currently) running sessions from Kaggle. In the last part, I did image detection and prediction of MNIST dataset and compared the performance and accuracy between.

MNIST Handwritten digit database is available here.

Tomaz has all of the code available as well.

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Analyzing Flight Data With Sparklyr

Published 2017-02-24 by Kevin Feasel

Aki Ariga continues his sparklyr series with some analysis of US flight data:

In this post, we will show you a visualization and build a predictive model of US flights with sparklyr. Flight visualization code is based on this article.

This post assumes you already have the following tables:

You should make these tables available through Apache Hive or Apache Impala (incubating) with Hue.

There’s some setup work to get this going, but getting a handle on sparklyr looks to be a good idea if you’re in the analytics space.

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RTVS RC1

Published 2017-02-24 by Kevin Feasel

David Smith alerts us that R Tools for Visual Studio Release Candidate 1 is available:

We’ll cover the features in detail with the general availability release of RTVS 1.0, but in summary the new features include:

  • Remote Execution: type R code in your local RTVS instance, but have the computations performed on a remote R server. You can also switch between local and remote workspaces at will.

  • SQL Server Integration: work with database connections and SQL queries, and create stored procedures with embedded R code.

  • Enhanced R Graphics Support: multiple floating and dockable plot windows, each with plot history.

I’ve been using RTVS more frequently lately and it’s definitely growing on me.

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Python + knitr

Published 2017-02-23 by Kevin Feasel

Steph Locke shows how to integrate Python code into knitr:

One of the nifty things about using R is that you can use it for many different purposes and even other languages!

If you want to use Python in your knitr docs or the newish RStudio R notebook functionality, you might encounter some fiddliness getting all the moving parts running on Windows. This is a quick knitr Python Windows setup checklist to make sure you don’t miss any important steps.

Between knitr, Zeppelin, and Jupyter, you should be able to find a cross-compatible notebook which works for you.

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Global Maps In R

Published 2017-02-22 by Kevin Feasel

The folks at Sharp Sight Labs show how to create high-quality map visuals in R:

Maps are great for practicing data visualization. First of all, there’s a lot of data available on places like Wikipedia that you can map.

Moreover, creating maps typically requires several essential skills in combination. Specifically, you commonly need to be able to retrieve the data (e.g., scrape it), mold it into shape, perform a join, and visualize it. Because creating maps requires several skills from data manipulation and data visualization, creating them will be great practice for you.

And if that’s not enough, a good map just looks great. They’re visually compelling.

With that in mind, I want to walk you through the logic of building one step by step.

Read on for a step by step process.

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