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Category: R

Visualizing Emergency Room Visits

Published 2017-05-26 by Kevin Feasel

Eugene Joh has a great blog post showing how to parse ICD-9 codes using regular expressions and then visualize the results as a treemap:

It looks like there is a header/title at [1], numeric grouping  at [2] “1.\tINFECTIOUS AND PARASITIC DISEASES”,  subgrouping by ICD-9 code ranges, at [3] “Intestinal infectious diseases (001-009)” and then 3-digit ICD-9 codes followed by a specific diagnosis, at [10] “007\tOther protozoal intestinal diseases”. At the end we want to produce three separate data frames that we’ll categorize as:

  1. Groups: the title which contains the general diagnosis grouping

  2. Subgroups: the range of ICD-9 codes that contain a certain diagnosis subgroup

  3. Classification: the specific 3-digit ICD-9 code that corresponds with a diagnosis

It’s a beefy article full of insight.

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Subplots In Maps

Published 2017-05-26 by Kevin Feasel

Ilya Kashnitsky shows how to embed subplots within a map using ggplot2:

So, with this map I want to show the location of more and less urbanized NUTS-2 regions of Europe. But I also want to show – with subplots – how I defined the three subregions of Europe (Eastern, Southern, and Western) and what is the relative frequency of the three categories of regions (Predominantly Rural, Intermediate, and Predominantly Rural) within each of the subregions. The logic of actions is simple: first prepare all the components, then assemble them in a composite plot. Let’s go!

This is very useful information, well worth the read.

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Spark Changes In HDP 2.6

Published 2017-05-25 by Kevin Feasel

Vinay Shukla and Syed Mahmood talk about what’s new with Spark and Zeppelin in the Hortonworks Data Platform 2.6 update:

SPARKR & PYSPARK

Most data scientists use R & Python and with SparkR & PySpark respectively they can continue to leverage their familiarity with the R & Python languages. However, they need to use the Spark API to leverage Machine learning with Spark and to take advantage of distributed computations. Both SparkR & PySpark are evolving rapidly and SparkR now supports a number of machine learning algorithms such as LDA, ALS, RF, GMM GBT etc. Another key improvement in SparkR is the ability to deploy a package interactively. This will help Data Scientists deploy their favorite R package in their own environment without stepping on other users.

PySpark now also supports deploying VirtualEnv and this will allow PySpark users to deploy their libraries in their own individual deployments.

There are several large changes, so check it out.

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Custom ggplot2 Subplots

Published 2017-05-25 by Kevin Feasel

Ilya Kashnitsky shows how to create custom subplots using ggplot2:

Actually, ggplot2 is a very powerful and flexible tool that allows to draw figures with quite a complex layout. Today I want to show the code that aligns six square plots (actually, maps) just as in the figure above. And it’s all about the handy function ggplot2::annotation_custom(). Since I used the layout more than once, I wrapped the code that produced it into a function that takes a list of 6 square plots as an input and yields the arranged figure with arrows as an output. Here is the commented code of the function.

This is the difference between “I’m just going to throw some stuff on there” (which is how I tend to operate) versus well thought out visual layout.

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Microsoft R Open 3.4.0

Published 2017-05-25 by Kevin Feasel

David Smith announces Microsoft R Open 3.4.0:

R 3.4.0 (upon which MRO 3.4.0 is based) is a major update to the R language, with many fixes and improvements. Most notably, R 3.4.0 introduces a just-in-time (JIT) compiler to improve performance of the scripts and functions that you write. There have been a few minor tweaks to the language itself, but in general functions and packages written for R 3.3.x should work the same in R 3.4.0. As usual, MRO points to a fixed CRAN snapshot from May 1 2017, but you can use the built-in checkpoint package to access packages from an earlier date (for compatibility) or a later date (to access new and updated packages).

The version of Microsoft R Server shipping with SQL Server 2017 will still be based on 3.3.3, but I’m going to guess that a new version of Microsoft R Server supporting 3.4.0 will ship in the next several months.

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Fisher’s Exact Test

Published 2017-05-25 by Kevin Feasel

Mala Mahadevan explains Fisher’s Exact Test and provides examples in T-SQL and R:

The decision rule in two sample tests of hypothesis depends on three factors :
1 Whether the test is upper, lower or two tailed (meaning the comparison is greater, lesser or both sides of gender and speaker count)
2 The level of significance or degree of accuracy needed,
3 The form of test statistic.
Our test here is to just find out if gender and speaker count are related so it is a two tailed test. The level of significance we can use is the most commonly used 95% which is also the default in R for Fischer’s Test. The form of the test statistic is P value. So our decision rule would be that gender and speaker category are related if P value is less than 0.05.

Click through for the R code followed by a code sample which should explain why you don’t want to do it in T-SQL.

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Deploying Packages To SQL Server R Services

Published 2017-05-24 by Kevin Feasel

Tracy Boggiano has a Powershell script to deploy packages to an instance running SQL Server R Services:

Somehow I have become the R DBA at my job which I don’t mind, I plan on taking Microsoft’s Professional Program on Data Science to be familiar with it.  But recently I’ve had to upload files to our R servers which the first time wasn’t too bad.  Copy these files to six different servers but come the second time around it became apparent that the Predictive Analytics Manger was going to be asking me to do this more frequently than I wanted to to it manually.  So I wrote a quick PowerShell function to take care of this added to our module we use in house.  It unzips the file provided to the correct location.  It does assume you have administrative rights to your server i.e. you can use the admin shares (c$) for example on the server.  You will need to get the function Get-CMSHost from my Running SQL Scripts Against Multiple Servers Using PowerShell post to run the code below.

Click through for the script.  This is particularly useful for deploying in-house packages and you don’t want to set up a miniCRAN.

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Three-Way Variance Analysis

Published 2017-05-22 by Kevin Feasel

Bogdan Anastasiei shows how to perform a three-way variance analysis when the third-order and second-order effects are both statistically significant:

In the formula above the interaction effect is, of course, dosegendertype. The ANOVA results can be seen below (we have only kept the line presenting the third-order interaction effect).

Df Sum Sq Mean Sq F value   Pr(>F)
dose:gender:type   2    187    93.4  22.367 3.81e-10

The interaction effect is statistically significant: F(2)=22.367, p<0.01. In other words, we do have a third-order interaction effect. In this situation, it is not advisable to report and interpret the second-order interaction effects (they could be misleading). Therefore, we are going to compute the simple second-order interaction effects.

This is definitely not a trivial article, but there are useful techniques in it.

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Building A Spinning Globe With R

Published 2017-05-19 by Kevin Feasel

James Cheshire shows how to use R to create an image of a spinning globe:

It has been a long held dream of mine to create a spinning globe using nothing but R (I wish I was joking, but I’m not). Thanks to the brilliant mapmate package created by Matt Leonawicz and shed loads of computing power, today that dream became a reality. The globe below took 19 hours and 30 processors to produce from a relatively low resolution NASA black marble data, and so I accept R is not the best software to be using for this – but it’s amazing that you can do this in R at all!

Now all that is missing is a giant TV and an evil lair.

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Multiple R Studio Users On HDInsight

Published 2017-05-18 by Kevin Feasel

Xiaoyong Zhu shows how to set up additional R Studio users in an HDInsight cluster:

Basically speaking, the “http user” will be used to authenticate through the HDInsight gateway, which is used to protect the HDInsight clusters you created. This user is used to access the Ambari UI, YARN UI, as well as many other UI components.

The “ssh user” will be used to access the cluster through secure shell. This user is actually a user in the Linux system in all the head nodes, worker nodes, edge nodes, etc., so you can use secure shell to access the remote clusters.

For Microsoft R Server on HDInsight type cluster, it’s a bit more complex, because we put R Studio Server Community version in HDInsight, which only accepts Linux user name and password as login mechanisms (it does not support passing tokens), so if you have created a new cluster and want to use R Studio, you need to first login using the http user’s credential and login through the HDInsight Gateway, and then use the ssh user’s credential to login to RStudio.

It’s a good read and also includes a sample Spark-R job.

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