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Category: R

Sentiment Analysis In R

Published 2017-06-07 by Kevin Feasel

Stefan Feuerriegel and Nicolas Pröllochs have a new package in CRAN:

Our package “SentimentAnalysis” performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as QDAP or Loughran-McDonald. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.

I’m not sure how it stacks up to external services, but it’s another option available to us.

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Summary Improvements In R

Published 2017-06-05 by Kevin Feasel

John Mount points out a nice quasi-bugfix in R 3.4.0:

In older versions of R (say R 3.3.1) the above code gave the following undesirable result:

summary(15555)

#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   15560   15560   15560   15560   15560   15560

This was always very confusing and hard to explain to beginners. To justify this you had to explain that “R, by default, calculates the summary rounded to 4 significant digits, and is simultaneously configured to give absolutely no indication has to how many significant digits are in fact being displayed.” To add insult to injury summary()picked a different number of sigfigs than the default numeric presentation. One could type “median(15555)” and get the expected presentation “15555“.

I like this change.

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Fresh R Installation On Linux

Published 2017-06-02 by Kevin Feasel

Marcelo Perlin has a script to install R on Linux:

Since I formatted all my three computers (home/laptop/work), I wrote a small bash file to automate the process of installing R and its dependencies. I use lots of R packages in a daily basis. For some of them, it is required to install dependencies using the terminal. Each time that a install.package() failed, I saved the name of the required software and added it to the bash file. While my bash file will not cover all dependencies for all packages, it will suffice for a great proportion.

Another option might be to grab a Docker image.

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Multi-Channel Attribution With R

Published 2017-06-01 by Kevin Feasel

Sergey Bryl walks through some of the difficulties of the multi-channel attribution solution he came up with before:

The main steps that we will review are the following:

  • splitting paths depending on purchases counts

  • replacing some channels/touch points

  • a unique channel/touchpoint case

  • consequent duplicated channels in the path and higher order Markov chains

  • paths that haven’t led to a conversion

  • customer journey duration

  • attributing revenue and costs comparisons

There’s a lot there, and I like the practical explanations of issues when dealing with a real business problem.

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Using sparklyr

Published 2017-05-31 by Kevin Feasel

Hossein Falaki and Xiangrui Meng show how to use sparklyr on a Databricks Spark cluster:

We collaborated with our friends at RStudio to enable sparklyr to seamlessly work in Databricks clusters. Starting with sparklyr version 0.6, there is a new connection method in sparklyr: databricks. When calling spark_connect(method = "databricks") in a Databricks R Notebook, sparklyr will connect to the spark cluster of that notebook. As this cluster is fully managed, you do not need to specify any other information such as version, SPARK_HOME, etc.

I’d lean toward sparklyr over SparkR because of the former’s tidyverse-centric view.

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Versioning R Code In SQL Server

Published 2017-05-30 by Kevin Feasel

Steph Locke shows how to combine R models and SQL Server temporal tables for versioning:

If we’re storing our R model objects in SQL Server then we can utilise another SQL Server capability, temporal tables, to take the pain out of versioning and make it super simple.

Temporal tables will track changes automatically so you would overwrite the previous model with the new one and it would keep a copy of the old one automagically in a history table. You get to always use the latest version via the main table but you can then write temporal queries to extract any version of the model that’s ever been implemented. Super neat!

I do exactly this.  In my case, it’s to give me the ability to review those models after the fact once I know whether they generated good outcomes or not.

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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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