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Category: R

Using Service Broker To Queue Up External Script Calls

Published 2017-10-20 by Kevin Feasel

Arvind Shyamsundar shows how to use Service Broker to run external R or Python scripts based on new data coming into a transactional system:

Here, we will show you how you can use the asynchronous execution mechanism offered by SQL Server Service Broker to ‘queue’ up data inside SQL Server which can then be asynchronously passed to a Python script, and the results of that Python script then stored back into SQL Server.

This is effectively similar to the external message queue pattern but has some key advantages:

  • The solution is integrated within the data store, leading to fewer moving parts and lower complexity
  • Because the solution is in-database, we don’t need to make copies of the data. We just need to know what data has to be processed (effectively a ‘pointer to the data’ is what we need).

Service Broker also offers options to govern the number of readers of the queue, thereby ensuring predictable throughput without affecting core database operations.

There are several interconnected parts here, and Arvind walks through the entire scenario.

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Reasons For Using Docker With R

Published 2017-10-17 by Kevin Feasel

Jeroen Ooms gives us a few reasons why we might want to containerize our R-based products:

The flagship of the OpenCPU system is the OpenCPU server: a mature and powerful Linux stack for embedding R in systems and applications. Because OpenCPU is completely open source we can build and ship on DockerHub. A ready-to-go linux server with both OpenCPU and RStudio can be started using the following (use port 8004 or 80):

docker run -t -p 8004:8004 opencpu/rstudio

Now simply open http://localhost:8004/ocpu/ and http://localhost:8004/rstudio/ in your browser! Login via rstudio with user: opencpu (passwd: opencpu) to build or install apps. See the readme for more info.

This is in the context of one particular product, but the reasons fit other scenarios too.  H/T R-Bloggers

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Linear Discriminant Analysis

Published 2017-10-13 by Kevin Feasel

Jake Hoare explains Linear Discriminant Analysis:

Linear Discriminant Analysis takes a data set of cases (also known as observations) as input. For each case, you need to have a categorical variable to define the class and several predictor variables (which are numeric). We often visualize this input data as a matrix, such as shown below, with each case being a row and each variable a column. In this example, the categorical variable is called “class” and the predictive variables (which are numeric) are the other columns.

Following this is a clear example of how to use LDA.  This post is also the second time this week somebody has suggested The Elements of Statistical Learning, so I probably should make time to look at the book.

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Azure Data Lake Store File Management With httr

Published 2017-10-12 by Kevin Feasel

Leila Etaati shows how to generate RESTful statements in R using httr:

In this post, I am going to share my experiment in how to do file management in ADLS using R studio,

to do this you need to have below items

1. An Azure subscription

2. Create an Azure Data Lake Store Account

3. Create an Azure Active Directory Application (for the aim of service-to-service authentication).

4. An Authorization Token from Azure Active Directory Application

It’s pretty easy to do, as Leila shows.

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Using R In Azure Data Lake Analytics

Published 2017-10-12 by Kevin Feasel

David Smith links to a tutorial which shows how to use R against Azure Data Lake Analytics:

The Azure Data Lake store is an Apache Hadoop file system compatible with HDFS, hosted and managed in the Azure Cloud. You can store and access the data within directly via the API, by connecting the filesystem directly to Azure HDInsight services, or via HDFS-compatible open-source applications. And for data science applications, you can also access the data directly from R, as this tutorial explains.

To interface with Azure Data Lake, you’ll use U-SQL, a SQL-like language extensible using C#. The R Extensions for U-SQL allow you to reference an R script from a U-SQL statement, and pass data from Data Lake into the R Script. There’s a 500Mb limit for the data passed to R, but the basic idea is that you perform the main data munging tasks in U-SQL, and then pass the prepared data to R for analysis. With this data you can use any function from base R or any R package. (Several common R packages are provided in the environment, or you can upload and install other packages directly, or use the checkpoint package to install everything you need.) The R engine used is R 3.2.2.

Click through for the details.

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Linear Regression With Deducer

Published 2017-10-12 by Kevin Feasel

Sunil Kappal demonstrates how to use Deducer, a GUI for R, to perform a simple linear regression:

Selecting the variables in the Deducer GUI:

  • Outcome variable: Y, or the dependent variable, should be put on this list

  • As numeric: Independent variables that should be treated as covariates should be put in this section. Deducer automatically converts a factor into a numeric variable, so make sure that the order of the factor level is correct

  • As factor: Categorically independent variables (language, ethnicity, etc.).

  • Weights: This option allows the users to apply sampling weights to the regression model.

  • Subset: Helps to define if the analysis needs to be done within a subset of the whole dataset.

Deducer is open source and looks like a pretty decent way of seeing what’s available to you in R.

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R And Python: Two Growing Languages

Published 2017-10-11 by Kevin Feasel

David Smith notes that as fast as Python is growing, R is as well:

Python has been getting some attention recently for its impressive growth in usage. Since both R and Python are used for data science, I sometimes get asked if R is falling by the wayside, or if R developers should switch course and learn Python. My answer to both questions is no.

First, while Python is an excellent general-purpose data science tool, for applications where comparative inference and robust predictions are the main goal, R will continue to be the prime repository of validated statistical functions and cutting-edge research for a long time to come. Secondly, R and Python are both top-10 programming languages, and while Python has a larger userbase, R and Python are both growing rapidly — and at similar rates.

I had a discussion about this last night.  I like the language diversity:  R is more statistician-oriented, whereas Python is more developer-oriented.  They both can solve the same set of problems, but there are certainly cases where one beats the other.  I think Python will end up being the more popular language for data science because of the number of application developers moving into the space, but for the data analysts and academicians moving to this field, R will likely remain the more interesting language.

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tibbletime: Time-Aware Data Sets In R

Published 2017-10-10 by Kevin Feasel

At Business Science, they’ve announced a new R package:

We are excited to announce the release of tibbletime v0.0.2 on CRAN. Loads of new functionality have been added, including:

  • Generic period support: Perform time-based calculations by a number of supported periods using a new ~period formula~.
  • Creating series: Use create_series() to quickly create a tbl_time object initialized with a regular time series.
  • Rolling calculations: Turn any function into a rolling version of itself with rollify().
  • A number of smaller tweaks and helper functions to make life easier.

As we further develop tibbletime, it is becoming clearer that the package is a tool that should be used in addition to the rest of the tidyverse. The combination of the two makes time series analysis in the tidyverse much easier to do!

Check out their demos comparing New York and San Francisco weather.  It looks like it’ll be a useful package.  H/T R-bloggers

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ANOVA

Published 2017-10-10 by Kevin Feasel

Mala Mahadevan explains what ANOVA is and why it’s interesting:

ANOVA – or analysis of variance, is a term given to a set of statistical models that are used to analyze differences among groups and if the differences are statistically significant to arrive at any conclusion. The models were developed by statistician and evolutionary biologist Ronald Fischer. To give a very simplistic definition – ANOVA is an extension of the two way T-Test to multiple cases.

ANOVA is an older test and a fairly simple process, but is quite useful to understand.

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Imbalanced Data In R

Published 2017-10-09 by Kevin Feasel

Rathnadevi Manivannan explains how to deal with imbalanced data using R:

Imbalanced data refers to classification problems where one class outnumbers other class by a substantial proportion. Imbalanced classification occurs more frequently in binary classification than in multi-level classification. For example, extreme imbalanced data can be seen in banking or financial data where majority credit card uses are acceptable and very few credit card uses are fraudulent.

With an imbalanced dataset, the information required to make an accurate prediction about the minority class cannot be obtained using an algorithm. So, it is recommended to use balanced classification dataset.

Rathnadevi uses fraudulent transactions for his sample, but medical diagnoses is also a good example:  suppose 1 person in 10,000 has a particular disease.  You’re 99.99% right if you just say nobody has the disease, but that’s a rather unhelpful model.

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