Category: R

Steph Locke tells us about a way to mock data for R:

Mockaroo is a really impressive service with a wide spread of different data types. They also have simple ways of adding things like within group differences to data so that you can mock realistic class differences. They use the freemium model so you can get a thousand rows per download, which is pretty sweet. The big BUT you can feel coming on is this – it’s a GUI! I don’t want to have spend time hand cranking a data extract.

Thankfully, they have a GUI for getting data too and it’s pretty simply to use so I’ve started making a package for it.

Steph is working on an R package, so this is pretty exciting.

R now integrates into Visual Studio:

RTVS is an IDE and as such you can use it with any recent version of R such as 3.2.x. If you install the free Microsoft R Open, you automatically get some turbo options such as threading support on multi-processor machines, providing significant speedup for a variety of analytical functions, as well as package collections check-pointed to a particular date/version. Microsoft R Server provides Big Data support and additional advanced features that can be used with SQL Server.

This is an early release, so expect a few bugs and some missing functionality.  It also isn’t RStudio—it’s RStudio several years ago.  But what it does nicely is integrate with the rest of your stack:  you can tie together the R code, the C#/F# code which helps clean data, the SQL Server project which holds your data, etc. etc.

David Smith gives us a tutorial on credit card fraud detection:

If you have a database of credit-card transactions with a small percentage tagged as fraudulent, how can you create a process that automatically flags likely fraudulent transactions in the future? That’s the premise behind the latest Data Science Deep Dive on MSDN. This tutorial provides a step by step to using the R language and the big-data statistical models of the RevoScaleR package of SQL Server 2016 R Services to build and use a predictive model to detect fraud.

This looks to be a follow-up from the fraud detection series.

Casimir Saternos has an article on matrix operations and other data transformations in R:

Operations that are conceptually simple can be difficult to perform using SQL.  Consider the common requirements to pivot or transpose a dataset.   Each of these actions are conceptually straightforward but are complex to implement using SQL.  The examples that follow are somewhat verbose, but the details are not significant. The main point is to illustrate is that, by using specialized functions outside of SQL,  R makes trivial some of those operations that would otherwise require complex SQL statements.  The contrast in the amount of code required is striking.  The simpler approach allows you to focus attention on the scientific or business problem at hand, rather than expending energy reading documentation or laboriously testing complex statements.

I consider this where the second-order value of R comes in.  The initial “wow” factor is in how easy you can plot things, and this ease of data cleansing is the next big time-saver.

Buck Woody shares some tips regarding learning R:

Another mistake I see a lot in beginning R students is forgetting that R cares about case. In other words, the variable “a” is a separate thing than the variable “A”.

NOTE: Package names can be case-sensitive as well.

A lot of this comes down to “learn the syntax.”