Category: R

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.”