Writing Good Tests In R

Kevin Feasel

2016-07-08

R, Testing

Brian Rowe discusses testing strategy in R:

It’s not uncommon for tests to be written at the get-go and then forgotten about. Remember that as code changes or incorrect behavior is found, new tests need to be written or existing tests need to be modified. Possibly worse than having no tests is having a bunch of tests spitting out false positives. This is because humans are prone to habituation and desensitization. It’s easy to become habituated to false positives to the point where we no longer pay attention to them.

Temporarily disabling tests may be acceptable in the short term. A more strategic solution is to optimize your test writing. The easier it is to create and modify tests, the more likely they will be correct and continue to provide value. For my testing, I generally write code to automate a lot of wiring to verify results programmatically.

I started this article with almost no idea how to test R code.  I still don’t…but this article does help.  I recommend reading it if you want to write production-quality R code.

Related Posts

Beware Multi-Assignment dplyr::mutate() Statements

John Mount hits on an issue when using dplyr backed by a database in R: Notice the above gives an incorrect result: all of the x_i columns are identical, and all of the y_i columns are identical. I am not saying the above code is in any way desirable (though something like it does arise naturally in certain test […]

Read More

Unit Testing Spark Streaming DStreams

Anuj Saxena shows how to create unit tests for DStreams in Spark Streaming: The method ‘ testOperation ‘ takes the output of the operation performed on the ‘inputPair’ and check whether it is equal to the ‘outputPair’ and just like this, we can test our business logic. This short snippet lets you test your business logic without […]

Read More

Categories

July 2016
MTWTFSS
« Jun Aug »
 123
45678910
11121314151617
18192021222324
25262728293031