Testing in R

R has its roots as a language in S, which was created back before the idea of object-oriented code was popularized, or the latest new languages were even invented. So, sometimes, testing takes a back burner in R for more reasons than the traditional software development excuses for not testing. It is erroneously believed to be hard to test code in R, or to setup a modern test framework, or to work in a test-first (test-driven) development manner. In truth, one can establish solid tests with a little planning and practice! In fact, anyone who writes code in R is already pretty good at testing their code – whether they know it or not! If you are using R, more than likely, you are producing some sort of statistical model or data analysis output. You inherently have to test your output this throughout the process – whether by inspecting (i.e. testing) the statistical model fit, or the graphical output of a distribution, the box plot, etc.

Ok, ok, but my point is that testing is not a foreign concept in R. In fact, it is the entire basis of analysis. So let’s put the “software/engineering testing” spin on the question. Testing in the most basic sense is easy in any language, and R is no exception. For the rest of this post we’ll consider segments of code in functions as an easy way to discuss and call discrete blocks of code. Good, reusable R code uses a lot of functions, so this is a great place to start testing. And I’m assuming if you are thinking about testing your code you are probably planning to use it more than once and likely share it with others.

my_function <- function (input) {
  if (!class(input) %in% c("integer", "numeric")) {
    stop("Invalid Input. Values should be integer or numeric") 
  }
  result <- NA
  if (input %in% c(1:5)) {
    result <- input * 2 
  } else if (input < 0) {
    result <- NaN 
  }
  return(result)
}

# ---------------------------------------------------------------------------------------------
# Tests a function for correct output results. The function does not need to be vectorized.
#
# Returns: a character vector of problems found with the results or NA if there are no issues 
#
# Note: This function will run all tests and return a vector of character string errors for
# the entire set of tests, not just the first error
# ---------------------------------------------------------------------------------------------
test_a_function <- function(tested_function,  # function to be tested
                            valid_in,         # one or more valid input values as a vector
                            valid_out,        # the matching valid output values as a vector
                            na_in = c(),      # function inputs that should return NA
                            nan_in = c(),     # function inputs that should return NaN
                            warning_in = c(), # function inputs that should return a warning
                            error_in = c()) { # function inputs that should cause a stop
... download the code file below (removed for brevity) 
}

> test_a_function(my_function,
+ valid_in = c(1, 3e0, 5),+ valid_out = c(2, 6, 10), + na_in = c(0, 10, 2.5, 10000.0),
+ nan_in = c(-1, -1.465),
+ error_in = c("fred", "5", TRUE))
[1] NA