Understanding Environmental Issues with `testthat`

Introduction

In this article, we’ll delve into the world of R’s testthat package and explore some environmental issues that can arise when writing tests. Specifically, we’ll examine how to handle complex functions with multiple wrapper functions and use cases involving eval() and match.call(). Understanding these concepts is crucial for writing robust and efficient tests.

Background

The testthat package provides a suite of tools for writing and running tests in R. One of its key features is the ability to create test cases that can be run using the test_that() function. This function allows us to define test suites, write assertions, and check the output of our functions.

However, when dealing with complex functions that involve multiple layers of wrapper functions, things can get complicated. In this article, we’ll discuss how environmental issues can arise in these scenarios and provide solutions for overcoming them.

The Problem: `eval()` and `match.call()`

In the provided example code, we have a main function main that takes three arguments: a, b, and c. We also have two wrapper functions: wrapper and helper.

The wrapper function is used to bundle some of the arguments passed to main into a list. The helper function, on the other hand, is an intermediate helper function that takes the result of match.call() as input.

Here’s how the code looks:

library(testthat)

wrapper <- function(a, b) {
  fun_call <- as.list(match.call())
  ret <- helper(fun_call)
  return(ret)
}

helper <- function(fun_call) {
  fun_call[[1]] <- quote(main)
  fun_call <- as.call(fun_call)
  fun_eval <- eval(as.call(fun_call))
  return(fun_eval)
}

main <- function(a, b, c = 1) {
  ret <- list(a = a, b = b, c = c)
  return(ret)
}

Now, let’s create a test suite using test_that():

test_that("Test", {
  a <- 1
  b <- 2
  x <- wrapper(a = a, b = b)
  y <- list(a = 1, b = 2, c = 1)
  expect_equal(x, y)
})

When we run this test suite manually, it passes. However, when we evaluate the entire test_that() chunk using eval(), the test fails because one of the arguments can’t be found.

The Solution: Understanding Environmental Contexts

The issue arises because the environment context within which the tests are executed differs from the one in which the functions are defined.

To solve this problem, we need to understand how environmental contexts work and how to modify them when needed. In R, each function has its own local environment, which is separate from the global environment (i.e., the parent frame).

When we use eval() or match.call(), it creates a new stack frame with its own environment context. This can lead to unexpected behavior if not handled properly.

Solution 1: Using `eval.parent()`

One way to resolve this issue is by using eval.parent() instead of eval() when creating our test suite.

helper <- function(fun_call) {
  fun_call[[1]] <- quote(main)
  fun_call <- as.call(fun_call)
  fun_eval <- eval.parent(fun_call, n=2)
  return(fun_eval)
}

By using eval.parent(), we ensure that the test suite is executed within the parent frame of the original environment context. This way, all variables and functions are available in the correct scope.

Solution 2: Modifying the Wrapper Function

Another approach to this problem is by modifying our wrapper function to bundle only the required arguments into a list.

wrapper <- function(a, b) {
  helper(mget(ls()))
}

helper <- function(params) {
  do.call("main", params)
}

In this revised version, wrapper simply bundles its parameters values into a list using mget(). Then, we pass that list as an argument to the helper() function. The do.call() function then calls the main() function with those parameters.

This solution avoids the need for explicit environment manipulation and makes our code more readable.

Conclusion

Writing effective tests in R involves understanding the intricacies of environmental contexts and how to handle them appropriately. In this article, we explored some common pitfalls that can arise when using functions like eval() and match.call(), as well as solutions for resolving these issues.

By choosing the right approach depending on your specific use case and requirements, you’ll be able to write more robust and efficient tests for your R code.

Last modified on 2023-09-13

Understanding Environmental Issues with testthat