Debugging a Known Bug with testthat and lintr

In the world of R package development, it’s not uncommon to encounter bugs and unexpected behavior. In this article, we’ll delve into a specific issue involving the testthat package and lintr, two popular tools used in R package testing. We’ll explore the problem, its root cause, and provide a solution that should help you avoid similar issues in your own projects.

The Problem: lintr::expect_lint_free() Fails with devtools::check()

The issue at hand is a known bug in lintr, which affects how it handles package linting. Specifically, the lintr::expect_lint_free() function fails when used with devtools::check(). This error occurs because lintr cannot determine which file(s) to lint.

Understanding the Context

To understand this issue, let’s take a step back and look at how these tools interact:

• testthat is a popular testing framework for R packages.
• devtools provides a set of functions for building and testing R packages.
• lintr is a tool for linting R code.

When you run devtools::check(), it executes your tests, including those defined with testthat. However, the issue arises when using lintr within these tests. The problem lies in how lintr determines which file(s) to lint.

The Current Workaround: Using find_package()

One workaround for this issue is to use a custom function, find_package(), to determine the package directory. This function mimics the behavior of lintr's own find_package() but avoids the problem by using a hardcoded path instead of relying on the file system.

Here’s an example implementation of find_package():

## Find package
# A duplicate copy of the find_package function from lintr
find_package <- function(path) {
  # Normalize path
  path <- normalizePath(path, mustWork = FALSE)

  # Loop until DESCRIPTION is found
  while (!file.exists(file.path(path, "DESCRIPTION"))) {
    # Move to parent directory
    path <- dirname(path)
    
    # Stop if we reach the root directory
    if (identical(path, dirname(path))) {
      return(NULL)
    }
  }

  # Return path
  path
}

Integrating find_package() with testthat

To use find_package() within your tests, you’ll need to create a context for linting. This involves checking if the package directory is available and then running lintr::expect_lint_free().

Here’s an example of how to integrate this into your tests:

if (requireNamespace("lintr", quietly = TRUE)) {
  library(lintr)

  # Create a context for linting
  context("linting package")
  
  test_that("Package Style", {
    # Use find_package() to get the package directory
    find_package <- function(path) {
      path <- normalizePath(path, mustWork = FALSE)
      
      while (!file.exists(file.path(path, "DESCRIPTION"))) {
        path <- dirname(path)
        if (identical(path, dirname(path))) {
          return(NULL)
        }
      }

      path
    }

    # Check if the package directory is available
    if (!is.null(find_package("."))) {
      expect_lint_free()
    }
  })
}

Conclusion

This workaround may seem complex, but it provides a reliable way to test your R packages using lintr without encountering issues. By creating a custom function for determining the package directory and integrating it with testthat, you can ensure that your tests accurately reflect your code’s quality.

When working with lintr and devtools, keep in mind the importance of understanding how these tools interact and how to work around known bugs like this one. By staying informed about package testing best practices, you’ll be better equipped to write high-quality R packages that meet the standards of the R community.

Recommendations

• Make sure to check for updates to lintr and consider updating your version if a newer release is available.
• Consider using other linting tools, such as r-amerling, which might avoid this specific issue altogether.
• When building and testing packages, always prioritize accuracy and reliability.

By implementing these strategies and staying informed about package testing best practices, you’ll be able to write robust, maintainable R packages that meet the needs of your users.

Last modified on 2024-07-02