Hot-fixing R Functions When Discovering Bugs

In a short period of time, I’ve encountered two situations where I needed to fix bugs in R functions, and I’ve also learned how to perform hot replacements…

Why Hot-fixing is Needed?

In data analysis projects, time is often a critical factor. If there are bugs in the R package functions we depend on, it can block the entire analysis workflow. While reporting bugs to package maintainers and helping with fixes is the best way to give back to the open-source community… you can’t really tell your boss, “I’ve reported the bug to the author, and we’ll get the correct results sometime in the distant future!”

So finding the problem yourself and performing a hot-fix is more practical.

The Most Basic Fix - Replacement

Based on my years of experience, although R supports objects, not many packages actually use them this way. At least most bioinformatics packages are still functional. Therefore, the simplest approach is to replace the problematic function with a modified version.

First, we can locate the function to be fixed, then use print(YOUR_FUNCTION) to get the code of the corresponding function in your current working environment.

Of course, sometimes the function we need to fix is not exported by the package for user use - it might be an internal function of the package. In this case, we need to use package_name + ::: + function_name to get the code.

After modifying the function, simply use <- to overwrite the original function to complete the modification. For example, my solution to the Azimuth problem was to directly modify and replace the function this way.

Setting the Namespace After Modifying Functions

Of course, most functions can’t be fixed as simply as described above because most functions are not isolated. They might need to call other functions within the original package, or they might be non-exported internal functions. In such cases, simply replacing them in the current namespace won’t work - we also need to replace the original function in the corresponding package’s namespace. The steps are as follows:

R provides the assignInNamespace function, which allows us to replace objects in a package’s namespace.

1. Use assignInNamespace to set the function’s environment to the package’s namespace
2. Assign the modified function back to the package’s namespace

The actual operation is quite simple:

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# Write the modified function
createOncoMatrix <- function(
    print('Modified!')
)

# Set the function environment to the package's namespace
environment(createOncoMatrix) <- asNamespace("maftools")

# Replace the modified function back to the package's namespace
assignInNamespace("createOncoMatrix", createOncoMatrix, ns = "maftools")

Afterword

Although I’ve documented the hot-fixing methods, I still hope I won’t encounter such problems again… A 30-minute analysis turning into an entire afternoon of debugging…