The R encountered a fatal error message is generally fairly rare. But, unfortunately, it’s also one of the more difficult errors to properly debug as your R session will be instantly terminated. The sudden cessation stems from the nature of a fatal error. Something has gone wrong with an important element of the R environment as a whole. To the point where it’s not providing you with the information that would normally facilitate proper debugging. However, the number of issues that can cause those errors is relatively small. And, as you’ll see, you can go through them in order of probability to essentially narrow things down until you’ve tracked down the underlying cause.
What’s Actually Happening When You See the Error?
You’ve probably fed the wrong type of data into a function at some point in your programming career. A function will ideally be designed to alert you to the problem. But theory and practice aren’t always the same thing. And you very well might have had the process misbehave or shut down. For example, if your code assumes it’s dealing with an integer and it’s actually a string then the function’s almost certainly not going to behave as expected. Most interpreted languages will be able to neatly exit and explain the underlying problem. But there are also cases where things will go so wrong that the interpreter doesn’t know how to proceed and will simply crash. This is generally what happens with a fatal error in R. Something, somewhere, is interfering with the system’s ability to process data.
The problem generally stems from incompatibilities of one type or another. From the use of incompatible data types, versioning errors with libraries, or attempts to work with unavailable memory. You can think of it as somewhat analogous to a computer that’s been shut down. If everything’s going right on a monitored system you’d have a nice log file showing the reason for the shutdown. But if something went terribly wrong in a way the computer couldn’t understand then it might simply shut down without being able to properly record the process. That’s essentially what a fatal error in R entails.
Further Complications When Pinpointing the Source of the Error
The previous conditions paint a complex picture. But things can get even more convoluted when you’re using a system that has multiple interlinked dependencies. And that tends to be the case for most modern R development cycles. When people use R they’re generally not just using the R interpreter. They’re also using external libraries, IDEs like RStudio, systems that link to other runtimes, and of course all of the dependencies required by those items. Packages like gputools even provide access to a computer’s GPU. Use of CUDA cores, for example, vastly increases what R code is capable of. But it also means that the code now has a bridge from IDE, to R interpreter, to the library for CUDA communication, to the C library, and finally to the GPU’s CUDA cores. All of this gets down to the crux of why fatal errors are often hard to isolate. You’re generally looking for a subtle problem within a complex system. But with that in mind, there are criteria you can use to help narrow things down and figure out the most probable cause for your fatal exception error.
Tracking Down the Root Cause and Implementing a Fix
The best place to start is with the easiest issues to fix. Take a moment to consider when the problem first appeared. Had you updated the IDE, your operating system, or even the libraries in your project? This is the most common explanation for the issue when it seemingly starts out of nowhere. Remember too that your operating system, package manager, or other automated systems might have installed an update without you specifically initiating it. Most IDEs will let you quickly move through your packages and downgrade them as needed in order to test them for incompatibility. It’s a good idea to give that a try before moving on to more complex fixes.
Secondly, memory is also a common cause for this particular problem. A lot of people working with smaller datasets assume that they don’t need to worry about RAM. But remember that you’re generally not just storing a data collection in memory. Your code is going to be actively manipulating it. Datasets are better thought of as living information that can grow over the course of your work. And this can even be an exponential process where a small dataset quickly snowballs into something massive. So take a moment to check your data collections to see what’s going on with them and whether they might be taking up too much memory. If they are then you’ll want to optimize your code to properly compensate for their size. This might seem intimidating, but the overall knowledge base on data optimization is quite extensive at this point.
Next, you should think about the larger environment. Have you updated your IDE recently? What about the version of R being used by that IDE? For example, it’s quite possible for RStudio to encounter problems when running with an incompatible version of R. The same thing goes for libraries that interface with RStudio. For example, if a library leverages that IDE’s graphing system then an update in one that doesn’t account for the other might cause fatal errors.
And, as with the previous consideration, you should think about whether your operating system might have updated any core libraries connected to R and your IDE. If an update had been recently applied then it might be the root cause of your problem. You can always try reinstalling your IDE or the underlying framework it’s running in. And that also includes Java if that’s a part of your IDE’s system.
If the problem still persists at this point then there’s a good chance of it being related to your hardware. If you’re on a Windows PC, think about whether you’ve seen a window appearing with information that might relate to this situation. Likewise, if you’ve seen the standard Windows blue screen and error code message appear recently then it might be a clue. Keep in mind as well that many of Window’s error message alerts can be fairly subtle and you might not have even noticed them.
Additionally, remember the earlier points about data incompatibility. That doesn’t always stem from your own programming. For example, if you’re loading data from a file then a problem with your hard drive could result in data loss or corrected data within your program. Hard drive issues can cause corruption, and corrupt files lead to corrupt data. Your graphics card can cause similar problems if your program makes use of CUDA, or even leverages its VRAM. And remember that a lot of different programs work with your graphics card. Game files in particular will often install updates to your graphics driver as part of their own updates. Which can, in turn, cause problems for anything related to advanced data processing.
You can often track down both of these issues through the Windows device manager. To load it you can bring up a command prompt or the run command and type “devmgmt.msc”. Then look for any devices with a driver problem. You can often simply find problematic devices, update driver or drivers as needed, and then reboot your computer to fix the problem.
Unfortunately, there might still be instances where hardware is to blame but manually working with the hardware manager doesn’t fix the problem. In that case, you might need to do a system restore from automatic repair. You can generally get into automatic repair by holding down your power button to do a hard shutdown right as Windows starts to boot. After this is done a couple of times Windows should boot into the repair system. If not, you can also use Windows installation media to boot into it. No matter how you boot into the repair tool, you’ll be able to restore your system from a previous restore point. This will roll back all of your operating system’s drivers to an older version. And in turn, the older drivers might restore compatibility with R if they were the underlying cause of the problem.