An R session aborted fatal error can be among the more frustrating problems you’ll run into when using R. One of the biggest issues is that it’s often resistant to reproduction or creating a reproducible example. Your code might work fine in one environment while crashing with the error in another. And this also points to the underlying issue. The error typically occurs when there’s a conflict between your R code and the environment where it’s being run. As such it can usually be fixed by simply working through the exact versioning of your code’s requirements and the requirements of the environment you’re running it in. This isn’t always the most straightforward process. However, you can generally simplify it by considering a few important points.
A General Overview of Session Aborted Errors
An R error message is usually fairly descriptive. It might use language or syntax that is a bit outside the norm. But for the most part, the R error message will point you toward the underlying problems with your code. However, this is one of the rare exceptions where that’s generally impossible. The big issue is that the error signals that the very system normally in charge of creating the error messages has crashed. In general, the entire session has just aborted without warning. That means everything related to the runtime process will have stopped working.
As you might imagine, this also makes tracking down the root cause a little more difficult. Somewhat akin to someone expecting you to fix a car whose sole descriptor is “won’t drive”. Thankfully, there are ways to narrow down the potential cause of the issue. But it’s not quite as straightforward as troubleshooting most R errors. You’ll instead need to mentally organize, categorize, and troubleshoot your code based on specific criteria.
A Deeper Dive Into What’s Going On
To begin fixing the problem you’ll need to consider what would cause a full fatal error in the R interpreter or your IDE. Think about some of the things that are major concerns when you’re writing your own code to handle different data collections. You’ve probably had your scripts error out due to incompatibility with the data you’ve supplied, the format of that data, memory limitations, etc. And the same thing can happen with the system running your scripts.
The R platform and the major IDEs associated with it are extremely mature and well-tested. But that doesn’t mean that they’re invincible. Anything that deals with varied input has the risk of severe incompatibility and fatal crashes come up. And the reasons are often the same as in your own code. That said, some potential pitfalls are more common than others. And you can generally solve this problem by starting out with the most common causes and then narrowing things down as you work through the list.
Practical Solutions
One of the most common causes for this issue comes down to compatibility between R studio or similar IDEs, your R code, and the R packages you’re using. Many R IDEs are tightly integrated with the R language and libraries. And if an incompatibility in the R version comes up the entire system could terminate. For example, the R 4.1 release changed elements of its graphics rendering system and this caused fatal errors on older versions of R Studio. The solution in this case is generally fairly simple. You can just move the version of R you’re using to a lower version. In general you’ll probably just need to go down one R version if this is the underlying cause of the error.
Likewise, an R package might also be the underlying cause of a fatal error. As with the R versioning, this often happens because an incompatibility sprang up within a new release of the package. Tracking down instances of this issue can be a little tedious if you’re using a lot of different packages. But you can essentially just try to reduce the version of every package you’re using. If this is the root cause then the fatal error will eventually stop occurring. Once you know which package caused the compatibility issue you can restore all of the others to their original version.
There’s also a possibility that the problem is simply due to issues with the currently active R session. Simply restarting the instance, or if possible the entire operating system, will often solve the problem. The tried and true tip of “Have you tried turning it off and on again?” can work wonders here. And a similar principle holds true for web-based R IDEs.
Keep in mind that an R IDE is essentially dependent on multiple environments. It can encompass the R interpreter, underlying operating system, web browser with its Javascript JIT compiler, and runtime bridges like Rcpp. That’s a lot of moving pieces and it’s not too surprising that communication issues between them might show up. A connection problem between any of them could result in improperly formatted data crashing the system. But, again, restarting everything will often fix those issues.
Fatal errors also commonly occur as a symptom of a memory problem. For example, if your script is using a huge data structure then you might be burning through all of your available memory. Once again, restarting the environment can often help with this issue as it’ll instantly unload all of the non-base libraries. This can free up a little more memory. You can also try to manually free up memory with the following code.
To remove a particular object you could simply use the following.
rm(“myObject”)
And to remove a package from memory you could use this piece of code.
detach(“package:myPackage”, unload = TRUE)
If you’re self-hosting an R IDE then it’s also important to consider any recent changes you might have made to that environment. Have you seen a similar issue in any other web-based systems on your server? These can often point to a recent environment or operating system update as the core problem.
You should also try to try running your code in different ways. For example, if you’re seeing the problem show up when you’re using an IDE then try to run it on the command line. And, likewise, you can take that in the other direction as well. This can help point to possible issues with an IDE rather than the code or libraries.
This is generally one of the more difficult R errors to track down. But it all essentially boils down to a central principle. Work under the assumption that it’s caused by two pieces of your code, environment, or libraries that aren’t properly fitting in with each other. Then track down which of those many pieces is the odd man out.