The “cannot allocate vector of size” error message occurs when there isn’t enough available memory (RAM) capacity to allocate a vector , array, or objects in R. You’ll see this error when you are working with large data sets or managing the allocation of large data objects. The good news this is usually isn’t a code error (like other R errors) but rather a system constrain that you’re going to need to maneuver around.
Why You’re Seeing This R Error
The “cannot allocate vector of size” memory error message occurs when you are creating or loading an extremely large amount of data that takes up a lot of virtual memory usage. When dealing with such large datasets it is possible to actually run out of usable memory. It is most likely to happen when a data set is loaded in from an external source such as a package or another type of file. This is because you cannot control the size of an object contained in a source that you did not create. It is most likely to occur when accessing the function to load or create a vector or other data set. They can also occur with smaller objects if you have an extremely large number of objects in your program.
A List of More Specific Causes of the R Error
The cause of the “cannot allocate vector of size” error message is a virtual memory allocation problem. That being said, here are some specific things to watch out for with this error:
- Memory allocation problems due to other applications open on your machine. The total memory usage from the other applications is consuming the available memory capacity.
- Bumping into the memory size limits for the R application. You can use the memory.limit() function to manage this on Windows systems.
- Sloppy memory allocation practices and data model design in designing your program; look for things like sparsely populated arrays and matrices, excessive large objects, or loading unused packages and code into the R environment.
- Limit vector size, array length, and column definitions to the values that you actually need; this includes being aware of na values, which occupy memory regardless. Consider making performance tradeoffs between holding data in memory and generating values via a function or a code loop.
- Difficulties recycling unused element(s) in R memory management. If you need them, look to move them into storage on disk or other environment.
Ways To Fix This Error
The “cannot allocate vector of size” memory issue error message has several R code solutions. The best thing about these solutions is that none of them is overly complicated, most are a simple single process that is easy to do in your R script.
- The simplest solution is to avoid using overly large objects or excessively large numbers of them in one program or R session, for example try removing unneeded objects from your calculation to better fit within your total allocation of memory.
- When reading in an external file enclose the read() function inside a subset() function resulting in the format of subset(read.(“filename”, header = TRUE), select = c(columns to be kept)) this will reduce the size of individual objects being created by removing unwanted columns.
- You can clear out unneeded objects using the rm() function.
- You can clean out address space using the gc() function.
- You can also test for a limit to the free memory using the memory.limit() and set a higher value with the format of memory.limit(size=number).
- Shut down any programs you are not using to clear up extra space.
Finding the best process to fix this error may take some trial and error but they are not hard or time-consuming. While this error message is not a coding problem it is still not difficult to fix within your R session.