Imagine you're working with a large data set in R and need to perform an operation on a list that's not memory-efficient. How would you handle this situation?

When working with a large data set in R and facing memory limitations with a list, you can handle the situation by processing the list in smaller chunks or subsets to reduce memory usage. This approach allows you to perform the operation incrementally, avoiding the need to load the entire list into memory at once. Additionally, utilizing lazy evaluation or on-demand processing can help optimize memory usage by computing values only when necessary. For extremely large datasets, implementing external memory algorithms or leveraging databases designed for efficient data processing can provide memory-efficient solutions.