I am trying to learn more about the apply functions. When should I use apply() vs lapply() or sapply()? Is there a specific use-case where apply() is superior?
The apply function operates on the margins of arrays or matrices, whereas lapply and sapply are designed to iterate over list-like objects and vectors, respectively.
3 answers
You should view these functions based on the return type you need and the dimensions of your input object.
- Use apply for margin-specific transformations on arrays or matrices where row or column dimensionality matters.
- Use lapply when you need to return a list, which is the most reliable way to handle heterogeneous data structures.
- Use sapply as a wrapper for lapply when you prefer an automatic simplification of the output into a vector or matrix.
The apply function is specifically designed to perform margin-based operations across rows or columns of a matrix or dataframe. Unlike lapply or sapply, which are built for list or vector iteration, apply provides the structural precision needed when you must maintain dimensionality during a transformation.
I recall back in a legacy trial study when I had to perform row-wise adverse event counts across thousands of patient records stored in a matrix. Using anything other than apply would have collapsed the data structure, which would have been catastrophic for my reporting requirements at that time.
Using apply over the other options is only superior when you are performing reduction operations on multidimensional objects. If you are dealing with a standard list or a simple vector, apply is actually a performance bottleneck compared to the specialized lapply. You should only reach for apply when your specific use-case necessitates row-wise or column-wise aggregation in a non-list environment.
