I am preparing raw clinical trial tables for a deep learning model. I need to filter out NA values from a dataframe in R, but I specifically require the exact integer positions of the removed rows rather than a filtered dataframe. What native function handles this?
3 answers
When your goal is to find the row numbers instead of the subsetted data when you filter out NA values from a dataframe in R, you want to bypass standard logical filtering. The cleanest way to achieve this is by using the which() function combined with is.na(). Running which(is.na(df$column)) evaluates the array directly and returns a standard vector of integer positions. This approach avoids creating unnecessary dataframe copies in memory, making it ideal for high-throughput pipelines.
Does the combination of which and is.na provide a noticeable performance advantage over simply resetting the row names with rownames(df) and filtering the resulting character vector?
Another native way is using complete.cases(df), which returns a logical vector indicating which cases are complete.
Good point, Pamela. The complete.cases function is highly efficient because it handles matrix-like structures simultaneously, making it perfect when you have an entire dataset to check for completeness.
Jeffrey, it is significantly faster. Querying character string rownames forces R to allocate new character vectors in memory. On the other hand, which operates on internal logical arrays in place, helping you filter out NA values from a dataframe in R and extract positions much quicker.