implementing NA propagation in a custom function using dplyr's mutate in R
I'm prototyping a solution and I've been banging my head against this for hours... I'm working with an scenario where NA values are being propagated unexpectedly when using a custom function within `dplyr::mutate()`. I'm running R version 4.1.0 and using the `dplyr` package version 1.0.7. My goal is to create a new column based on a conditional calculation involving another column. However, whenever the input column has NA values, my function seems to return NA for the entire new column, not just those rows that correspond to NA inputs. Here's the relevant snippet of my code: ```r library(dplyr) # Sample data frame df <- data.frame(id = 1:5, value = c(10, NA, 30, 40, NA)) # Custom function that calculates a new value based on some conditions custom_function <- function(x) { if (is.na(x)) { return(NA) } else if (x < 20) { return(x * 2) } else { return(x + 10) } } # Attempting to mutate the data frame df <- df %>% mutate(new_value = custom_function(value)) ``` After running this, I expect that rows where `value` is NA should produce NA in `new_value`, but instead, I am getting all NA values in `new_value` when I use this function. I've tried using `na_if()` before applying the function, but that didn't resolve the question. Moreover, I checked the function separately with test values and it works as expected, returning results only for non-NA inputs. Is there a specific way to prevent NA propagation in such a scenario or a better approach to handle this within `mutate()`? Any insights would be appreciated! What am I doing wrong? The project is a REST API built with R.