Handling non-standard missing values in a nested list structure with purrr in R
I'm dealing with I'm working with a nested list structure in R, and I'm trying to replace non-standard missing values (like strings 'NA', 'null', and blanks) with actual NA values. I initially attempted this using `purrr::map` but encountered unexpected behavior. Here's a simplified version of my data: ```r nested_list <- list( a = list(values = c(1, 2, 'NA', 4)), b = list(values = c('null', 3, 4)), c = list(values = c(5, '', 6)) ) ``` I want to convert those non-standard missing values into NA throughout the entire list. I tried using `map()` with a custom function: ```r library(purrr) clean_list <- map(nested_list, function(x) { x$values[x$values %in% c('NA', 'null', '')] <- NA return(x) }) ``` However, when I print `clean_list`, the values that should have been replaced remain unchanged. Instead, I see that the `NA` values are just being ignored. The output looks like this: ```r > clean_list $list$a [1] 1 2 'NA' 4 $list$b [1] 'null' 3 4 $list$c [1] 5 '' 6 ``` Why are the replacements not working as intended? Is there a more effective way to handle this while ensuring that those non-standard missing values are correctly converted to NA? Any advice on how to properly manage this or best practices for dealing with similar structures would be greatly appreciated. I'm using R version 4.1.0 and `purrr` version 0.3.4. I'm working on a service that needs to handle this. What's the best practice here? I'm working in a Windows 10 environment. What am I doing wrong?