implementing `purrr::map()` producing unexpected results when using a custom function in R
Hey everyone, I'm running into an issue that's driving me crazy. I'm currently using `purrr::map()` to apply a custom function over a list of data frames, but I am working with unexpected results. The custom function is designed to calculate the mean of a specified column for each data frame. However, when I run the code, I get inconsistent output, and some results return `NA` unexpectedly. Here’s the relevant part of my code: ```r library(purrr) library(dplyr) # Sample list of data frames list_of_dfs <- list( df1 = data.frame(a = c(1, 2, NA), b = c(4, 5, 6)), df2 = data.frame(a = c(NA, 1, 2), b = c(7, 8, 9)), df3 = data.frame(a = c(3, NA, 4), b = c(10, 11, 12)) ) # Custom function to calculate mean of column 'a' mean_col_a <- function(df) { mean(df$a, na.rm = TRUE) } # Applying the function using map() results <- map(list_of_dfs, mean_col_a) print(results) ``` When I run this code, I expect to get a list of means for each data frame, but instead I'm getting: ``` [[1]] [1] 1.5 [[2]] [1] 1.5 [[3]] [1] 3.5 ``` The outputs seem correct, but I also noticed that when the data frames have only `NA` in column 'a', the mean should return `NA`, yet I don't see this reflected in the output. I've tried adding `na.rm = TRUE`, but it seems to be ignored in cases where all values are `NA`. How can I ensure that the output reflects an `NA` when there are no available values to calculate the mean? Additionally, is there a better approach to handle this that might provide more clarity in the results? Any help would be greatly appreciated! My development environment is macOS.