Difficulty in using `lapply()` with custom functions and data frames in R - results inconsistent
I'm relatively new to this, so bear with me. I'm getting frustrated with Hey everyone, I'm running into an issue that's driving me crazy. I am working with inconsistent results when using `lapply()` with a custom function on a list of data frames. Specifically, I have a list of data frames where I want to apply a function that computes the mean of a specified column, but I'm seeing unexpected behavior when some data frames contain missing values. Here’s a simplified version of what I’m trying to achieve: ```r # Sample data set.seed(123) df1 <- data.frame(id = 1:5, value = c(1, 2, NA, 4, 5)) df2 <- data.frame(id = 1:5, value = c(2, 3, 4, 5, 6)) list_of_dfs <- list(df1, df2) # Custom function to calculate mean mean_value <- function(df) { mean(df$value, na.rm = TRUE) # ignore NA values } # Applying lapply() means <- lapply(list_of_dfs, mean_value) print(means) ``` When I run this code, I expect `means` to return a list with the mean values for `df1` and `df2`, ignoring the `NA` in `df1`. However, instead of getting the expected output: `list(3, 4)`, I'm getting unexpected results: `list(NA, 4)`. I’ve also tried using `sapply()` instead of `lapply()`, but I see the same scenario. I suspect the question may be related to how `lapply()` handles the list elements or the NA values in the data frames. I’ve tried setting `na.rm = TRUE` within the `mean()` function, and I've confirmed that the data frames do exist and are in the correct format. Any insights on why this might be happening and how I can fix it would be greatly appreciated! How would you solve this? This is part of a larger web app I'm building. The stack includes R and several other technologies. Any pointers in the right direction? This is for a CLI tool running on Ubuntu 20.04. I'd be grateful for any help.