advanced patterns when using dplyr's mutate with custom functions and grouped data
After trying multiple solutions online, I still can't figure this out... I'm trying to create a new column in my data frame using a custom function within a `mutate` call from the `dplyr` package, but I'm running into unexpected results when the data is grouped. Here’s the code I'm using: ```r library(dplyr) set.seed(123) df <- data.frame( group = rep(letters[1:3], each = 5), value = rnorm(15) ) custom_function <- function(x) { return(mean(x) + sd(x)) } result <- df %>% group_by(group) %>% mutate(new_value = custom_function(value)) print(result) ``` In the output, I expect `new_value` to contain the same value for all rows within each group, since it’s based on the mean and standard deviation of the `value` column for that group. However, I see that `new_value` gets assigned the same mean plus standard deviation for the first row of each group only, and all other rows show `NA` values. I believe this has to do with how `mutate` treats the output of `custom_function`, but I'm not sure how to fix it. When I run the code, I receive warnings like: `In custom_function(value): NAs produced by integer overflow` This suggests that something is going wrong with my aggregation within the function, but I’m not sure how to handle it properly within a grouped context. I've also tried using `summarize()` before `mutate()` but that did not achieve the desired result. Any ideas on how to correctly apply my custom function here, or do I need to rethink my approach? I'm working on a web app that needs to handle this. Any help would be greatly appreciated!