👀 Views: 208 💬 Answers: 1 📅 Created: 2025-07-14

r dplyr data-manipulation R

I'm refactoring my project and I'm performance testing and I'm sure I'm missing something obvious here, but I'm working with a grouped data frame in R where I need to create a new variable based on a condition using `dplyr::mutate()` and `if_else()`... However, I am working with unexpected `NA` values in the resulting column after the operation. My data frame looks something like this: ```r library(dplyr) # Sample data set.seed(123) my_data <- data.frame( group = rep(1:3, each = 5), value = rnorm(15, mean = 10, sd = 2) ) # Group by 'group' and create a new column based on a condition my_data <- my_data %>% group_by(group) %>% mutate(new_value = if_else(value > 11, "High", "Low")) ``` When I run the above code, I notice that some entries in the `new_value` column are unexpectedly set to `NA`. Upon checking, I see that it happens for values that are equal to or less than 11, which should not result in `NA` since the `if_else()` function should return "Low" in those cases. I suspect this might be due to missing values or the way `if_else()` handles the logical condition within a grouped context. I've also tried switching to `case_when()` and still faced similar issues: ```r my_data <- my_data %>% mutate(new_value = case_when( value > 11 ~ "High", TRUE ~ "Low" )) ``` I still see `NA` values in `new_value`. I've verified that there are no NA values in the `value` column prior to this operation. I’m using `dplyr` version 1.0.7. Can anyone guide to understand why `NA` values are appearing and how I can avoid this scenario? I've been using R for about a year now. Has anyone dealt with something similar? The project is a CLI tool built with R. What's the correct way to implement this?