scenarios in custom R function for calculating weighted averages using dplyr across multiple groups
I'm converting an old project and I tried several approaches but none seem to work... I've been struggling with this for a few days now and could really use some help. Quick question that's been bugging me - I'm stuck on something that should probably be simple... I'm working with an scenario with my custom R function that calculates weighted averages for multiple groups in a data frame using the `dplyr` package. My data frame `df` contains three columns: `group`, `value`, and `weight`, where `group` indicates the category, `value` is the numeric value, and `weight` is the associated weight for that value. I want to compute the weighted average for each group but I'm getting `NA` results unexpectedly. Here's my function: ```r library(dplyr) calculate_weighted_avg <- function(data) { data %>% group_by(group) %>% summarize(weighted_avg = sum(value * weight, na.rm = TRUE) / sum(weight, na.rm = TRUE)) } ``` When I run this function with my data frame: ```r df <- data.frame( group = c('A', 'A', 'B', 'B', 'C', 'C', 'C'), value = c(10, 20, 30, 40, 50, NA, 60), weight = c(1, 2, 1, 1, 1, 1, 0) ) calculate_weighted_avg(df) ``` I receive the following output: ``` # A tibble: 3 Ã 2 group weighted_avg <chr> <dbl> 1 A 16.7 2 B 35 3 C NA ``` The weighted average for group C is returning `NA`, which doesn't seem right since group C has valid values. I suspect that it may be related to the way NA values are being handled in the `summarize` function. I've tried using `na.rm = TRUE` in both the numerator and denominator, but it still returns `NA`. I also tried different approaches by checking for NA values in the `weight` column, but it didn't change the outcome. Any insights on why this might happen, or how I can modify the function to correctly compute the weighted average for all groups? I'm using R version 4.1.0 and dplyr version 1.0.7. For context: I'm using R on macOS. I'd really appreciate any guidance on this. For context: I'm using R on Ubuntu. Any ideas what could be causing this? Any ideas what could be causing this? I'm coming from a different tech stack and learning R. I'm open to any suggestions. My team is using R for this web app. I'm developing on CentOS with R.