Handling grouped lag calculations in R with dplyr resulting in unexpected output
I recently switched to After trying multiple solutions online, I still can't figure this out. I'm trying to calculate the lag of a variable within groups in a data frame using `dplyr`, but I'm running into unexpected results. Specifically, I want to create a new column that stores the lagged values of a column called `sales` for each `store_id`. Here's a snippet of my code: ```r library(dplyr) # Sample data frame df <- data.frame( store_id = c(1, 1, 1, 2, 2, 2), sales = c(100, 150, 200, 300, 350, 400), date = as.Date(c('2021-01-01', '2021-01-02', '2021-01-03', '2021-01-01', '2021-01-02', '2021-01-03')) ) # Attempting to calculate lagged sales result <- df %>% arrange(store_id, date) %>% group_by(store_id) %>% mutate(lag_sales = lag(sales)) ``` However, the resulting data frame contains `NA` for the first entry of each `store_id`, which is expected, but when I print the `result`, I notice that the lagged values for `store_id` 1 seem correct, while for `store_id` 2, it returns the values from `store_id` 1 instead of following its own group. The output looks like this: ``` store_id sales date lag_sales 1 1 100 2021-01-01 <NA> 2 1 150 2021-01-02 100 3 1 200 2021-01-03 150 4 2 300 2021-01-01 <NA> 5 2 350 2021-01-02 300 6 2 400 2021-01-03 350 ``` I expected the `lag_sales` for `store_id` 2 to be `<NA>` for the first entry and `300` for the second entry, but it seems to be pulling from the previous group instead. I've tried rearranging the `arrange()` command and double-checking the grouping, but I'm still getting this odd behavior. I'm using `dplyr` version 1.0.7. Did I miss something in how `mutate` and `lag` work together with grouped data? My team is using R for this microservice. Thanks, I really appreciate it! This is happening in both development and production on Ubuntu 22.04. Has anyone else encountered this?