Trouble with date filtering in R using dplyr - unexpected results with grouped data
I'm collaborating on a project where I'm currently working with a DataFrame that contains a `date` column in the format `YYYY-MM-DD` and I need to filter the data for a specific year while also performing some summary statistics grouped by another categorical variable... However, I'm working with unexpected results when applying `dplyr` functions. Specifically, I expect my filtered dataset to contain only entries from 2022, but I'm getting more rows than anticipated. Here's a snippet of my code: ```r library(dplyr) # Sample data frame df <- data.frame( id = 1:10, category = rep(c('A', 'B'), each = 5), date = as.Date(c('2022-01-01', '2022-02-01', '2022-03-01', '2021-11-01', '2021-12-01', '2022-01-15', '2022-02-15', '2022-03-15', '2022-04-15', '2022-05-15')) ) # Attempting to filter for 2022 and summarize filtered_df <- df %>% filter(format(date, '%Y') == '2022') %>% group_by(category) %>% summarise(count = n()) print(filtered_df) ``` When I run this, I expect to see counts only for category 'A' and 'B' entries from 2022, but I also see unexpected results, such as additional rows or incorrect counts. After checking the output of `print(df)`, I confirmed that there are indeed only 5 rows from 2022, but the summarised output shows discrepancies. I also tried using `year(date) == 2022` from the `lubridate` package but that gave me similar problematic results. Am I missing something in the filtering process, or is there a more efficient way to achieve this without running into such issues? Any help would be greatly appreciated! I'm working with R in a Docker container on macOS. Any ideas how to fix this? For reference, this is a production CLI tool. Could this be a known issue?