working with unexpected NA values when performing time series analysis with the zoo package in R
I need help solving I'm building a feature where I tried several approaches but none seem to work..... I'm currently working on a time series analysis using the `zoo` package in R, and I've encountered an unexpected scenario where NA values are being introduced after converting my data into a `zoo` object. My data is a data frame with a date column and a corresponding value column. I expected the `zoo` object to handle the data smoothly, but after applying a moving average function, I'm seeing NA values that I need to seem to trace back to my original dataset. Here’s a snippet of what I’m doing: ```r library(zoo) data <- data.frame( date = as.Date('2023-01-01') + 0:9, value = c(1, 2, NA, 4, 5, 6, NA, 8, 9, 10) ) zoo_data <- zoo(data$value, order.by = data$date) # Applying a moving average with a window of 3 moving_avg <- rollapply(zoo_data, width = 3, FUN = mean, fill = NA) ``` After running this code, I expected the moving average to compute values for the valid entries, but I end up with unexpected NA values in the output. The resulting `moving_avg` looks like this: ```r [1] NA NA 2.000000 3.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 ``` I’ve checked for any leading or trailing NAs in my original data and tried using the `fill` parameter differently, but nothing seems to resolve this. How can I ensure that the moving average correctly computes without generating unwanted NA values? Any guidance on this would be very helpful! What am I doing wrong? This is my first time working with R 3.11. I'd really appreciate any guidance on this. I've been using R for about a year now. Any ideas what could be causing this?