Grouped summaries and validation

The idea

A grouped calculation separates observations by a key, applies the same operation to each group, and combines the results. In base R, split creates a list of groups that can be processed with lapply or vapply. Keep the group labels attached to the results so callers can identify each summary without guessing an order. Validate that the grouping key has the intended relationship to the observations.

Missingness changes the meaning of a summary. An all-missing group should not silently become evidence that a real measurement equals zero unless that is the stated requirement. A small helper can distinguish that case before using na.rm = TRUE. stop raises an error for unsupported input, while tryCatch can handle a failure at a boundary where the program knows what useful action to take.

known_mean <- function(x) {
  if (all(is.na(x))) return(NA_real_)
  mean(x, na.rm = TRUE)
}
values <- c(2, 6, NA, 10)
team <- c("A", "A", "B", "B")
means <- vapply(split(values, team), known_mean, numeric(1))
cat(means["A"], ":", means["B"], "
", sep = "")

4:10

Group A averages two known observations. Group B averages its one known observation, while the helper would return NA for an entirely missing group.