When I need to apply multiple functions to multiple columns sequentially and aggregate by multiple columns and want the results to be bound into a data frame I usually use aggregate() in the following manner:
# bogus functions
foo1 <- function(x){mean(x)*var(x)}
foo2 <- function(x){mean(x)/var(x)}
# for illustration purposes only
npk$block <- as.numeric(npk$block)
subdf <- aggregate(npk[,c("yield", "block")],
by = list(N = npk$N, P = npk$P),
FUN = function(x){c(col1 = foo1(x), col2 = foo2(x))})
Having the results in a nicely ordered data frame is achieved by using:
df <- do.call(data.frame, subdf)
Can I avoid the call to do.call() by somehow using aggregate() smarter in this scenario or shorten the whole process by using another base R solution from the start?
As @akrun suggested, dplyr's summarise_each is well-suited to the task.
library(dplyr)
npk %>%
group_by(N, P) %>%
summarise_each(funs(foo1, foo2), yield, block)
# Source: local data frame [4 x 6]
# Groups: N
#
# N P yield_foo2 block_foo2 yield_foo1 block_foo1
# 1 0 0 2.432390 1 1099.583 12.25
# 2 0 1 1.245831 1 2205.361 12.25
# 3 1 0 1.399998 1 2504.727 12.25
# 4 1 1 2.172399 1 1451.309 12.25