Aggregating frequency tables in R

Question

I would like to aggregate data frames A, B, and C by rows and columns to obtain D.

A <- data.frame(A = c("John","Fred","Paul"), Money = c(5,20,10), Hats = c(1,2,2))
B <- data.frame(A = c("John","Fred"), Money = c(15,10), Hats = c(1,2))
C <- data.frame(A = c("Paul"), Money = c(20), Hats = c(1))

D <- data.frame(A = c("John","Fred","Paul"), Money = c(20,30,30), Hats = c(2,3,3))

Which one would it be the fastest way in R?

Answer 1

You could do:

aggregate(.~A, do.call(rbind,list(A,B,C)), sum)

     A Money Hats
1 Fred    30    4
2 John    20    2
3 Paul    30    3

or simply

aggregate(.~A, rbind(A,B,C), sum)

     A Money Hats
1 Fred    30    4
2 John    20    2
3 Paul    30    3

Answer 2

Using dplyr:

library(dplyr)
bind_rows(A,B,C) %>% group_by(A) %>% summarise(Money = sum(Money), Hats = sum(Hats))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 3
  A     Money  Hats
  <chr> <dbl> <dbl>
1 Fred     30     4
2 John     20     2
3 Paul     30     3