How do I sum the values of columns in several tables if tables have different lengths?

Question

Alright, this should be an easy one but I'm looking for a solution that's as fast as possible.

Let's say I have 3 tables (the number of tables will be much larger):

tab1 <- table(c(1, 1, 1, 2, 2, 3, 3, 3))
tab2 <- table(c(1, 1, 4, 4, 4))
tab3 <- table(c(1, 1, 2, 3, 5))

This is what we get:

> tab1
1 2 3 
3 2 3 
> tab2
1 4 
2 3 
> tab3
1 2 3 5 
2 1 1 1 

What I want to have in a fast way so that it works with many big tables is this:

1 2 3 4 5
7 3 4 3 1

So, basically the tables get aggregated over all names. Is there an elementary function that does this which I am missing? Thanks for your help!

Answer 1

We concatenate (c) the tab output to create 'v1', use tapply to get the sum of the elements grouped by the names of that object.

v1 <- c(tab1, tab2, tab3)
tapply(v1, names(v1), FUN=sum)
#1 2 3 4 5 
#7 3 4 3 1 

Answer 2

You could use rowsum(). The output will be slightly different than what you show, but you can always restructure it after the calculations. rowsum() is known to be very efficient.

x <- c(tab1, tab2, tab3)
rowsum(x, names(x))
#   [,1]
# 1    7
# 2    3
# 3    4
# 4    3
# 5    1

Here's a benchmark with akrun's data.table suggestion added in as well.

library(microbenchmark)
library(data.table)

xx <- rep(x, 1e5)

microbenchmark(
    tapply = tapply(xx, names(xx), FUN=sum),
    rowsum = rowsum(xx, names(xx)),
    data.table = data.table(xx, names(xx))[, sum(xx), by = V2]
)
# Unit: milliseconds
#        expr       min        lq      mean    median        uq       max neval
#      tapply 150.47532 154.80200 176.22410 159.02577 204.22043 233.34346   100
#      rowsum  41.28635  41.65162  51.85777  43.33885  45.43370 109.91777   100
#  data.table  21.39438  24.73580  35.53500  27.56778  31.93182  92.74386   100

Answer 3

you can try this

df <- rbind(as.matrix(tab1), as.matrix(tab2), as.matrix(tab3))
aggregate(df, by=list(row.names(df)), FUN=sum)
  Group.1 V1
1       1  7
2       2  3
3       3  4
4       4  3
5       5  1