What is the difference of tapply and aggregate in R? [closed]

Question

Aaa <- data.frame(amount=c(1,2,1,2,1,1,2,2,1,1,1,2,2,2,1), 
                  card=c("a","b","c","a","c","b","a","c","b","a","b","c","a","c","a"))

aggregate(x=Aaa$amount, by=list(Aaa$card), FUN=mean)

##   Group.1    x
## 1       a 1.50
## 2       b 1.25
## 3       c 1.60

tapply(Aaa$amount, Aaa$card, mean)

##    a    b    c 
## 1.50 1.25 1.60 

Above is an example code.

It seems that aggregate and tapply both are very handy and perform similar functionality.

Can someone explain or give examples on their differences?

Answer 1

aggregate is designed to work on multiple columns with one function and returns a dataframe with one row for each category, while tapply is designed to work on a single vector with results returned as a matrix or array. Only using a two-column matrix does not really allow the capacities of either function (or their salient differences) to be demonstrated. aggregate also has a formula method, which tapply does not.

> Aaa <- data.frame(amount=c(1,2,1,2,1,1,2,2,1,1,1,2,2,2,1), cat=sample(letters[21:24], 15,rep=TRUE),
+                   card=c("a","b","c","a","c","b","a","c","b","a","b","c","a","c","a"))
> with( Aaa, tapply(amount, INDEX=list(cat,card), mean) )
    a   b   c
u 1.5 1.5  NA
v 2.0 1.0 2.0
w 1.0  NA 1.5
x 1.5  NA 1.5

>  aggregate(amount~cat+card, data=Aaa, FUN= mean) 
  cat card amount
1   u    a    1.5
2   v    a    2.0
3   w    a    1.0
4   x    a    1.5
5   u    b    1.5
6   v    b    1.0
7   v    c    2.0
8   w    c    1.5
9   x    c    1.5

The xtabs function also delivers an R "table" and it has a formula interface. R tables are matrices that typically have integer values because they are designed to be "contingency tables" holding counts of items in cross-classifications of the marginal categories.