R has many *apply functions which are ably described in the help files (e.g. `?apply`

). There are enough of them, though, that beginning users may have difficulty deciding which one is appropriate for their situation or even remembering them all. They may have a general sense that “I should be using an *apply function here”, but it can be tough to keep them all straight at first.

Despite the fact (noted in other answers) that much of the functionality of the *apply family is covered by the extremely popular `plyr`

package, the base functions remain useful and worth knowing.

This answer is intended to act as a sort of **signpost** for new users to help direct them to the correct *apply function for their particular problem. Note, this is **not** intended to simply regurgitate or replace the R documentation! The hope is that this answer helps you to decide which *apply function suits your situation and then it is up to you to research it further. With one exception, performance differences will not be addressed.

**apply** – *When you want to apply a function to the rows or columns of a matrix (and higher-dimensional analogues); not generally advisable for data frames as it will coerce to a matrix first.*

```
# Two dimensional matrix
M <- matrix(seq(1,16), 4, 4)
# apply min to rows
apply(M, 1, min)
[1] 1 2 3 4
# apply max to columns
apply(M, 2, max)
[1] 4 8 12 16
# 3 dimensional array
M <- array( seq(32), dim = c(4,4,2))
# Apply sum across each M[*, , ] - i.e Sum across 2nd and 3rd dimension
apply(M, 1, sum)
# Result is one-dimensional
[1] 120 128 136 144
# Apply sum across each M[*, *, ] - i.e Sum across 3rd dimension
apply(M, c(1,2), sum)
# Result is two-dimensional
[,1] [,2] [,3] [,4]
[1,] 18 26 34 42
[2,] 20 28 36 44
[3,] 22 30 38 46
[4,] 24 32 40 48
```

If you want row/column means or sums for a 2D matrix, be sure to investigate the highly optimized, lightning-quick `colMeans`

, `rowMeans`

, `colSums`

, `rowSums`

.

**lapply** – *When you want to apply a function to each element of a list in turn and get a list back.*

This is the workhorse of many of the other *apply functions. Peel back their code and you will often find `lapply`

underneath.

```
x <- list(a = 1, b = 1:3, c = 10:100)
lapply(x, FUN = length)
$a
[1] 1
$b
[1] 3
$c
[1] 91
lapply(x, FUN = sum)
$a
[1] 1
$b
[1] 6
$c
[1] 5005
```

**sapply** – *When you want to apply a function to each element of a list in turn, but you want a vector back, rather than a list.*

If you find yourself typing `unlist(lapply(...))`

, stop and consider `sapply`

.

```
x <- list(a = 1, b = 1:3, c = 10:100)
#Compare with above; a named vector, not a list
sapply(x, FUN = length)
a b c
1 3 91
sapply(x, FUN = sum)
a b c
1 6 5005
```

In more advanced uses of `sapply`

it will attempt to coerce the result to a multi-dimensional array, if appropriate. For example, if our function returns vectors of the same length, `sapply`

will use them as columns of a matrix:

` sapply(1:5,function(x) rnorm(3,x))`

If our function returns a 2 dimensional matrix, `sapply`

will do essentially the same thing, treating each returned matrix as a single long vector:

` sapply(1:5,function(x) matrix(x,2,2))`

Unless we specify `simplify = "array"`

, in which case it will use the individual matrices to build a multi-dimensional array:

` sapply(1:5,function(x) matrix(x,2,2), simplify = "array")`

Each of these behaviors is of course contingent on our function returning vectors or matrices of the same length or dimension.

**vapply** – *When you want to use sapply but perhaps need to squeeze some more speed out of your code.*

For `vapply`

, you basically give R an example of what sort of thing your function will return, which can save some time coercing returned values to fit in a single atomic vector.

`x <- list(a = 1, b = 1:3, c = 10:100) #Note that since the advantage here is mainly speed, this # example is only for illustration. We're telling R that # everything returned by length() should be an integer of # length 1. vapply(x, FUN = length, FUN.VALUE = 0L) a b c 1 3 91`

mapply-For when you have several data structures (e.g. vectors, lists) and you want to apply a function to the 1st elements of each, and then the 2nd elements of each, etc., coercing the result to a vector/array as in`sapply`

.

This is multivariate in the sense that your function must accept multiple arguments.

```
#Sums the 1st elements, the 2nd elements, etc.
mapply(sum, 1:5, 1:5, 1:5)
[1] 3 6 9 12 15
#To do rep(1,4), rep(2,3), etc.
mapply(rep, 1:4, 4:1)
[[1]]
[1] 1 1 1 1
[[2]]
[1] 2 2 2
[[3]]
[1] 3 3
[[4]]
[1] 4
```

Map-A wrapper to`mapply`

with`SIMPLIFY = FALSE`

, so it is guaranteed to return a list.

`Map(sum, 1:5, 1:5, 1:5) [[1]] [1] 3 [[2]] [1] 6 [[3]] [1] 9 [[4]] [1] 12 [[5]] [1] 15`

rapply-For when you want to apply a function to each element of anested liststructure, recursively.

To give you some idea of how uncommon `rapply`

is, I forgot about it when first posting this answer! Obviously, I’m sure many people use it, but YMMV. `rapply`

is best illustrated with a user-defined function to apply:

```
#Append ! to string, otherwise increment
myFun <- function(x){
if (is.character(x)){
return(paste(x,"!",sep=""))
}
else{
return(x + 1)
}
}
#A nested list structure
l <- list(a = list(a1 = "Boo", b1 = 2, c1 = "Eeek"),
b = 3, c = "Yikes",
d = list(a2 = 1, b2 = list(a3 = "Hey", b3 = 5)))
#Result is named vector, coerced to character
rapply(l,myFun)
#Result is a nested list like l, with values altered
rapply(l, myFun, how = "replace")
```

**tapply** – *For when you want to apply a function to subsets of a vector and the subsets are defined by some other vector, usually a factor.*

The black sheep of the *apply family, of sorts. The help file’s use of the phrase “ragged array” can be a bit confusing, but it is actually quite simple.

A vector:

` x <- 1:20`

A factor (of the same length!) defining groups:

` y <- factor(rep(letters[1:5], each = 4))`

Add up the values in `x`

within each subgroup defined by `y`

:

```
tapply(x, y, sum)
a b c d e
10 26 42 58 74
```

More complex examples can be handled where the subgroups are defined by the unique combinations of a list of several factors. `tapply`

is similar in spirit to the split-apply-combine functions that are common in R (`aggregate`

, `by`

, `ave`

, `ddply`

, etc.) Hence its black sheep status.