Loop Functions

lapply

Loop over a list and apply a function.

Let's calculate mean of each element in the list using mean function.

list <- list(a = 1:10, b = 10:100)

lapply(list, mean)

Using runif function.

lapply(1:10, runif, min = 0, max = 10)

sapply

sapply is almost the same as lapply but it tries to simplify the result if possible.

• If the result is a list, it returns vector

• If the result is list where each element is vector, matrix is returned

• If it does not know, list is returned

Let's see what is the difference between lapply and sapply.

> lapply(list, mean)
$a
[1] 5.5

$b
[1] 55

> sapply(list,mean)
   a    b
 5.5 55.0

apply

Evaluate

> str(apply)
function (X, MARGIN, FUN, ...)

• X - is array

• MARGIN - margin to retain

• FUN - function

• ... - argumetns for FUN function

x <- matrix(rnorm(200), 20, 10)

apply(x, 1, mean)
apply(x, 2, mean)

We can use rowSums, rowMeans, colSums and colMeans functions instead, they are faster.

mapply

With mapply function we can iterate trough multiple sets. E.g.

x <- list(rep(1, 4), rep(2, 3))
mapply(mean, x)

tapply

Applies function across subset of a vector. It splits data into little pieces and after calculation it is put together again.

First we create a sample data.

> x <- c(rnorm(10), runif(10), rnorm(10, 1))
> f <- gl(3, 10)
> x
 [1]  0.6546703  0.1908048  0.7510316 -0.5275388  0.1565112
 [6] -1.0819140  0.5881377  1.9318782 -1.6296265  0.1217391
[11]  0.8565242  0.9489818  0.6900339  0.9405762  0.5868754
[16]  0.7953035  0.1449435  0.3803465  0.6014722  0.7375775
[21]  0.9127986 -0.1662831  0.4377588  0.9954957  1.0922615
[26]  1.2733158  1.4715993  3.6705109  1.1215348  1.1913898
> f
 [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3
Levels: 1 2 3

Mean of each number in x.

> tapply(x, f, mean)
        1         2         3
0.1155694 0.6682635 1.2000382

Turn of simplify and function returns list then.

> tapply(x, f, mean, simplify=FALSE)
$`1`
[1] 0.1155694

$`2`
[1] 0.6682635

$`3`
[1] 1.200038