Loops (like for, while and repeat) are a way to repeatedly execute some code. However, they are often slow in execution when it comes to processing large data sets.

R has a more efficient and quick approach to perform iterations – The apply family.

Apply family in R

The apply family consists of vectorized functions. Below are the most common forms of apply functions.

• apply()
• lapply()
• sapply()
• tapply()

These functions let you take data in batches and process the whole batch at once.

There primary difference is in the object (such as list, matrix, data frame etc.) on which the function is applied to and the object that will be returned from the function.

apply() function

The apply() function is used to apply a function to the rows or columns of matrices or data frames.

It assembles the returned values into a vector, and then returns that vector.

Syntax

The syntax for apply() is as follows:

apply(x,MARGIN,FUN,…)

Parameters

Examples

# Get the sum of each column
data <- matrix(1:9, nrow=3, ncol=3)
data
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

apply(data, 2, sum)
[1]  6 15 24

# Get the sum of each row
data <- matrix(1:9, nrow=3, ncol=3)
data
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

apply(data, 1, sum)
[1] 12 15 18

You can use user-defined functions as well.

# Apply a custom function that squares each element in a matrix
data <- matrix(1:9, nrow=3, ncol=3)
data
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

apply(data, 2, function(x){x^2})
     [,1] [,2] [,3]
[1,]    1   16   49
[2,]    4   25   64
[3,]    9   36   81

lapply() function

The lapply() function is used to apply a function to each element of the list. It collects the returned values into a list, and then returns that list.

Syntax

The syntax for lapply() is as follows:

lapply(x,FUN,…)

Parameters

Example

# Get the sum of each list item
data <- list(item1 = 1:5,
             item2 = seq(4,36,8),
             item4 = c(1,3,5,7,9))
data
$item1
[1] 1 2 3 4 5

$item2
[1]  4 12 20 28 36

$item4
[1] 1 3 5 7 9

lapply(data, sum)
$item1
[1] 15

$item2
[1] 100

$item4
[1] 25

sapply() function

The sapply() and lapply() work basically the same.

The only difference is that lapply() always returns a list, whereas sapply() tries to simplify the result into a vector or matrix.

• If the return value is a list where every element is length 1, you get a vector.
• If the return value is a list where every element is a vector of the same length (> 1), you get a matrix.
• If the lengths vary, simplification is impossible and you get a list.

Syntax

The syntax for sapply() is as follows:

sapply(x,FUN,…)

Parameters

Example

# Get the sum of each list item and simplify the result into a vector
data <- list(item1 = 1:5,
             item2 = seq(4,36,8),
             item4 = c(1,3,5,7,9))
data
$item1
[1] 1 2 3 4 5

$item2
[1]  4 12 20 28 36

$item4
[1] 1 3 5 7 9

sapply(data, sum)
item1 item2 item4 
   15   100    25

tapply() function

The tapply() function breaks the data set up into groups and applies a function to each group.

Syntax

The syntax for tapply() is as follows:

tapply(x,INDEX,FUN,…,simplify)

Parameters

Example

# Find the age of youngest male and female
data <- data.frame(name=c("Amy","Max","Ray","Kim","Sam","Eve","Bob"),
                   age=c(24, 22, 21, 23, 20, 24, 21),
                   gender=factor(c("F","M","M","F","M","F","M")))
data
  name age gender
1  Amy  24      F
2  Max  22      M
3  Ray  21      M
4  Kim  23      F
5  Sam  20      M
6  Eve  24      F
7  Bob  21      M

tapply(data$age, data$gender, min)
 F  M 
23 20