Contents

In this tutorial, we will discuss about `apply()`

function in R with some examples. The `apply()`

function is available in `base`

R package.

## apply() function in R

The `apply()`

function is the most popular function in R. The `apply()`

function takes a matrix or array, an index and a function (built-in or user-defined) as inputs.

The general syntax of `apply()`

function is

`apply(X,MARGIN,FUN,...)`

where

**X:**an array or matrix**MARGIN:**a vector giving the subscripts which the function will be applied over. For matrix`1`

indicate rows,`2`

indicate columns,`c(1,2)`

indicates both.**FUN:**the function to be applied.**…:**optional argument to`FUN`

.

The function `apply(X, MARGIN, FUN)`

apply a function (`FUN`

) to margins (`MARGIN`

) of an array or matrix (`X`

) and return a vector or array or list of values by applying a function to margins of an array or matrix.

The apply function extract each row or column of a matrix as a vector, one at a time and passes it to the `FUN`

.

## apply() function in R on matrix

### Example 1: `apply()`

function on rows of a Matrix

Suppose we have a matrix A as

` $$ A= \begin{bmatrix} 12 & 14\\ 17 & 18 \\ 13 & 20 \end{bmatrix} $$ `

Create above matrix in R using `matrix()`

function as:

```
A <- matrix(c(12, 14, 17, 18, 13, 20), nrow = 3)
A
```

```
[,1] [,2]
[1,] 12 18
[2,] 14 13
[3,] 17 20
```

Suppose you wish to compute the row sums of matrix $A$. For this use `apply()`

function on matrix `A`

by setting `MARGIN=1`

and `FUN = sum`

.

```
# compute row sums
apply(A, 1, sum)
```

`[1] 30 27 37`

Suppose you wish to compute the mean of each row of matrix $A$. For this use `apply()`

function on matrix `A`

by setting `MARGIN=1`

and `FUN = s=mean`

.

```
# compute row means
apply(A, 1, mean)
```

`[1] 15.0 13.5 18.5`

Note that for the calculation of row sums and row means of matrix or array, the more efficient way is to use `rowSums()`

and `rowMeans()`

function respectively.

### Example 2: apply() function on columns of a Matrix

Suppose you wish to compute the column means of matrix $A$. For this use `apply()`

function on matrix `A`

by setting `MARGIN=2`

and `FUN = mean`

.

```
# Compute column means
apply(A, 2, mean)
```

`[1] 14.33333 17.00000`

Note that for the calculation of column sums and column means of matrix or array, the more efficient way is to use `columnSums()`

and `columnMeans()`

function respectively.

Suppose you wish to compute the standard deviation for each column of matrix $A$. For this use `apply()`

function on matrix `A`

by setting `MARGIN=2`

and `FUN = sd`

.

```
# Compute column standard deviation
apply(A, 2, sd)
```

`[1] 2.516611 3.605551`

### Example 3: apply() function with optional argument

The `apply()`

function allows us to pass an additional argument to the function.

Suppose you wish to compute the column means of matrix $A$. For this use `apply()`

function on matrix `A`

by setting `MARGIN=2`

and `FUN = mean`

.

```
B <- matrix(c(10, 20, 30, NA), nrow = 2)
B
```

```
[,1] [,2]
[1,] 10 30
[2,] 20 NA
```

```
# Compute row means
apply(B, 1, mean, na.rm = TRUE)
```

`[1] 20 20`

```
# Compute column means
apply(B, 2, mean, na.rm = TRUE)
```

`[1] 15 30`

Note that we can use optional argument `...`

to the function in `apply()`

function, like `na.rm=TRUE`

for the `mean()`

function.

### Example 4: apply() function on Matrix with user-defined function

Suppose we want to calculate standard error of each column of given matrix. First define a user-defined function for standard error as follows:

```
std.error <- function(x) {
sd(x) / sqrt(length(x))
}
```

To calculate standard error for each column of a matrix `A`

, we use `apply()`

function with `FUN`

as a user-defined function `std.error`

as follows:

```
# compute the standard error for columns of A
apply(A, 2, std.error)
```

`[1] 1.452966 2.081666`

## apply() function in R on array

### Example 5: apply() function on array

To understand the use of `apply()`

function on array, let us create an array of dimension $2\times 3\times 2$ and store it in `myarray`

.

```
myarray <- array(1:12, dim = c(2, 3, 2))
myarray
```

```
, , 1
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
, , 2
[,1] [,2] [,3]
[1,] 7 9 11
[2,] 8 10 12
```

```
# Compute sum of the rows
apply(myarray, 1, sum)
```

`[1] 36 42`

The result is the sum of all the elements of 1st row of Array `myarray`

`1+3+5+7+9+11 = 36`

`2+4+6+8+10+12 =42`

.

```
# Compute sum of the columns
apply(myarray, 2, sum)
```

`[1] 18 26 34`

The result is the sum of all the elements of 1st row of Array `myarray`

.

`1+2+7+8 = 18`

`3+4+9+10 = 26`

`5+6+11+12 =34`

```
# Compute sum of the rows as well as columns
apply(myarray, c(1, 2), sum)
```

```
[,1] [,2] [,3]
[1,] 8 12 16
[2,] 10 14 18
```

The result is the sum of all corresponding elements of array (for rows and columns). (i.e. `1+7=8`

, `3+9=12`

, etc.)

Similarly, one can use other built-in functions or user-defined functions in `apply()`

function.

## apply() function in R on data frame

### Example 6: apply() function on data frame

`apply()`

function can also be used on data frame. To understand the use of apply function on data frame, let us create a small data frame `df`

as follows:

```
x<-1:6
y<-c("S","F","F","S","F","S")
z<-c(10,12,13,15,17,18)
# create a data frame
df <- data.frame(x=x,y=y,z=z)
df
```

```
x y z
1 1 S 10
2 2 F 12
3 3 F 13
4 4 S 15
5 5 F 17
6 6 S 18
```

```
# compute means of 1st and 3rd column of df
apply(df[,c(1,3)],2,mean)
```

```
x z
3.50000 14.16667
```

Basically `apply()`

function is for matrix and array, but you can use it on columns of data frame also. Don’t use `apply()`

function on rows of data frames, because their types and units of measurements may be different.

## Endnote

In this tutorial you learned about `apply()`

function in R and how to use `apply()`

function on matrix and array with illustration.

Learn more about functions in R, refer to the following tutorials:

Hopefully you enjoyed learning this tutorial on `apply()`

function in R. Hope the content is more than sufficient to understand `apply()`

function in R.