Table of Contents
A nested for loop is a type of looping structure in R that allows a user to loop through multiple sets of data at the same time. It is created by placing one for loop inside another for loop. A nested for loop works by first executing the outer loop, then executing the inner loop for each iteration of the outer loop. Examples of nested for loops in R can include looping over multiple columns of a data frame, iterating over a list of lists, looping over the rows of a matrix, and more.
A nested for loop allows you to loop through elements in multiple vectors (or multiple dimensions of a matrix) and perform some operations.
The basic structure of a for loop in R is:
for(i in 1:4) { print (i) } [1] 1 [1] 2 [1] 3 [1] 4
And the basic structure of a nested for loop is:
for(i in 1:4) { for(j in 1:2) { print (i*j) } } [1] 1 [1] 2 [1] 2 [1] 4 [1] 3 [1] 6 [1] 4 [1] 8
This tutorial shows a few examples of how to create nested for loops in R.
Example 1: Nested For Loop in R
The following code shows how to use a nested for loop to fill in the values of a 4×4 matrix:
#create matrix
empty_mat <- matrix(nrow=4, ncol=4)
#view empty matrix
empty_mat
[,1] [,2] [,3] [,4]
[1,] NA NA NA NA
[2,] NA NA NA NA
[3,] NA NA NA NA
[4,] NA NA NA NA
#use nested for loop to fill in values of matrix
for(i in 1:4) {
for(j in 1:4) {
empty_mat[i, j] = (i*j)
}
}
#view matrix
empty_mat
[,1] [,2] [,3] [,4]
[1,] 1 2 3 4
[2,] 2 4 6 8
[3,] 3 6 9 12
[4,] 4 8 12 16
Example 2: Nested For Loop in R
The following code shows how to use a nested for loop to square each value in a data frame:
#create empty data frame
df <- data.frame(var1=c(1, 7, 4),
var2=c(9, 13, 15))
#view empty data frame
df
var1 var2
1 1 9
2 7 13
3 4 15
#use nested for loop to square each value in the data frame
for(i in 1:nrow(df)) {
for(j in 1:ncol(df)) {
df[i, j] = df[i,j]^2
}
}
#view new data frame
df
var1 var2
1 1 81
2 49 169
3 16 225
A Note on Looping
In general, nested for loops perform fine on small datasets or matrices but they tend to be fairly slow with larger data.
For big data, the family of apply functions tend to be much quicker and the data.table package has many built-in functions that perform efficiently on larger datasets.
How to Loop Through Column Names in R
How to Append Rows to a Data Frame in R