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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 4Ã—4 matrix:

#create matrix empty_mat 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 #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.Â

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.

**Additional Resources**

How to Loop Through Column Names in R

How to Append Rows to a Data Frame in R