R Programming FAQs

In different programming languages and R, the for loop (for statement) allows one to specify the set of codes (commands) that should be repeated a fixed number of times. The for loop in R is not limited to integers or even numbers in the input. Character vectors, logical vectors, lists, or even expressions can also be used in a for loop in R.

“For loops” are fundamental programming constructs that allow you to execute a block of code. In R, for loops are particularly useful for iterative operations on vectors, lists, or data frames.

General Syntax of For Loop in R

The general syntax of the for loop in R Language is

for (named vector) {
   statements (R codes)
}

The curly braces contain a set of commands so that these commands can be treated as a single command and can be repeated the desired number of times. However, if there is only a single statement, then there is no need to use curly braces.

Let us understand the loop through different examples. Note that some of the examples can be done without the use of a for loop or with alternatives such as apply(), lapply(), sapply(), and tapply() functions.

For Loop Examples in R

Example: Suppose you want to compute the squared values for 1 to 10. Let us do this using a for loop in R, as shown below:

for (i in 1:10){
    squared <- i^2
    print(squared)
}

Note: If you write the print(squared) command outside the for loop (after the curly braces), then the last result of the loop iteration will be displayed in the console only, that is, the square of the last number (n = 10) will be printed.

To store the result of each iteration in a variable (vector, matrix, data frame, or list), a container (variable) needs to be specified with the sample length as that of the loop. For example, the outcome of each iteration (from the above example) can be stored in a variable as,

result <- vector("numeric", 10)

for (i in length(result) ){
    squared <- i^2
    result[i] <- squared
}

result

Now, results can be displayed without print() command as the results are stored in a container (vector variable). To store results in a data frame or matrix (in the form of a table) with an iteration number, the above example can be extended as

result <- data.frame(matrix(nrow = 10, ncol = 2))
colnames(result) <- c("i", "Square")

for (i in 1:10 ){
    squared <- i ^ 2
    result[i, 1] <- i  # stores iteration number in 1stcolumn of data frame
    result[i, 2] <- squared # stores iteration result in 2nd column of data frame
}

result

Nesting For Loop in R

Placing the loop inside the body of another loop is called nesting. For nested loops, the outer loop takes control of the iteration of the inner loop. The inner loop will be executed (iterated) n-times for every iteration of the outer loop. For example

# Nested For loop in R Language
for (i in 1:10){
    for (j in 1:5){
        print(i*j)
    }
}

There will be a total of 50 iterations. For each iteration of the first loop (outer loop), there will be five iterations in the inner loop.

The break statement can be used inside a loop if one wants to stop the iteration when a certain condition (situation) occurs, and the control will be out of the loop when the condition is satisfied. For example,

# break statement in for loops
n <- 1:5
for (i in x){
   if( i == 3){
     break
   }
print(i)
}

It is also possible to jump to the next iteration using the next statement when certain conditions are satisfied. For example,

# next statement in for loops
n <- 1:5

for( i in x ){
    if (i == 2){
       next
    }
print(i)
}

Now consider the example of a for loop using a character vector

# for loop in R using a character vector
v = LETTERS[1:10]
for(i in v){
    print(i)
}

Using For Loops in Simulations

In simulations, loops can be used to generate or resample (bootstrap) data. For example, let us create a variable having 1000 observations (n = 1000), where each observation is a function of the previous observation according to the equation $y_t$ is 80%, $y_{t-1} + 20%$. with random noise having a mean of 0 and a standard deviation of 1. The value of$y_1=1$.

# Simulating response variable in R using for loop
y <- rep(1,1000)

for(i in 2:1000){
    y[i] = 0.8 * y[i-1] + 0.2 * rnorm(1)
}

y

Consider another example of generating simulated data in R. Suppose you want to simulate the mixture data and want to repeat it many times, and you also want to store the data for each time.

# generating simulated data in R

n = 100
res = list()
N = 1000
X = matrix(0, nrow = N, ncol = 2)

for(i in 1:n){
    U = runif(N, min = 0, max = 1)
    for(j in 1:N){
        if (U[j] & 0.8){
          X[j,] <- rnorm(1, 2.5, 3)
        } else{
             X[j,] <- rnorm(1,2,1)
          }
        }
  
    res[[i]] = X
}

res[[100]]

Note that each res[[i]] is a separate data set, which can be used for further calculations.

When to Avoid For Loops in R Language

In R Language, vectorized operations are often preferable:

• Use apply() family functions (such as lapply(), sapply(), mapply() etc.)
• Consider purrr::map() functions from the tidyverse
• For data frames, dplyr operations are often more efficient

However, for loops remain valuable for:

• Complex iterative algorithms
• Cases where operations depend on previous iterations
• When readability is more important than maximum performance

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