Skip to main content

Exploring Control Structures in R Programming: Learn How to Use While Loops and Statements Like Repeat, Break, Continue, Next and Return to Enhance Your Code!

The Beginner’s Guide to Control Structures (While Loops, Repeat, Break, Continue, Next and Return) in R Programming:

Control structures are an essential aspect of programming in any language, including R. In R, control structures help programmers to define the flow of a program's logic. In addition to if-else statements, switch cases, and for loops, R also supports while loops and statements such as repeat, break, continue, next, and return. This blog post will explain how to use these control structures in R and provide practice material for learners.


While Loop:

The while loop is used to execute a block of code repeatedly as long as the specified condition remains true. The syntax of the while loop in R is as follows:

while (condition) {
  # Execute code as long as the condition is true
}

For example, consider the following code that prints the numbers from 1 to 5 using a while loop:

i <- 1

while (i <= 5) {
  print(i)
  i <- i + 1
}

Output:
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5

Repeat, Break, Continue, Next and Return statements:

The repeat statement is used to execute a block of code repeatedly until a specified condition is met. The syntax of the repeat statement in R is as follows:

repeat {
  # Execute code repeatedly
  if (condition) {
    break # Exit the loop if the condition is true
  }
}

The break statement is used to exit a loop prematurely if a specified condition is met. The syntax of the break statement in R is as follows:

for (i in 1:5) {
  if (i == 3) {
    break # Exit the loop if i is equal to 3
  }
  print(i)
}

The continue statement is used to skip the current iteration of a loop and move on to the next iteration. The syntax of the continue statement in R is as follows:

for (i in 1:5) {
  if (i == 3) {
    continue # Skip the iteration if i is equal to 3
  }
  print(i)
}

The return statement is used to exit a function and return a value. The syntax of the return statement in R is as follows:

my_function <- function(x, y) {
  if (x == y) {
    return("x is equal to y")
  }
  return("x is not equal to y")
}

The next statement in R is used to skip the current iteration of a loop and move on to the next iteration. This statement is typically used in for loops, while loops, and repeat loops to skip over certain iterations based on a specified condition. For example, consider the following code that uses a for loop to print all even numbers from 1 to 10:

for (i in 1:10) {
  if (i %% 2 != 0) {
    next # Skip the iteration if i is odd
  }
  print(i)
}

Output:
[1] 2
[1] 4
[1] 6
[1] 8
[1] 10

In this code, the modulo operator (%%) is used to determine if a number is even or odd. If the number is odd, the next statement is used to skip the current iteration of the loop and move on to the next iteration. As a result, only even numbers are printed to the console.

Practice Material:

Here are a few practice exercises to help you get started:

  • Write a program that calculates the sum of the first 10 natural numbers using a while loop.
  • Write a program that takes a list of numbers as input and returns the sum of all even numbers in the list. Use a repeat loop to prompt the user to enter a number until they enter -1 to break the loop. Use a continue statement to skip odd numbers in the loop.
  • For more practice you should start swirl's lessons in  R Programming. Complete download process of swirl and R Programming is here, click on the link!
  • You can look in to the practice and reading material that is provided in the text book, click here to download the textbook.
  • Lecture slides can be downloaded from here. It would be great if you go through them too.

These practice materials are suitable for you to get hands-on experience with while loops and repeat, break, continue, next, and return statements in R programming. Good luck with your learning!
Labels:

Comments

Post a Comment

Type your comment here.
However, Comments for this blog are held for moderation before they are published to blog.
Thanks!

Popular posts from this blog

What is Data? And What is Data Science Process?

The Beginner’s Guide to Data & Data Science Process About Data: In our First Video today we talked about Data and how the Cambridge English Dictionary and Wikipedia defines Data, then we looked on few forms of Data that are: Sequencing data   Population census data ( Here  is the US census website and  some tools to help you examine it , but if you aren’t from the US, I urge you to check out your home country’s census bureau (if available) and look at some of the data there!) Electronic medical records (EMR), other large databases Geographic information system (GIS) data (mapping) Image analysis and image extrapolation (A fun example you can play with is the  DeepDream software  that was originally designed to detect faces in an image, but has since moved on to more  artistic  pursuits.) Language and translations Website traffic Personal/Ad data (e.g.: Facebook, Netflix predictions, etc.) These data forms need a lot of preprocessin...
1 comment
Read more

Efficient Data Manipulation with Loop Functions in R: A Deep Dive into apply and mapply

The Beginner’s Guide to Loop Functions in R: In addition to lapply and sapply , R also has apply and mapply , which are other loop functions that are commonly used for data manipulation and analysis. In this blog post, we'll explain what these functions are, how they work, and provide some practice material for beginners to intermediate level. apply:  Apply a Function to a Matrix or Array apply is a loop function in R that applies a function to either rows or columns of a matrix or array. Here's the basic syntax: apply(matrix/array, margin, function) The matrix/array argument is the matrix or array you want to apply the function to, and the margin argument specifies whether you want to apply the function to rows or columns. margin = 1 applies the function to rows, while margin = 2 applies the function to columns. The function argument is the function you want to apply. For example, let's say we have a matrix of numbers and we want to apply the sum function to each row:...
Post a Comment
Read more

Optimization Example of Lexical Scoping in R: Exploring optim, optimize, and nlm Functions

The Beginner’s Guide to Optimization Example of Lexical Scoping in R: When it comes to optimization in R, lexical scoping can be a useful tool for optimizing complex functions that involve multiple variables. In this blog post, we will explore how lexical scoping can be used to optimize a function using the NLL (negative log-likelihood) function , and how the optim, optimize, and nlm functions can be used to perform optimization in R. Optimizing the NLL Function using Lexical Scoping The NLL function is a common function used in optimization problems. It is defined as the negative log of the likelihood function, which is used to estimate the parameters of a statistical model. In R, the NLL function can be defined using lexical scoping, which allows us to pass arguments to the function and access variables from within the function. Here is an example of how to define the NLL function using lexical scoping in R: nll <- function(data, parameters) {   # Define local variables ...
Post a Comment
Read more