Beginners Guide to R – R For Loop

Hits: 8

R For Loop

The for statement in R is a bit different from what you usually use in other programming languages.

Rather than iterating over a numeric progression, R’s for statement iterates over the items of a vector or a list. The items are iterated in the order that they appear in the vector.

Syntax

Here’s the syntax of the for statement:

r for loop syntax

Basic Examples

# Iterate through a vector
colors <- c("red","green","blue","yellow")
for (x in colors) {
  print(x)
}
[1] "red"
[1] "green"
[1] "blue"
[1] "yellow"
# Iterate through a list
l <- list(3.14, "Hi", c(1,2,3))
for (x in l) {
  print(x)
}
[1] 3.14
[1] "Hi"
[1] 1 2 3

If you need to execute a group of statements for a specified number of times, use sequence operator : or built-in function seq()

# Print 'Hello!' 3 times
for (x in 1:3) {
  print("Hello!")
}
[1] "Hello!"
[1] "Hello!"
[1] "Hello!"
# Iterate a sequence and square each element
for (x in seq(from=2,to=8,by=2)) {
  print(x^2)
}
[1] 4
[1] 16
[1] 36
[1] 64

for Loop Without Curly Braces

If you have only one statement to execute, you can skip curly braces.

# Print the numbers 0 to 4
for (x in 0:4) print(x)
[1] 0
[1] 1
[1] 2
[1] 3
[1] 4

Nested for loop

A nested for loop is a loop within a loop. They are useful for when you want to repeat something several times for several things.

for(x in 1:3) {
  for(y in 1:2) {
    print(paste(x, y))
  }
}
[1] "1 1"
[1] "1 2"
[1] "2 1"
[1] "2 2"
[1] "3 1"
[1] "3 2"

Break in for Loop

In R, break statement is used to exit the loop immediately. It simply jumps out of the loop altogether, and the program continues after the loop.

# Break the loop at 'blue'
colors <- c("red","green","blue","yellow")
for (x in colors) {
  if (x == "blue")
    break
  print(x)
}
[1] "red"
[1] "green"

Next (continue) in for Loop

The next statement skips the current iteration of a loop and continues with the next iteration.

# Skip 'blue' using continue statement
colors <- c("red","green","blue","yellow")
for (x in colors) {
  if (x == "blue")
    next
  print(x)
}
[1] "red"
[1] "green"
[1] "yellow"

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.