Creating a Basic For Loop

iteration

Learn how to write a for loop when you’re dealing with numeric values.

Author

Affiliation

Daphne Virlar-Knight

National Center for Ecological Analysis and Synthesis

Published

September 7, 2022

One of the key tenets of programming is the DRY principle: Don’t Repeat Yourself. Essentially, this boils down to not repeating your code ad nauseum to change only one or two things. This is particularly useful for computationally intensive workflows that would require changing tens or hundreds of items. That’s where the for loop comes into play.

First, let’s look at the basic syntax of a for loop. When you write one, you’re telling the computer “run this piece of code (statement) some number of times (range_of_values) in this spot (value).”

Syntax

for(value in range_of_values){
  statement 
  print(statement) 
}

And that’s it! Congratulations, now you know the syntax for a basic for loop! Now let’s see it in practice. Now let’s put it into practice.

Example

Let’s say Europe is experiencing a heat wave, but I’m not quite grasping the context of how bad it is because I’m not familiar with the Celsius temperature scale. I know the average temperatures are somewhere between 35-40°C, so I’ll write a quick function to convert the temperatures into Fahrenheit! But that spans over five numbers, and writing that function out five individual times goes against the DRY principle. This gives us the perfect excuse to write a for loop.

The equation for converting temperatures from Celsius to Fahrenheit is the following. \[(deg C*1.8) + 32\] Knowing that conversion formula, here’s how I’d write my for loop:

for(degC in 35:40){
  # Write your statement. Make sure the output of the statement is assigned to an object,
  # otherwise R will only remember the very last conversion value. 
  fahr <- (degC*1.8) + 32
  
  # Print the outputs
  print(fahr)
}

[1] 95
[1] 96.8
[1] 98.6
[1] 100.4
[1] 102.2
[1] 104

Yep, that’s pretty toasty!

For more help on for loops and other iterative processes, make sure to check out the R for Data Science book by Hadley Wickham and Garrett Grolemund!