Mastering R Operators: Unlock Efficient Coding Discover the secret behind R’s infix operators, how to create custom operators, and best practices for defining them. Boost your coding efficiency and readability with this comprehensive guide.

Unlocking the Power of R Operators

When working with R, understanding operators is crucial for efficient coding. Most R operators are binary, meaning they require two operands, and are infix, meaning they’re placed between the operands. But did you know that these operators are actually function calls in disguise?

The Magic Behind Infix Operators

Take the simple expression a + b. What’s happening behind the scenes? R is actually calling the + function with arguments a and b, like this: +(a, b). Note the back tick (`) around the function name, which is essential when working with special symbols.

Examples of Infix Operators in Action

Here are some examples of infix operators and the functions they call:

• a + b calls the + function
• a * b calls the * function
• a == b calls the == function

Creating Your Own Infix Operators

But what if you want to create your own custom infix operators? R makes it possible! By defining a function that starts and ends with %, you can create a user-defined infix operator. For instance, let’s create an operator to check if a number is exactly divisible by another:

R
`%divisible%` <- function(a, b) {
a %% b == 0
}


Now, you can use this operator like this: a %divisible% b or as a function call: %divisible%(a, b). Both are equivalent!

Best Practices for Defining Infix Operators

When creating your own infix operators, remember:

• They must start and end with %
• Surround the function definition with back ticks (`) to escape special symbols
• Use these operators to simplify your code and make it more readable!

Predefined Infix Operators in R

R comes with a range of predefined infix operators, from arithmetic operators like + and - to logical operators like && and ||. By mastering these operators and learning how to create your own, you’ll become a more efficient and effective R programmer.