When you need to make more complex decisions in your R programs, a single if statement might not be enough. Sometimes, you want to check one condition, and only if that is true, check another condition. This is where nested conditions come in. Nesting if statements means placing one if statement inside another. You might need nested conditions when the outcome of one decision leads to another, more specific decision. For example, you may want to check if a number is positive and then determine if it is even or odd.

In this example, the first if checks if num is greater than 0. If this is true, the code moves on to the inner if statement, which checks if num is even using the modulo operator (%%). The inner if statement only runs if the outer condition is true. If the outer condition is false, the code skips the nested statements entirely and prints that the number is not positive.

Nesting lets you refine your decisions step by step. In the age categorization example, each layer checks a more specific condition as you go deeper into the nested structure. This approach is powerful, but it can make your code harder to read if overused. To keep your code clear, try to avoid unnecessary nesting and consider using else if or breaking your logic into functions when possible.

Deeply nested code can be hard to follow and maintain. To avoid confusion, keep your nesting as shallow as possible. Use clear variable names, comment your logic, and refactor complex conditions into helper functions when needed. This will make your code much easier for others (and yourself) to understand in the future.