How To Use Logic Operators in R Programming For Data Model Accuracy

Boolean operators can help you identify logic discrepancies in a vector

Logic operators in R programming can help confirm if birds of a feather flock together (Source: Pierre DeBois)

Sometimes a few lines of code can check a degree of data agreement between models in a data frame or table — just enough to help identify overlap of numbers or note when columns that are meant to be equivalent are not. If you are looking to insert simple lines of code for those purposes, consider logic statements as functions that can best help you.

Logic statements in programming are designed to reveal which elements in an object are a match for another. That qualifying nature is meant to support Boolean operations. Boolean conditions are expressions that are either true or false. Thus logic, which usually means “characterized to be sound reasoning”, is defined as confirming something in a binary way. So you are ultimately confirming the logic quality of your R object. In R programming Zero is treated as a FALSE value. Non-zero numbers are considered a TRUE value.

AND and OR

A single ampersand (&) or single pipe (|) evaluates all vectors. The AND logic, &, conducts a Boolean evaluation for each element in two or more vectors. The OR logic, |, evaluates if either element in two or more vectors are true or false. In the example…