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Classes, Methods, Factors, and Levels in R Programming
Understanding the underlying categories in R can help you plan your functions and libraries to efficiently manage data.
In many instances, data scientists and marketers use just the basic frameworks for assigning data within a code. Languages like R, JavaScript, and Python can assign a number or a set of numbers within a data object easily.
While working with a language you may have data that has a specific set of categories associated with it. The categories are more nuanced than what is set within a data frame or matrix. To set up these categories, you need to learn about classes, methods, factors, and levels to arrange data into suitable categories.
The phrase Classes, Methods, Factors, and Levels sounds like a multidimensional version of the Chutes and Ladders game. In reality, they are useful to know when trying to understand parameters within a library or for creating a new library that contains functions. They can also be helpful when deciding how to classify or bin data in a program, as factors and levels demonstrate (You can learn about binning data in this post). This post will explain how classes, methods, factors, and levels are arranged within R programming to make these benefits possible.
