Introduction to Data Science Using R Runestone Version 1

R-Studio is an Integrated Development Environment (IDE). Every software engineer knows that if you want to get serious about building something out of code, you must use an IDE because it will make your programming so much easier. Before we start that, let’s consider why we need an IDE to work with R. One can run R commands using what is known as the "R console," but the console is an old technology term that dates back to the days when computers were so big that they each occupied their own air conditioned room. Within that room there was often one "master control station" where a computer operator could do just about anything to control the giant computer by typing in commands. That station was known as the console. The term console is now used in many cases to refer to any interface where you can directly type in commands.

It is way too easy to make a mistake to create what computer scientists refer to as a bug if you are doing every little task via the console. Using an IDE, we can easily build reusable pieces of code. The IDE gives us the capability to open up the process of creation, to peer into the component parts when we need to, and to close the hood and hide them when we don’t. Because we are working with data, we also need a way of closely inspecting the data, both its contents and its structure. It can get pretty tedious doing this at the R console, where almost every piece of output is a chunk of text and longer chunks scroll off the screen before you can see them. As an IDE for R, R-Studio allows us to control and monitor both our code and our text in a way that supports the creation of reusable elements.