R Tutorial: Inline functions with cppFunction

Want to learn more? Take the full course at https://learn.datacamp.com/courses/optimizing-r-code-with-rcpp at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- So far, you have only used "evalCpp()" to evaluate short C++ expressions. This limits you to very simple code, the next step is to define your own C++ functions. In the next chapter, you will learn how to write C++ functions in .cpp files, but for now, let's enjoy the comfort of the R console and define functions directly. Similar to evalCpp(), "cppFunction()" takes a string of valid C++ code. But this time the string contains code that defines an entire C++ function. You will learn about C++ functions in a minute, so hang in there if you don't quite understand them just yet. In between the C++ code you type and the R function you call from the R console, there are many layers of engineering. The strength of Rcpp is that you never need to worry about any of that. It all just works. You only have to write the C++ code of the function, and cppFunction() takes care of everything for you: Surround your code with boilerplate and save it to a temporary .cpp file. Compile the cpp file with the C++ compiler and load it directly in the global environment. You can then call your function just like any other R function. In R, variables can first be defined as one type and then later be redefined as something else. This is because R is what is called a dynamically typed language, you can generally not infer the type of a variable before the runtime. On the other hand, C++ is a statically typed language, that means that the compiler must know the type of each object and that type can never change. Consequently, it also means that functions must declare the type of each of their arguments and the type of the objects they return. Thus writing C++ code is more work, but having that information allows the compiler to optimize the code and it's one