R Tutorial: Optimizing R Code with Rcpp | Intro

Key Takeaways

welcome to this course my name is Roman Francois I'm one of the authors of the LGBT package and I will be your instructor our is a great language but sometimes it's slow that is because our is an interpreted language our expressions are passed and interpreted at runtime on the other end C++ is a compiled language the C++ compilers do a great job of translating C++ statements into machine code that runs much faster in this course you will learn how to use the RCP package to make your code faster by writing critical paths in C++ you don't need to know everything there is to know about C++ but only a small subset it has always been possible to use C or C++ in your our code but our CPP makes the process very easy you only have to focus on writing simple C++ code and our CPP takes care of converting our objects to C++ and vice versa the course is divided into four chapters that will gradually equip you with skills to take advantage of C++ in your R code the first chapter covers basic C++ syntax the second chapter is about C++ functions you will learn how to write them and how to make them available to our most of the time using our CBP consists of manipulating our vectors you will learn in the third chapter how to access existing our vectors and create new ones finally in the last chapter you'll practice your new skills on many case studies when you use our CPP you typically want to keep the main logic of your code in R and resort to C++ for data intensive work or when your our code cannot easily be expressed with vectorized functions the micro benchmark package will help you to compare performance of two or more versions of your code for example the initial our code and the supposedly optimized C++ version as illustrated here micro benchmark takes any number of our expressions evaluates them hundred times each and then summarizes the information we typically only look at the median : for more information about benchmarking and profiling that is finding the actual bottlenecks in your code please refer to the datacom course writing efficient R code by : Gillespie as a compiled language C++ does not offer a console for interactive use but our CPP is a few utilities to mimic that the eval CPP function takes a string of valid C++ code compiled it under the hood and evaluates it this is often used to check if a machine has been properly set up to use our CPP C++ has a rich set of basic number types you will only use integers of type int and floating-point numbers of type double because those are the types used in our there's a difference between our C++ about literal numbers in our literal integer numbers are doubles by default and you have to convert them explicitly to introduce using either as dot integer or with the capital L suffix in C++ literal integer numbers are integer and you can suffix them with dot 0 to force them to be a double to convert explicitly to a double you can also use casting with the syntax presented here the name of the target type your double between parenthesis before the object being cast casting to a double is sometimes necessary for example C++ uses integer division when both operands are int whereas our promotes automatically to doubles when needed now it's your turn to write some basic C++ code