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Curso

Optimizing R Code with Rcpp

IntermediárioNível de habilidade

Atualizado 02/2025

Use C++ to dramatically boost the performance of your R code.

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RProgramming

4 h

15 vídeos

52 Exercícios

4,350 XP

3,958

Declaração de realização

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Descrição do curso

R is a great language for data science, but sometimes the code can be slow to run. Combining the comfort of R with the speed of a compiled language is a great way to reclaim the performance your code deserves.C++ is a modern, high performance language that is simple enough to learn in the context of accelerating R code. With the help of the Rcpp package, C++ integrates very neatly with R. You will learn how to create and manipulate typical R objects (vectors and lists), and write your own C++ functions to dramatically boost the performance of your R code.

Pré-requisitos

Introduction to Writing Functions in R

1

Introduction

Writing, benchmarking, and debugging your first C++ code.

Welcome to the course

Benchmarking with microbenchmark

Simple C++ Expressions with evalCpp

Conversion between numbers using cast

Inline functions with cppFunction

First function

Euclidean distance from 0

Print to the console

Error messages

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2

Functions and Control Flow

Writing functions, controlling the flow with if and else, and learning to use the three kinds of loops in C++.

C++ functions belong to C++ files

What happens when you compile this C++ file

Boiler plate

Writing functions in C++

First function - again

Exported and unexported functions

R code in C++ files

if and if/else

Calculating square roots with a for loop

Breaking out of a for loop

While loops

Calculating square roots with a while loop

Do it again: do-while loop

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3

Vector Classes

Manipulate and compute with Rcpp and native C++ vectors.

Rcpp classes and vectors

First and last values of a vector

Indexing a vector

Sum of double vector

Creating vectors

Sequence of integers

Create vector with given values

Vector cloning

Weighted mean

Weighted mean (C++ version)

Handling of missing values

Vectors from the STL

Don't change the size of Rcpp vectors

STL vectors

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4

Case Studies

Use random numbers and write algorithms for applied time series models.

Random number generation

Scalar random number generation

Sampling from a mixture of distributions (I)

Sampling from a mixture of distributions (II)

Rolling operations

Rolling means

Rolling means (in C++)

Last observation carried forward

Mean carried forward

Auto regressive model

Simulate AR(p) model

Simulate MA(q) model

ARMA (p, q) model

Congratulations!

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Optimizing R Code with Rcpp

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