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Optimizing R Code with Rcpp

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

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4 Hours15 Videos52 Exercises

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Course Description

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.

1
Introduction
Free
Writing, benchmarking, and debugging your first C++ code.
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Welcome to the course
50 xp
Benchmarking with microbenchmark
100 xp
Simple C++ Expressions with evalCpp
100 xp
Conversion between numbers using cast
100 xp
Inline functions with cppFunction
50 xp
First function
100 xp
Euclidean distance from 0
100 xp
Debugging
50 xp
Print to the console
100 xp
Error messages
100 xp
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++.
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C++ functions belong to C++ files
50 xp
What happens when you compile this C++ file
50 xp
Boiler plate
100 xp
Writing functions in C++
50 xp
First function - again
100 xp
Exported and unexported functions
100 xp
R code in C++ files
100 xp
if and if/else
100 xp
For loops
50 xp
Calculating square roots with a for loop
100 xp
Breaking out of a for loop
100 xp
While loops
50 xp
Calculating square roots with a while loop
100 xp
Do it again: do-while loop
100 xp
3
Vector classes
Manipulate and compute with Rcpp and native C++ vectors.
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Rcpp classes and vectors
50 xp
First and last values of a vector
50 xp
Indexing a vector
100 xp
Sum of double vector
100 xp
Creating vectors
50 xp
Sequence of integers
100 xp
Create vector with given values
100 xp
Vector cloning
100 xp
Weighted mean
50 xp
Weighted mean (C++ version)
100 xp
Handling of missing values
100 xp
Vectors from the STL
50 xp
Don't change the size of Rcpp vectors
100 xp
STL vectors
100 xp
4
Case Studies
Use random numbers and write algorithms for applied time series models.
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Random number generation
50 xp
Scalar random number generation
100 xp
Sampling from a mixture of distributions (I)
100 xp
Sampling from a mixture of distributions (II)
100 xp
Rolling operations
50 xp
Rolling means
100 xp
Rolling means (in C++)
100 xp
Last observation carried forward
100 xp
Mean carried forward
100 xp
Auto regressive model
50 xp
Simulate AR(p) model
100 xp
Simulate MA(q) model
100 xp
ARMA (p, q) model
100 xp
Congratulations!
50 xp

Collaborators

Sumedh Panchadhar

Richie Cotton

Prerequisites

Introduction to Writing Functions in R

R Training

ThinkR provides specialized consulting, development and training in R software.

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