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

Debugging

Print to the console

Error messages

Introduction

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

For loops

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

Functions and Control Flow

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

Vector Classes

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!

Case Studies

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.


Introduction to Writing Functions in R

Learn how to use the Rcpp package to intergrate C++ with R to boost the performance of your R code. You'll learn to write, debug, and control C++ code.

Optimizing R Code with Rcpp

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

Consulting Datactive, ThinkR

The 4 parts of C++ for loops

For loops - the initialization

For loops - the continue condition

For loops - the increment

For loops - the body

Typical for loop

Example: sum of n first integers

Newton iterative method to calculate square roots

Newton's method in C++

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_3963/subtitles/course_3963_2b5067806e451827d2278708f17763d4.vtt

Functions and Control Flow - For loops

For loops

Create Your Free Account