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Christoph Mayer has completed

Optimizing R Code with Rcpp

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4 hr
4,350 XP
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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.
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  1. 1

    Introduction

    Free

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

    Play Chapter Now
    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. 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. 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. 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
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collaborators

Collaborator's avatar
Sumedh Panchadhar
Collaborator's avatar
Richie Cotton

prerequisites

Introduction to Writing Functions in R
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