---
title: |
       | Extending \rlang with C++:
       | A Brief Introduction to Rcpp

# Use letters for affiliations
author:
  - name: Dirk Eddelbuettel
    affiliation: a
  - name: James Joseph Balamuta
    affiliation: b
address:
  - code: a
    address: Debian and R Projects; Chicago, IL, USA; \url{edd@debian.org}
  - code: b
    address: Depts of Informatics and Statistics, Univ. of Illinois at Urbana-Champaign; Champaign, IL, USA; \url{balamut2@illinois.edu}

# For footer text  TODO(fold into template, allow free form two-authors)
lead_author_surname: Eddelbuettel and Balamuta

# Place DOI URL or CRAN Package URL here
doi: "https://cran.r-project.org/package=Rcpp"

# Abstract
abstract: |
  \rlang has always provided an application programming interface (API) for extensions.
  Based on the \clang language, it uses a number of macros and other low-level constructs
  to exchange data structures between the \rlang process and any dynamically-loaded component
  modules authors added to it. With the introduction of the \rcpp package, and its later
  refinements, this process has become considerably easier yet also more robust.  By now, \rcpp has
  become the most popular extension mechanism for \rlangns. This article introduces \rcppns, and
  illustrates with several examples how the _Rcpp Attributes_ mechanism in particular
  eases the transition of objects between \rlang and \cpp code.

# Acknowledgements
acknowledgements: |
  We thank Bob Rudis and Lionel Henry for excellent comments and suggestion on an earlier
  draft of this manuscript. Furthermore, we appreciate the improved \cpp annotated function
  graphic provided by Bob Rudis. This version is a pre-print of \citet{PeerJ:Rcpp,TAS:Rcpp}.

# One or more keywords
keywords:
  - applications and case studies
  - statistical computing
  - computationally intensive methods
  - simulation

# Bibliography
bibliography: Rcpp

# Enable a watermark on the document
watermark: false

# Customize footer, eg by referencing the vignette
footer_contents: "Rcpp Vignette"

# Produce a pinp document
output:
    pinp::pinp:
        collapse: true

# Local additiona of a few definitions we use
header-includes: >
  \newcommand{\TODO}{\marginnote{TODO}}
  \newcommand{\rlang}{\textit{R }}
  \newcommand{\rlangns}{\textit{R}}
  \newcommand{\slang}{\textit{S }}
  \newcommand{\rcpp}{\textit{Rcpp }}
  \newcommand{\rcppns}{\textit{Rcpp}}
  \newcommand{\clang}{\textit{C }}
  \newcommand{\clangns}{\textit{C}}
  \newcommand{\cpp}{\textit{C++ }}
  \newcommand{\cppns}{\textit{C++}}
  \newcommand{\fortran}{\textit{Fortran }}
  \newcommand{\fortranns}{\textit{Fortran}}
  \newcommand{\python}{\textit{Python}}
  \newcommand{\julia}{\textit{Julia}}
  \newcommand{\pkg}[1]{\textbf{#1}}

vignette: >
  %\VignetteIndexEntry{Rcpp-introduction}
  %\VignetteKeywords{Rcpp, R, Cpp}
  %\VignettePackage{Rcpp}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(cache=FALSE)
library(Rcpp)
options("width"=50, digits=5)
```


# Introduction

The \rlang language and environment \citep{R:main} has established itself as
both an increasingly dominant facility for data analysis, and the
*lingua franca* for statistical computing in both research and
application settings.

Since the beginning, and as we argue below, "by design",
the \rlang system has always provided an application programming interface (API)
suitable for extending \rlang with code written in \clang or \fortranns.  Being
implemented chiefly in \rlang and \clang (with a generous sprinkling of \fortran
for well-established numerical subroutines), \rlang has always been
extensible via a \clang interface.  Both the actual
implementation and the \clang interface use a number of macros and other
low-level constructs to exchange data structures between the \rlang process
and any dynamically-loaded component modules authors added to it.

A \clang interface will generally also be accessible to other languages.
Particularly noteworthy here is the \cpp language, developed originally as a
'better \clangns', which is by its design very interoperable with
\clangns. And with the introduction of the \rcpp package
\citep{JSS:Rcpp,Eddelbuettel:2013:Rcpp,CRAN:Rcpp}, and its later refinements, this
process of extending \rlang has become considerably easier yet also more robust.
To date, \rcpp has become the most popular extension system for
\rlangns. This article introduces \rcppns, and illustrates with several
examples how the _Rcpp Attributes_ mechanism
\citep{CRAN:Rcpp:Attributes} in particular eases the transition of
objects between \rlang and \cpp code.


## Background

\citet[p. 3]{Chambers:2008:SoDA} provides a very thorough discussion of
desirable traits for a system designed to _program with data_,
and the \rlang system in particular. Two key themes motivate the introductory
discussion. First, the _Mission_ is to aid exploration in order to
provide the best platform to analyse data: "to boldly go where no one
has gone before." Second, the _Prime Directive_ is that the software
systems we build must be _trustworthy_: "the many computational steps
between original data source and displayed result must all be
trustful."  The remainder of the book then discusses \rlangns, leading
to two final chapters on interfaces.

\citet[p. 4]{Chambers:2016:ExtR} builds and expands on this theme.  Two core
facets of what "makes" \rlang are carried over from the previous book. The
first states what \rlang is composed of: _Everything that exists in \rlang is
an object_.  The second states how these objects are created or
altered: _Everything that happens in \rlang is a function call_. A third
statement is now added: _Interfaces to other software are part of \rlangns_.

This last addition is profound. If and when suitable and performant
software for a task exists, it is in fact desirable to have a (preferably
also perfomant) interface to this software from \rlangns.
\cite{Chambers:2016:ExtR} discusses several possible approaches for simpler
interfaces and illustrates them with reference implementations to both \python\ and
\julia. However, the most performant interface for \rlang is provided at the
subroutine level, and rather than discussing the older \clang interface for
\rlangns, \cite{Chambers:2016:ExtR} prefers to discuss \rcppns.  This article
follows the same school of thought and aims to introduce \rcpp to
analysts and data scientists, aiming to enable them to use---and
create--- further _interfaces_ for \rlang which aid the _mission_ while
staying true to the _prime directive_.  Adding interfaces in such a way is in
fact a natural progression from the earliest designs for its predecessor
\slang which was after all designed to provide a more useable 'interface' to
underlying routines in \fortranns.

The rest of the paper is structured as follows. We start by discussing
possible first steps, chiefly to validate correct installations. This
is followed by an introduction to simple \cpp functions, comparison to
the \clang API, a discussion of packaging with \rcpp and a linear algebra
example.  The appendix contains some empirical illustrations of the
adoption of \rcppns.


# First Steps with \rcpp

\rcpp is a CRAN package and can be installed
by using `install.packages('Rcpp')` just like any other \rlang package. On
some operating systems this will download _pre-compiled_ binary packages; on
others an installation from source will be attempted.  But \rcpp is a little
different from many standard \rlang packages in one important aspect: it helps the user
to _write C(++) programs more easily_.  The key aspect to note here is \cpp
programs: to operate, \rcpp needs not only \rlang but also an additional
_toolchain_ of a compiler, linker and more in order to be able to create _binary_ object
code extending \rlangns.

We note that this requirement is no different from what is needed with base \rlang when
compilation of extensions is attempted.  How to achieve this using only base \rlang is described in
some detail in the _Writing R Extensions_ manual \citep{R:Extensions} that is included
with \rlangns. As for the toolchain requirements, on Linux and macOS, all required
components are likely to be present. The macOS can offer additional challenges as toolchain
elements can be obtained in different ways. Some of these are addressed in the _Rcpp FAQ_
\citep{CRAN:Rcpp:FAQ} in sections 2.10 and 2.16.  On Windows, users will have to install
the Rtools kit provided by R Core available at <https://cran.r-project.org/bin/windows/Rtools/>.
Details of these installation steps are beyond the scope of this
paper. However, many external resources exist that provide detailed
installation guides for \rlang toolschains in
[Windows](http://thecoatlessprofessor.com/programming/rcpp/install-rtools-for-rcpp/)
and
[macOS](http://thecoatlessprofessor.com/programming/r-compiler-tools-for-rcpp-on-os-x/).

As a first step, and chiefly to establish that the toolchain is set up correctly, consider
a minimal use case such as the following:

```{r evalCpp}
library("Rcpp")
evalCpp("2 + 2")
```

Here the \rcpp package is loaded first via the `library()` function.  Next, we deploy one
of its simplest functions, `evalCpp()`, which is described in the _Rcpp Attributes_ vignette
\citep{CRAN:Rcpp:Attributes}. It takes the first (and often only) argument---a character
object---and evaluates it as a minimal \cpp expression.  The value assignment and return
are implicit, as is the addition of a trailing semicolon and more.  In fact, `evalCpp()`
surrounds the expression with the required 'glue' to make it a minimal source file which
can be compiled, linked and loaded. The exact details behind this process
are available in-depth when the `verbose` option of the function is set.
If everything is set up correctly, the newly-created \rlang function will be
returned.

While such a simple expression is not interesting in itself, it serves a
useful purpose here to unequivocally establish whether \rcpp is correctly
set up.  Having accomplished that, we can proceed to the next step of
creating simple functions.

# A first \cpp function using \rcpp

As a first example, consider the determination of whether a number is odd or even. The
default practice is to use modular arithmetic to check if a remainder exists under $x
\bmod 2$. Within \rlangns, this can be implemented as follows:

```{r isOddR, cache=TRUE}
isOddR <- function(num = 10L) {
   result <- (num %% 2L == 1L)
   return(result)
}
isOddR(42L)
```

The operator `%%` implements the $\bmod$ operation in \rlangns. For the default
(integer) argument of ten used in the example, $10 \bmod 2$ results in zero, which is then
mapped to `FALSE` in the context of a logical expression.

Translating this implementation into \cppns, several small details have to be
considered. First and foremost, as \cpp is a _statically-typed language_, there needs to
be additional (compile-time) information provided for each of the variables. Specifically,
a *type*, _i.e._ the kind of storage used by a variable must be explicitly
defined. Typed languages generally offer benefits in terms of both correctness
(as it is harder to accidentally assign to an ill-matched type) and
performance (as the compiler can optimize code based on the storage and cpu
characteristics). Here we have an `int` argument, but return a logical, or
`bool` for short.  Two more smaller differences are that each statement
within the body must be concluded with a semicolon, and that `return` does
not require parentheses around its argument. A graphical breakdown of all
aspects of a corresponding \cpp function is given in Figure \ref{fig:cpp-function-annotation}.

\begin{figure*}
  \begin{center}
    \includegraphics[width=5.5in]{figures/function_annotation_cpp.png}
    \caption{Graphical annotation of the \texttt{is\_odd\_cpp} function.}
    \label{fig:cpp-function-annotation}
  \end{center}
\end{figure*}

When using \rcppns, such \cpp functions can be directly embedded and compiled in an \rlang
script file through the use of the `cppFunction()` provided by _Rcpp Attributes_
\citep{CRAN:Rcpp:Attributes}. The first parameter of the function accepts string input
that represents the \cpp code. Upon calling the `cppFunction()`, and similarly to the
earlier example involving `evalCpp()`, the \cpp code is both _compiled_ and _linked_, and
then _imported_ into \rlang under the name of the function supplied (_e.g._ here `isOddCpp()`).

```{r isOddRcpp}
library("Rcpp")
cppFunction("
bool isOddCpp(int num = 10) {
   bool result = (num % 2 == 1);
   return result;
}")
isOddCpp(42L)
```

# Extending \rlang via its \clang API

Let us first consider the case of 'standard \rlangns', _i.e._ the API as
defined in the core \rlang documentation.  Extending \rlang with routines written
using the \clang language requires the use of internal macros and functions
documented in Chapter 5 of _Writing R Extensions_ \citep{R:Extensions}.

```{Rcpp convolutionC, eval = FALSE}
#include <R.h>
#include <Rinternals.h>

SEXP convolve2(SEXP a, SEXP b) {
    int na, nb, nab;
    double *xa, *xb, *xab;
    SEXP ab;

    a = PROTECT(coerceVector(a, REALSXP));
    b = PROTECT(coerceVector(b, REALSXP));
    na = length(a); nb = length(b);
    nab = na + nb - 1;
    ab = PROTECT(allocVector(REALSXP, nab));
    xa = REAL(a); xb = REAL(b); xab = REAL(ab);
    for (int i = 0; i < nab; i++)
        xab[i] = 0.0;
    for (int i = 0; i < na; i++)
	    for (int j = 0; j < nb; j++)
            xab[i + j] += xa[i] * xb[j];
    UNPROTECT(3);
    return ab;
}
```

This function computes a _convolution_ of two vectors supplied on input, $a$ and
$b$, which is defined to be ${ab_{k + 1}} = \sum\limits_{i + j == k} {{a_i} \cdot {b_j}}$.
Before computing the convolution (which is really just the three lines involving two
nested for loops with indices $i$ and $j$), a total of ten lines of mere housekeeping
are required. Vectors $a$ and $b$ are coerced to `double`, and a results vector `ab` is
allocated. This expression involves three calls to the `PROTECT` macro for which a _precisely_
matching `UNPROTECT(3)` is required as part of the interfacing of internal memory
allocation. The vectors are accessed through pointer equivalents `xa`, `xb` and `xab`; and
the latter has to be explicitly zeroed prior to the convolution calculation involving
incremental summary at index $i+j$.


# Extending \rlang via the \cpp API of \rcpp

Using the idioms of \rcppns, the above example can be written in a much more
compact fashion---leading to code that is simpler to read and
maintain.

```{Rcpp convolutionRcpp, eval = FALSE}
#include "Rcpp.h"
using namespace Rcpp;

// [[Rcpp::export]]
NumericVector
convolve_cpp(const NumericVector& a,
             const NumericVector& b) {

    // Declare loop counters, and vector sizes
    int i, j,
        na = a.size(), nb = b.size(),
        nab = na + nb - 1;

    // Create vector filled with 0
    NumericVector ab(nab);

    // Crux of the algorithm
    for(i = 0; i < na; i++) {
        for(j = 0; j < nb; j++) {
            ab[i + j] += a[i] * b[j];
        }
    }

    // Return result
    return ab;
}
```

To deploy such code from within an \rlang script or session, first save it into a new
file---which could be called **convolve.cpp**---in either the working directory, a
temporary directoy or a project directory. Then from within the \rlang session,
use `Rcpp::sourceCpp("convolve.cpp")` (possibly using a path as well as the filename). This
not only compiles, links and loads the code within the external file but also adds the
necessary "glue" to make the \rcpp function available in the \rlang environment. Once the
code is compiled and linked, call the newly-created `convolve_cpp()` function with the
appropriate parameters as done in previous examples.

What is notable about the \rcpp version is that it has no `PROTECT` or `UNPROTECT`
which not only frees the programmer from a tedious (and error-prone) step but
more importantly also shows that memory management can be handled automatically.
The result vector is already initialized at zero as well,
reducing the entire function to just the three lines for the two nested loops,
plus some variable declarations and the `return` statement.  The resulting code is
shorter, easier to read, comprehend and maintain. Furthermore, the \rcpp code
is more similar to traditional \rlang code, which reduces
the barrier of entry.


# Data Driven Performance Decisions with \rcpp

When beginning to implement an idea, more so an algorithm, there are many ways
one is able to correctly implement it. Prior to the routine being used in
production, two questions must be asked:

1. Does the implementation produce the _correct_ results?
2. What implementation of the routine is the _best_?

The first question is subject to a binary pass-fail unit test verification
while the latter question is where the details of an implementation are scrutinized
to extract maximal efficiency from the routine. The quality of the _best_ routine
follows first and foremost from its correctness. To that end,
\rlang offers many different unit testing frameworks such as \pkg{RUnit} by
\cite{CRAN:RUnit}, which is used to construct \rcppns's 1385+ unit tests, and
\pkg{testthat} by \cite{CRAN:testthat}. Only when correctness is
achieved is it wise to begin the procedure of optimizing the efficiency of
the routine and, in turn, selecting the best routine.

Optimization of an algorithm involves performing a quantitative analysis of the
routine's properties. There are two main approaches to analyzing the behavior
of a routine: theoretical analysis\footnote{
Theoretical analysis is often directed to describing
the limiting behavior of a function through asymptotic notation,
commonly referred to as Big O and denoted as $\mathcal{O}(\cdot)$.}
or an empirical examination using profiling tools.\footnote{
Within base \rlangns, profiling can be activated by \code{utils::Rprof()}
for individual command timing information, \code{utils::Rprofmem()} for memory
information, and \code{System.time(\{\})} for a quick overall execution timing.
Additional profiling \rlang packages such as \pkg{profvis} by
\cite{CRAN:profvis}, \pkg{Rperform} by \cite{GitHub:Rperform}, and
benchmarking packages have extended the ability to analyze performance.}
Typically, the latter option is more prominently used
as the routine's theoretical properties are derived prior to an implementation
being started. Often the main concern regarding an implementation in \rlang relates
to the speed of the algorithm as it impacts how quickly analyses
can be done and reports can be provided to decision makers. Coincidentally, the
speed of code is one of the key governing use cases of \rcppns. Profiling \rlang
code will reveal shortcomings related to loops, _e.g._ `for`, `while`, and `repeat`;
conditional statements, _e.g._ `if`-`else if`-`else` and `switch`;
and recursive functions, _i.e._ a function written in terms of itself such that the
problem is broken down on each call in a reduced state until an answer can be
obtained. In contrast, the overhead for such operations is significantly less
in \cppns. Thus, critical components of a given routine should be written
in \rcpp to capture maximal efficiency.

Returning to the second question, to decide which
implementation works the best, one needs to employ a benchmark
to obtain _quantifiable results_. Benchmarks are an ideal way to quantify how
well a method performs because they have the ability to show the amount of time the
code has been running and where bottlenecks exist within functions.
This does not imply that benchmarks are completely infallible as user error
can influence the end results. For example, if a user decides
to benchmark code in one \rlang session and in another session performs a heavy
computation, then the benchmark will be biased (if "wall clock" is measured).

There are different levels of magnification that a benchmark can provide. For a more macro
analysis, one should benchmark data using `benchmark(test = func(), test2 = func2())`,
a function from the \pkg{rbenchmark} \rlang package by \cite{CRAN:rbenchmark}. This form of
benchmarking will be used when the computation is more intensive.
The motivating example `isOdd()`
(which is only able to accept a single `integer`) warrants a much more
microscopic timing comparison.  In cases such as this, the objective
is to obtain precise results in the amount of nanoseconds elapsed. Using the
`microbenchmark` function from the \pkg{microbenchmark} \rlang package by
\cite{CRAN:microbenchmark} is more helpful to obtain timing information.
To perform the benchmark:

```{r microbenchmark_isOdd, dependson=c("isOddR", "isOddRcpp"), eval=FALSE}
library("microbenchmark")
results <- microbenchmark(isOddR   = isOddR(12L),
                          isOddCpp = isOddCpp(12L))
print(summary(results)[, c(1:7)],digits=1)
```

By looking at the summary of 100 evaluations, we note that the
\rcpp function performed better than the equivalent in \rlang by achieving
a lower run time on average. The lower run time in this part is not necessarily
critical as the difference is nanoseconds on a trivial computation.
However, each section of code does contribute to a faster overall runtime.

# Random Numbers within \rcppns: An Example of _Rcpp Sugar_

\rcpp connects \rlang with \cppns. Only the former is vectorized: \cpp is not.
_Rcpp Sugar_, however, provides a convenient way to work with high-performing
\cpp functions in a similar way to how \rlang offers vectorized operations.
The _Rcpp Sugar_ vignette \citep{CRAN:Rcpp:Sugar} details these, as well as
many more functions directly accessible to \rcpp in a way that should feel
familiar to \rlang users. Some examples of _Rcpp Sugar_ functions
include special math functions like gamma and beta, statistical distributions
and random number generation.

We will illustrate a case of random number generation.  Consider drawing one
or more $N(0,1)$-distributed random variables. The very
simplest case can just use `evalCpp()`:

```{r rnormScalar}
evalCpp("R::rnorm(0, 1)")
```

By setting a seed, we can make this reproducible:

```{r normWithSeed}
set.seed(123)
evalCpp("R::rnorm(0, 1)")
```

One important aspect of the behind-the-scenes code generation for the single expression
(as well as all code created via _Rcpp Attributes_) is the automatic preservation of the
state of the random nunber generators in \rlangns.  This means that from a given seed, we will
receive _identical_ draws of random numbers whether we access them from \rlang or via \cpp code
accessing the same generators (via the \rcpp interfaces). To illustrate, the
same number is drawn via \rlang code after resetting the seed:

```{r rnormWithSeedFromR}
set.seed(123)
# Implicit mean of 0, sd of 1
rnorm(1)
```

We can make the _Rcpp Sugar_
function `rnorm()` accessible from \rlang in the same way to return a vector of values:

```{r rnormExCpp}
set.seed(123)
evalCpp("Rcpp::rnorm(3)")
```

Note that we use the `Rcpp::` namespace explicitly here to
contrast the vectorised `Rcpp::rnorm()` with the scalar `R::rnorm()`
also provided as a convenience wrapper for the \clang API of \rlangns.

And as expected, this too replicates from \rlang as the very
same generators are used in both cases along with consistent handling
of generator state permitting to alternate:

```{r rnormExR}
set.seed(123)
rnorm(3)
```

# Translating Code from \rlang into \rcppns: Bootstrap Example

Statistical inference relied primarily upon asymptotic theory until
\cite{Efron:1979:Bootstrap} proposed the bootstrap. Bootstrapping is known
to be computationally intensive due to the need to use loops. Thus, it is an ideal candidate to use as an
example. Before starting to write \cpp code using \rcpp, prototype the code in \rlangns.

```{r bootstrap_in_r}
# Function declaration
bootstrap_r <- function(ds, B = 1000) {

  # Preallocate storage for statistics
  boot_stat <- matrix(NA, nrow = B, ncol = 2)

  # Number of observations
  n <- length(ds)

  # Perform bootstrap
  for(i in seq_len(B)) {
     # Sample initial data
     gen_data <- ds[ sample(n, n, replace=TRUE) ]
     # Calculate sample data mean and SD
     boot_stat[i,] <- c(mean(gen_data),
                        sd(gen_data))
  }

  # Return bootstrap result
  return(boot_stat)
}
```

Before continuing, check that the initial prototype \rlang code works. To
do so, write a short \rlang script. Note the use of `set.seed()` to ensure
reproducible draws.

```{r bootstrap_example}
# Set seed to generate data
set.seed(512)
# Generate data
initdata <- rnorm(1000, mean = 21, sd = 10)
# Set a new _different_ seed for bootstrapping
set.seed(883)
# Perform bootstrap
result_r <- bootstrap_r(initdata)
```

Figure \ref{fig:bootstrap-graphs} shows that the bootstrap procedure worked well!

```{r dist_graphs, echo = FALSE, results = "hide", eval=FALSE}
make_boot_graph <- function(ds, actual, type, ylim){
  hist(ds, main = paste(type, "Bootstrap"), xlab = "Samples",
       col = "lightblue", lwd = 2, prob = TRUE, ylim = ylim, cex.axis = .85, cex.lab = .90)
  abline(v = actual, col = "orange2", lwd = 2)
  lines(density(ds))
}
pdf("figures/bootstrap.pdf", width=6.5, height=3.25)
par(mfrow=c(1,2))
make_boot_graph(result_r[,1], 21, "Mean", c(0, 1.23))
make_boot_graph(result_r[,2], 10, "SD", c(0, 1.85))
dev.off()
```

\begin{figure*}
  \begin{center}
    \includegraphics[width=6.5in, height=3.25in]{figures/bootstrap}
    \caption{Results of the bootstrapping procedure for sample mean and variance.}
    \label{fig:bootstrap-graphs}
  \end{center}
\end{figure*}

With reassurances that the method to be implemented within \rcpp works
appropriately in \rlangns, proceed to translating the code
into \rcppns. As indicated previously, there are many convergences between \rcpp
syntax and base \rlang via \rcpp Sugar.


```{Rcpp bootstrap_in_cpp}
#include <Rcpp.h>

// Function declaration with export tag
// [[Rcpp::export]]
Rcpp::NumericMatrix
bootstrap_cpp(Rcpp::NumericVector ds,
              int B = 1000) {

  // Preallocate storage for statistics
  Rcpp::NumericMatrix boot_stat(B, 2);

  // Number of observations
  int n = ds.size();

  // Perform bootstrap
  for(int i = 0; i < B; i++) {
    // Sample initial data
    Rcpp::NumericVector gen_data =
        ds[ floor(Rcpp::runif(n, 0, n)) ];
    // Calculate sample mean and std dev
    boot_stat(i, 0) = mean(gen_data);
    boot_stat(i, 1) = sd(gen_data);
  }

  // Return bootstrap results
  return boot_stat;
}
```

In the \rcpp version of the bootstrap function, there are a few additional
changes that occurred during the translation. In particular, the use of
`Rcpp::runif(n, 0, n)` enclosed by `floor()`, which rounds down to the nearest integer,
in place of `sample(n, n, replace = TRUE)` to sample row ids. This is an
equivalent substitution since equal weight is being placed upon all row ids and
replacement is allowed.\footnote{For more flexibility in sampling see Christian Gunning's
Sample extension for \pkg{RcppArmadillo} and
\href{http://gallery.rcpp.org/articles/using-the-Rcpp-based-sample-implementation/}{Rcpp Gallery: Using the \pkg{RcppArmadillo}-based Implementation of R's sample()} or consider using the \code{Rcpp::sample()} sugar function added in 0.12.9 by Nathan Russell.}
Note that the upper bound of the interval, `n`, will never be reached. While
this may seem flawed, it is important to note that vectors and matrices in
\cpp use a zero-based indexing system, meaning that they begin at 0 instead of 1
and go up to $n-1$ instead of $n$, which is unlike \rlangns's system. Thus, an
out of bounds error would be triggered if `n` was used as that point does _not_
exist within the data structure. The application of this logic can be seen
in the span the `for` loop takes in \cpp when compared to \rlangns. Another
syntactical change is the use of `()` in place of `[]`
while accessing the matrix. This change is due to the governance of \cpp
and its comma operator making it impossible to place multiple indices inside
the square brackets.

To validate that the translation was successful, first run the \cpp function
under the _same_ data and seed as was given for the \rlang function.

```{r bootstrap_cpp}
# Use the same seed use in R and C++
set.seed(883)
# Perform bootstrap with C++ function
result_cpp <- bootstrap_cpp(initdata)
```

Next, check the output between the functions using \rlang's `all.equal()` function
that allows for an $\varepsilon$-neighborhood around a number.

```{r check_r_to_cpp}
# Compare output
all.equal(result_r, result_cpp)
```

Lastly, make sure to benchmark the newly translated \rcpp function against the
\rlang implementation. As stated earlier, data is paramount to making a decision
related to which function to use in an analysis or package.

```{r benchmark_r_to_cpp}
library(rbenchmark)

benchmark(r = bootstrap_r(initdata),
          cpp = bootstrap_cpp(initdata))[, 1:4]
```

# Using \rcpp as an Interface to External Libraries: Exploring Linear Algebra Extensions

Many of the previously illustrated \rcpp examples were directed primarily to
show the gains in computational efficiency that are possible by implementing
code directly in \cppns; however, this is only one potential application of \rcppns.
Perhaps one of the most understated features of \rcpp is its ability to
enable \cite{Chambers:2016:ExtR}'s third statement of
_Interfaces to other software are part of \rlangns_. In particular, \rcpp is
designed to facilitate interfacing libraries written in \cpp or \clang to \rlangns.
Hence, if there is a specific feature within a \cpp or \clang library,
then one can create a bridge to it using \rcpp to enable it from within \rlangns.

An example is the use of \cpp matrix algebra libraries like
\pkg{Armadillo} \citep{Sanderson:2010:Armadillo}
<!-- \citep{Sanderson+Curtin:2016} -->
or \pkg{Eigen} \citep{Eigen:Web}.  By outsourcing complex linear
algebra operations to matrix libraries, the need to directly call
functions within \pkg{Linear Algebra PACKage (LAPACK)}
\citep{Anderson:1990:UGLAPACK} is negated. Moreover, the \rcpp design
allows for seamless transfer between object types by using automatic
converters governed by `wrap()`, \cpp to \rlang, and `as<T>()`, \rlang
to \cpp with the `T` indicating the type of object being cast into.
These two helper functions provide a non-invasive way to work with an
external object. Thus, a further benefit to using external \cpp
libraries is the ability to have a portable code base that can be
implemented within a standalone \cpp program or within another
computational language.


## Compute RNG draws from a multivariate Normal

A common application in statistical computing is simulating from a multivariate normal distribution.
The algorithm relies on a linear transformation of the
standard Normal distribution.
Letting $\boldsymbol{Y}_{m \times 1} = \boldsymbol{A}_{m\times n}\boldsymbol{Z}_{n\times 1} + \boldsymbol{b}_{m\times 1}$,
where $\boldsymbol{A}$ is a $m \times n$ matrix, $\boldsymbol{b} \in \mathbb{R}^m$, $\boldsymbol{Z} \sim N(\boldsymbol{0}_{n},\boldsymbol{I}_n)$,
and $\boldsymbol{I}_n$ is the identity matrix, then ${\boldsymbol{Y}} \sim N_{m}\left({\boldsymbol{\mu} = \boldsymbol{b}, \boldsymbol{\Sigma} = \boldsymbol{A}\boldsymbol{A}^T}\right)$.
To obtain the matrix $\boldsymbol{A}$ from $\boldsymbol{\Sigma}$, either a Cholesky or Eigen decomposition
is required. As noted in \citet{Venables+Ripley:2002:MASS}, the Eigen decomposition is more
stable in addition to being more computationally demanding compared to
the Cholesky decomposition. For simplicity and speed, we have opted to implement
the sampling procedure using a Cholesky decomposition. Regardless, there
is a need to involve one of the above matrix libraries to make the sampling
viable in \cppns.

Here, we demonstrate how to take advantage of the *Armadillo* linear algebra template
classes \citep{Sanderson+Curtin:2016} via the \pkg{RcppArmadillo} package
\citep{Eddelbuettel+Sanderson:2013:RcppArmadillo, CRAN:RcppArmadillo}. Prior to
running this example, the \pkg{RcppArmadillo} package must be installed using
`install.packages('RcppArmadillo')`.\footnote{macOS users may encounter `-lgfortran`
and `-lquadmath` errors on compilations with this package if the development environment
is not appropriately set up. \href{https://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-FAQ.pdf}{Section 2.16 of the Rcpp FAQ} provides details regarding the necessary `gfortran` binaries.}
One important caveat when using additional packages
within the \rcpp ecosystem is the correct header file may not be `Rcpp.h`.
In a majority of cases, the additional package ships a dedicated header
(as _e.g._ `RcppArmadillo.h` here) which not only declares data
structures from both systems, but may also add complementary integration and conversion
routines.  It typically needs to be listed in an `include` statement along with a
`depends()` attribute to tell \rlang where to find the additional header files:

```{Rcpp armaDepends, eval = F}
// Use the RcppArmadillo package
// Requires different header file from Rcpp.h
#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]
```

With this in mind, sampling from a multivariate normal distribution can
be obtained in a straightforward manner. Using only _Armadillo_
data types and values:

```{Rcpp rmvnorm, eval = FALSE}
#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]

// Sample N x P observations from a Standard
// Multivariate Normal given N observations, a
// vector  of P means, and a P x P cov matrix
// [[Rcpp::export]]
arma::mat rmvnorm(int n,
                  const arma::vec& mu,
                  const arma::mat& Sigma) {
    unsigned int p = Sigma.n_cols;

    // First draw N x P values from a N(0,1)
    Rcpp::NumericVector draw = Rcpp::rnorm(n*p);

    // Instantiate an Armadillo matrix with the
    // drawn values using advanced constructor
    // to reuse allocated memory
    arma::mat Z = arma::mat(draw.begin(), n, p,
                            false, true);

    // Simpler, less performant alternative
    // arma::mat Z = Rcpp::as<arma::mat>(draw);

    // Generate a sample from the Transformed
    // Multivariate Normal
    arma::mat Y = arma::repmat(mu, 1, n).t() +
        Z * arma::chol(Sigma);

    return Y;
}
```

As a result of using a random number generation (RNG), there is an
additional requirement to ensure reproducible results: the necessity
to explicitly set a seed (as shown above). Because of the
(programmatic) interface provided by \rlang to its own RNGs, this setting of the
seed has to occur at the \rlang level
via the `set.seed()` function as no (public) interface is provided by
the \rlang header files.


## Faster linear model fits

As a second example, consider the problem of estimating a common
linear model repeatedly. One use case might be the
simulation of size and power of standard tests. Many users of \rlang would
default to using `lm()`, however, the overhead associated with this function
greatly impacts speed with which an estimate can be obtained. Another approach
would be to take the base \rlang function `lm.fit()`, which is called by `lm()`,
to compute estimated $\hat{\beta}$ in just about the fastest time possible.
However, this approach is also not viable as it does not report the estimated
standard errors. As a result, we cannot use any default
\rlang functions in the context of simulating finite sample population
effects on inference.

One alternative is provided by the `fastLm()` function in
\pkg{RcppArmadillo} \citep{CRAN:RcppArmadillo}.

```{Rcpp fastLm, eval = FALSE}
#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]

// Compute coefficients and their standard error
// during multiple linear regression given a
// design matrix X containing N observations with
// P regressors and a vector y containing of
// N responses
// [[Rcpp::export]]
Rcpp::List fastLm(const arma::mat& X,
                  const arma::colvec& y) {
    // Dimension information
    int n = X.n_rows, p = X.n_cols;
    // Fit model y ~ X
    arma::colvec coef = arma::solve(X, y);
    // Compute the residuals
    arma::colvec res  = y - X*coef;
    // Estimated variance of the random error
    double s2 =
        std::inner_product(res.begin(), res.end(),
                           res.begin(), 0.0)
                           / (n - p);

    // Standard error matrix of coefficients
    arma::colvec std_err = arma::sqrt(s2 *
        arma::diagvec(arma::pinv(X.t()*X)));

    // Create named list with the above quantities
    return Rcpp::List::create(
        Rcpp::Named("coefficients") = coef,
        Rcpp::Named("stderr")       = std_err,
        Rcpp::Named("df.residual")  = n - p    );
}
```

The interface is very simple: a matrix $X_{n \times p}$ of regressors, and a
dependent variable $y_{n \times 1}$ as a vector.  We invoke the standard Armadillo
function `solve()` to fit the model `y ~ X`.\footnote{We should note
that this will use the standard \pkg{LAPACK} functionality via Armadillo whereas R
uses an internal refinement of \pkg{LINPACK} \citep{Dongarra:1979:UGLINPACK} via pivoting,
rendering the operation numerically more stable.  That is an important
robustness aspect---though common datasets on current hardware almost
never lead to actual differences.  That said, if in doubt, stick with
the R implementation.  What is shown here is mostly for exposition of
the principles.}  We then compute residuals, and extract the
(appropriately scaled) diagonal of the covariance matrix, also taking
its square root, in order to return both estimates $\hat{\beta}$ and $\hat{\sigma}$.

# \rcpp in Packages

Once a project containing compiled code has matured to the point of sharing it with
collaborators\footnote{It is sometimes said that every project has two collaborators:
self, and future self. Packaging code is \textsl{best practices} even for code not
intended for public uploading.} or using it within a parallel computing environments, the
ideal way forward is to embed the code within an \rlang package. Not only does an \rlang
package provide a way to automatically compile source code, but also enables the use of
the \rlang help system to document how the written functions should be used. As a further
benefit, the package format enables the use of unit tests to ensure that the functions are
producing the correct output.  Lastly, having a package provides the option of uploading
to a repository such as CRAN for wider dissemination.

To facilitate package building, \rcpp provides a function `Rcpp.package.skeleton()` that
is modeled after the base \rlang function `package.skeleton()`. This function automates
the creation of a skeleton package appropriate for distributing \rcppns:

```{r skeleton, eval = FALSE}
library("Rcpp")
Rcpp.package.skeleton("samplePkg")
```

\begin{figure}
  \begin{center}
    \includegraphics[width=3in]{figures/samplePkg-files-light-bg.png}
    \caption{Graphical annotation of the \texttt{is\_odd\_cpp} function.}
    \label{fig:package-files}
  \end{center}
\end{figure}


This shows how distinct directories `man`, `R`, `src` are created for,
respectively, the help pages, files with \rlang code and files with
\cpp code. Generally speaking, all compiled code, be it from \clangns,
\cpp or \fortran sources, should be placed within the `src/`
directory.

Alternatively, one can achieve similar results to using
`Rcpp.package.skeleton()` by using a feature of the
RStudio IDE. Specifically, while creating a new package project there
is an option to select the type of package by engaging a dropdown menu
to select "Package w/ Rcpp" in RStudio versions prior to v1.1.0. In RStudio
versions later than v1.1.0, support for package templates has been added
allowing users to directly create \rcppns-based packages that use Eigen or Armadillo.

Lastly, one more option exists for users who are familiar with the \pkg{devtools}
\rlang package. To create the \rlang package skeleton
use `devtools::create("samplePkg")`. From here, part of the
structure required by \rcpp can be added by using `devtools::use_rcpp()`.
The remaining aspects needed by \rcpp must be manually copied from
the roxygen tags written to console and pasted into one of the package's \rlang
files to successfully incorporate the dynamic library and link to \rcppns's
headers.

All of these methods take care of a number of small settings one would have to enable
manually otherwise.  These include an 'Imports:' and 'LinkingTo:' declaration in file
DESCRIPTION, as well as 'useDynLib' and 'importFrom' in NAMESPACE. For _Rcpp Attributes_
use, the `compileAttributes()` function has to be called. Similarly, to take advantage of
its documentation-creation feature, the `roxygenize()` function from \pkg{roxygen2} has to
be called.\footnote{The \pkg{littler} package \citep{CRAN:littler} has a helper script `roxy.r`
for this.} Additional details on using \rcpp within a package scope are detailed in
\citet{CRAN:Rcpp:Package}.

# Conclusion

\rlang has always provided mechanisms to extend it.  The bare-bones \clang API is already used
to great effect by a large number of packages.  By taking advantage of a number of \cpp
features, \rcpp has been able to make extending \rlang easier, offering a combination of
both speed _and_ ease of use that has been finding increasingly widespread utilization by
researchers and data scientists.  We are thrilled about this adoption, and look forward to
seeing more exciting extensions to \rlang being built.
