This content is adapted (with permission) from the Rcpp chapter of Hadley Wickham’s book Advanced R.
Introduction
Sometimes R code just isn’t fast enough. You’ve used profiling to figure out where your bottlenecks are, and you’ve done everything you can in R, but your code still isn’t fast enough. In this vignette you’ll learn how to improve performance by rewriting key functions in C++. This magic comes by way of the cpp11 package.
cpp11 makes it very simple to connect C++ to R. While it is possible to write C or Fortran code for use in R, it will be painful by comparison. cpp11 provides a clean, approachable API that lets you write highperformance code, insulated from R’s more complex C API.
Typical bottlenecks that C++ can address include:
Loops that can’t be easily vectorised because subsequent iterations depend on previous ones.
Recursive functions, or problems which involve calling functions millions of times. The overhead of calling a function in C++ is much lower than in R.
Problems that require advanced data structures and algorithms that R doesn’t provide. Through the standard template library (STL), C++ has efficient implementations of many important data structures, from ordered maps to doubleended queues.
The aim of this vignette is to discuss only those aspects of C++ and cpp11 that are absolutely necessary to help you eliminate bottlenecks in your code. We won’t spend much time on advanced features like objectoriented programming or templates because the focus is on writing small, selfcontained functions, not big programs. A working knowledge of C++ is helpful, but not essential. Many good tutorials and references are freely available, including https://www.learncpp.com/ and https://en.cppreference.com/w/cpp. For more advanced topics, the Effective C++ series by Scott Meyers is a popular choice.
Outline
Section intro teaches you how to write C++ by converting simple R functions to their C++ equivalents. You’ll learn how C++ differs from R, and what the key scalar, vector, and matrix classes are called.
Section cpp_source shows you how to use
cpp11::cpp_source()
to load a C++ file from disk in the same way you usesource()
to load a file of R code.Section classes discusses how to modify attributes from cpp11, and mentions some of the other important classes.
Section na teaches you how to work with R’s missing values in C++.
Section stl shows you how to use some of the most important data structures and algorithms from the standard template library, or STL, builtin to C++.
Section casestudies shows two real case studies where cpp11 was used to get considerable performance improvements.
Section package teaches you how to add C++ code to an R package.
Section more concludes the vignette with pointers to more resources to help you learn cpp11 and C++.
Getting started with C++
cpp_function()
allows you to write C++ functions in
R:
cpp_function('int add(int x, int y, int z) {
int sum = x + y + z;
return sum;
}')
# add works like a regular R function
add
#> function (x, y, z)
#> {
#> .Call("_code_1c592eb778aa_add", x, y, z, PACKAGE = "code_1c592eb778aa")
#> }
add(1, 2, 3)
#> [1] 6
When you run the above code, cpp11 will compile the C++ code and construct an R function that connects to the compiled C++ function. There’s a lot going on underneath the hood but cpp11 takes care of all the details so you don’t need to worry about them.
The following sections will teach you the basics by translating simple R functions to their C++ equivalents. We’ll start simple with a function that has no inputs and a scalar output, and then make it progressively more complicated:
 Scalar input and scalar output
 Vector input and scalar output
 Vector input and vector output
 Matrix input and vector output
No inputs, scalar output
Let’s start with a very simple function. It has no arguments and always returns the integer 1:
one < function() 1L
The equivalent C++ function is:
int one() {
return 1;
}
We can compile and use this from R with
cpp_function()
cpp_function('int one() {
return 1;
}')
This small function illustrates a number of important differences between R and C++:
The syntax to create a function looks like the syntax to call a function; you don’t use assignment to create functions as you do in R.
You must declare the type of output the function returns. This function returns an
int
(a scalar integer). The classes for the most common types of R vectors are:doubles
,integers
,strings
, andlogicals
.Scalars and vectors are different. The scalar equivalents of numeric, integer, character, and logical vectors are:
double
,int
,String
, andbool
.You must use an explicit
return
statement to return a value from a function.Every statement is terminated by a
;
.
Scalar input, scalar output
The next example function implements a scalar version of the
sign()
function which returns 1 if the input is positive,
and 1 if it’s negative:
sign_r < function(x) {
if (x > 0) {
1
} else if (x == 0) {
0
} else {
1
}
}
cpp_function('int sign_cpp(int x) {
if (x > 0) {
return 1;
} else if (x == 0) {
return 0;
} else {
return 1;
}
}')
In the C++ version:
We declare the type of each input in the same way we declare the type of the output. While this makes the code a little more verbose, it also makes clear the type of input the function needs.
The
if
syntax is identical — while there are some big differences between R and C++, there are also lots of similarities! C++ also has awhile
statement that works the same way as R’s. As in R you can usebreak
to exit the loop, but to skip one iteration you need to usecontinue
instead ofnext
.
Vector input, scalar output
One big difference between R and C++ is that the cost of loops is
much lower in C++. For example, we could implement the sum
function in R using a loop. If you’ve been programming in R a while,
you’ll probably have a visceral reaction to this function!
sum_r < function(x) {
total < 0
for (i in seq_along(x)) {
total < total + x[i]
}
total
}
In C++, loops have very little overhead, so it’s fine to use them. In
Section stl, you’ll see alternatives to
for
loops that more clearly express your intent; they’re
not faster, but they can make your code easier to understand.
cpp_function('double sum_cpp(doubles x) {
int n = x.size();
double total = 0;
for(int i = 0; i < n; ++i) {
total += x[i];
}
return total;
}')
The C++ version is similar, but:
To find the length of the vector, we use the
.size()
method, which returns an integer. C++ methods are called with.
(i.e., a full stop).The
for
statement has a different syntax:for(init; check; increment)
. This loop is initialised by creating a new variable calledi
with value 0. Before each iteration we check thati < n
, and terminate the loop if it’s not. After each iteration, we increment the value ofi
by one, using the special prefix operator++
which increases the value ofi
by 1.In C++, vector indices start at 0, which means that the last element is at position
n  1
. I’ll say this again because it’s so important: IN C++, VECTOR INDICES START AT 0! This is a very common source of bugs when converting R functions to C++.Use
=
for assignment, not<
.C++ provides operators that modify inplace:
total += x[i]
is equivalent tototal = total + x[i]
. Similar inplace operators are=
,*=
, and/=
.
This is a good example of where C++ is much more efficient than R. As
shown by the following microbenchmark, sum_cpp()
is
competitive with the builtin (and highly optimised) sum()
,
while sum_r()
is several orders of magnitude slower.
x < runif(1e3)
bench::mark(
sum(x),
sum_cpp(x),
sum_r(x)
)[1:6]
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sum(x) 1.1µs 1.5µs 593122. 0B 0
#> 2 sum_cpp(x) 2.3µs 2.7µs 312554. 0B 0
#> 3 sum_r(x) 33.1µs 37.6µs 23742. 35.8KB 0
Vector input, vector output
Next we’ll create a function that computes the Euclidean distance between a value and a vector of values:
pdist_r < function(x, ys) {
sqrt((x  ys) ^ 2)
}
In R, it’s not obvious that we want x
to be a scalar
from the function definition, and we’d need to make that clear in the
documentation. That’s not a problem in the C++ version because we have
to be explicit about types:
cpp_function('doubles pdist_cpp(double x, doubles ys) {
int n = ys.size();
writable::doubles out(n);
for(int i = 0; i < n; ++i) {
out[i] = sqrt(pow(ys[i]  x, 2.0));
}
return out;
}')
This function introduces a few new concepts:
Because we are creating a new vector we need to use
writable::doubles
rather than the readonlydoubles
.We create a new numeric vector of length
n
with a constructor:cpp11::writable::doubles out(n)
. Another useful way of making a vector is to copy an existing one:cpp11::doubles zs(ys)
.C++ uses
pow()
, not^
, for exponentiation.
Note that because the R version is fully vectorised, it’s already going to be fast.
y < runif(1e6)
bench::mark(
pdist_r(0.5, y),
pdist_cpp(0.5, y)
)[1:6]
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 pdist_r(0.5, y) 5.5ms 7.09ms 139. 7.63MB 73.3
#> 2 pdist_cpp(0.5, y) 4.27ms 5.85ms 166. 7.63MB 82.9
On my computer, it takes around 5 ms with a 1 million element
y
vector. The C++ function is about 2.5 times faster, ~2
ms, but assuming it took you 10 minutes to write the C++ function, you’d
need to run it ~200,000 times to make rewriting worthwhile. The reason
why the C++ function is faster is subtle, and relates to memory
management. The R version needs to create an intermediate vector the
same length as y (x  ys
), and allocating memory is an
expensive operation. The C++ function avoids this overhead because it
uses an intermediate scalar.
Using cpp_source
So far, we’ve used inline C++ with cpp_function()
. This
makes presentation simpler, but for real problems, it’s usually easier
to use standalone C++ files and then source them into R using
cpp_source()
. This lets you take advantage of text editor
support for C++ files (e.g., syntax highlighting) as well as making it
easier to identify the line numbers in compilation errors.
Your standalone C++ file should have extension .cpp
,
and needs to start with:
#include "cpp11.hpp"
using namespace cpp11;
And for each function that you want available within R, you need to prefix it with:
[[cpp11::register]]
If you’re familiar with roxygen2, you might wonder how this relates
to @export
. cpp11::register
registers a C++
function to be called from R. @export
controls whether a
function is exported from a package and made available to the user.
To compile the C++ code, use
cpp_source("path/to/file.cpp")
. This will create the
matching R functions and add them to your current session. Note that
these functions can not be saved in a .Rdata
file and
reloaded in a later session; they must be recreated each time you
restart R.
This example also illustrates a different kind of a for
loop, a foreach loop.
#include "cpp11/doubles.hpp"
using namespace cpp11;
[[cpp11::register]]
double mean_cpp(doubles x) {
int n = x.size();
double total = 0;
for(double value : x) {
+= value;
total }
return total / n;
}
NB: if you run this code, you’ll notice that mean_cpp()
is faster than the builtin mean()
. This is because it
trades numerical accuracy for speed.
For the remainder of this vignette C++ code will be presented
standalone rather than wrapped in a call to cpp_function
.
If you want to try compiling and/or modifying the examples you should
paste them into a C++ source file that includes the elements described
above. This is easy to do in RMarkdown by using cpp11
instead of {r}
at the beginning of your code blocks.
Exercises
 With the basics of C++ in hand, it’s now a great time to practice by reading and writing some simple C++ functions. For each of the following functions, read the code and figure out what the corresponding base R function is. You might not understand every part of the code yet, but you should be able to figure out the basics of what the function does.
#include "cpp11.hpp"
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]]
double f1(doubles x) {
int n = x.size();
double y = 0;
for(int i = 0; i < n; ++i) {
+= x[i] / n;
y }
return y;
}
[[cpp11::register]]
(doubles x) {
doubles f2int n = x.size();
::doubles out(n);
writable
[0] = x[0];
outfor(int i = 1; i < n; ++i) {
[i] = out[i  1] + x[i];
out}
return out;
}
[[cpp11::register]]
bool f3(logicals x) {
int n = x.size();
for(int i = 0; i < n; ++i) {
if (x[i]) {
return true;
}
}
return false;
}
[[cpp11::register]]
int f4(cpp11::function pred, list x) {
int n = x.size();
for(int i = 0; i < n; ++i) {
(pred(x[i]));
logicals resif (res[0]) {
return i + 1;
}
}
return 0;
}

To practice your function writing skills, convert the following functions into C++. For now, assume the inputs have no missing values.
Other classes
You’ve already seen the basic vector classes (integers
,
doubles
, logicals
, strings
) and
their scalar (int
, double
, bool
,
string
) equivalents. cpp11 also provides wrappers for other
base data types. The most important are for lists and data frames,
functions, and attributes, as described below.
Lists and data frames
cpp11 also provides list
and data_frame
classes, but they are more useful for output than input. This is because
lists and data frames can contain arbitrary classes but C++ needs to
know their classes in advance. If the list has known structure (e.g.,
it’s an S3 object), you can extract the components and manually convert
them to their C++ equivalents with as_cpp()
. For example,
the object created by lm()
, the function that fits a linear
model, is a list whose components are always of the same type.
The following code illustrates how you might extract the mean
percentage error (mpe()
) of a linear model. This isn’t a
good example of when to use C++, because it’s so easily implemented in
R, but it shows how to work with an important S3 class. Note the use of
Rf_inherits()
and the stop()
to check that the
object really is a linear model.
#include "cpp11.hpp"
using namespace cpp11;
[[cpp11::register]]
double mpe(list mod) {
if (!Rf_inherits(mod, "lm")) {
("Input must be a linear model");
stop}
(mod["residuals"]);
doubles resid(mod["fitted.values"]);
doubles fittedint n = resid.size();
double err = 0;
for(int i = 0; i < n; ++i) {
+= resid[i] / (fitted[i] + resid[i]);
err }
return err / n;
}
mod < lm(mpg ~ wt, data = mtcars)
mpe(mod)
#> [1] 0.01541615
Functions
You can put R functions in an object of type function
.
This makes calling an R function from C++ straightforward. The only
challenge is that we don’t know what type of output the function will
return, so we use the catchall type sexp
. This stands for
SExpression and is used as the type of all R Objects in the internal C
code.
#include "cpp11.hpp"
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]]
(function f) {
sexp call_with_onereturn f(1);
}
call_with_one(function(x) x + 1)
#> [1] 2
call_with_one(paste)
#> [1] "1"
Calling R functions with positional arguments is obvious:
("y", 1); f
But you need a special syntax for named arguments:
using namespace cpp11::literals;
("x"_nm = "y", "value"_nm = 1); f
Attributes
All R objects have attributes, which can be queried and modified with
.attr()
. cpp11 also provides .names()
as an
alias for the names
attribute. The following code snippet
illustrates these methods. Note the use of {}
initializer
list syntax. This allows you to create an R vector from C++ scalar
values:
#include "cpp11.hpp"
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]]
() {
doubles attribs::doubles out = {1., 2., 3.};
writable.names() = {"a", "b", "c"};
out.attr("myattr") = "myvalue";
out.attr("class") = "myclass";
outreturn out;
}
Missing values
If you’re working with missing values, you need to know two things:
How R’s missing values behave in C++’s scalars (e.g.,
double
).How to get and set missing values in vectors (e.g.,
doubles
).
Scalars
The following code explores what happens when you take one of R’s missing values, coerce it into a scalar, and then coerce back to an R vector. Note that this kind of experimentation is a useful way to figure out what any operation does.
#include "cpp11.hpp"
using namespace cpp11;
[[cpp11::register]]
() {
list scalar_missingsint int_s = NA_INTEGER;
= NA_STRING;
r_string chr_s bool lgl_s = NA_LOGICAL;
double num_s = NA_REAL;
return writable::list({as_sexp(int_s), as_sexp(chr_s), as_sexp(lgl_s), as_sexp(num_s)});
}
str(scalar_missings())
#> List of 4
#> $ : int NA
#> $ : chr NA
#> $ : logi TRUE
#> $ : num NA
With the exception of bool
, things look pretty good
here: all of the missing values have been preserved. However, as we’ll
see in the following sections, things are not quite as straightforward
as they seem.
Integers
With integers, missing values are stored as the smallest integer. If
you don’t do anything to them, they’ll be preserved. But, since C++
doesn’t know that the smallest integer has this special behaviour, if
you do anything to it you’re likely to get an incorrect value: for
example, cpp_eval('NA_INTEGER + 1')
gives 2147483647.
So if you want to work with missing values in integers, either use a
length 1 integers
or be very careful with your code.
Doubles
With doubles, you may be able to get away with ignoring missing values and working with NaNs (not a number). This is because R’s NA is a special type of IEEE 754 floating point number NaN. So any logical expression that involves a NaN (or in C++, NAN) always evaluates as FALSE:
cpp_eval("NAN == 1")
#> [1] FALSE
cpp_eval("NAN < 1")
#> [1] FALSE
cpp_eval("NAN > 1")
#> [1] FALSE
cpp_eval("NAN == NAN")
#> [1] FALSE
(Here I’m using cpp_eval()
which allows you to see the
result of running a single C++ expression, making it excellent for this
sort of interactive experimentation.) But be careful when combining them
with Boolean values:
However, in numeric contexts NaNs will propagate NAs:
Strings
String
is a scalar string class introduced by cpp11, so
it knows how to deal with missing values.
Boolean
C++’s bool
has two possible values (true
or
false
), a logical vector in R has three (TRUE
,
FALSE
, and NA
). If you coerce a length 1
logical vector, make sure it doesn’t contain any missing values;
otherwise they will be converted to TRUE. One way to fix this is to use
int
instead, as this can represent TRUE
,
FALSE
, and NA
.
Vectors
With vectors, you need to use a missing value specific to the type of
vector, NA_REAL
, NA_INTEGER
,
NA_LOGICAL
, NA_STRING
:
#include "cpp11.hpp"
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]]
() {
list missing_samplerreturn writable::list({
::doubles({NA_REAL}),
writable::integers({NA_INTEGER}),
writable::logicals({r_bool(NA_LOGICAL)}),
writable::strings({NA_STRING})
writable});
}
str(missing_sampler())
#> List of 4
#> $ : num NA
#> $ : int NA
#> $ : logi NA
#> $ : chr NA
Exercises
Rewrite any of the functions from the first exercise to deal with missing values. If
na_rm
is true, ignore the missing values. Ifna_rm
is false, return a missing value if the input contains any missing values. Some good functions to practice with aremin()
,max()
,range()
,mean()
, andvar()
.Rewrite
cumsum()
anddiff()
so they can handle missing values. Note that these functions have slightly more complicated behaviour.
Standard Template Library
The real strength of C++ is revealed when you need to implement more complex algorithms. The standard template library (STL) provides a set of extremely useful data structures and algorithms. This section will explain some of the most important algorithms and data structures and point you in the right direction to learn more. I can’t teach you everything you need to know about the STL, but hopefully the examples will show you the power of the STL, and persuade you that it’s useful to learn more.
If you need an algorithm or data structure that isn’t implemented in
STL, one place to look is boost. Installing boost on your
computer is beyond the scope of this vignette, but once you have it
installed, you can use boost data structures and algorithms by including
the appropriate header file with (e.g.)
#include <boost/array.hpp>
.
Using iterators
Iterators are used extensively in the STL: many functions either accept or return iterators. They are the next step up from basic loops, abstracting away the details of the underlying data structure. Iterators have three main operators:
 Advance with
++
.  Get the value they refer to, or dereference, with
*
.  Compare with
==
.
For example we could rewrite our sum function using iterators:
#include "cpp11.hpp"
using namespace cpp11;
[[cpp11::register]]
double sum2(doubles x) {
double total = 0;
for(auto it = x.begin(); it != x.end(); ++it) {
+= *it;
total }
return total;
}
The main changes are in the for loop:
We start at
x.begin()
and loop until we get tox.end()
. A small optimization is to store the value of the end iterator so we don’t need to look it up each time. This only saves about 2 ns per iteration, so it’s only important when the calculations in the loop are very simple.Instead of indexing into x, we use the dereference operator to get its current value:
*it
.Notice we use
auto
rather than giving the type of the iterator.
This code can be simplified still further through the use of a C++11 feature: rangebased for loops.
#include "cpp11.hpp"
using namespace cpp11;
[[cpp11::register]]
double sum3(doubles xs) {
double total = 0;
for(auto x : xs) {
+= x;
total }
return total;
}
Iterators also allow us to use the C++ equivalents of the apply
family of functions. For example, we could again rewrite
sum()
to use the accumulate()
function, which
takes a starting and an ending iterator, and adds up all the values in
the vector. The third argument to accumulate
gives the
initial value: it’s particularly important because this also determines
the data type that accumulate
uses (so we use
0.0
and not 0
so that accumulate
uses a double
, not an int
.). To use
accumulate()
we need to include the
<numeric>
header.
#include <numeric>
#include "cpp11.hpp"
using namespace cpp11;
[[cpp11::register]]
double sum4(doubles x) {
return std::accumulate(x.begin(), x.end(), 0.0);
}
Algorithms
The <algorithm>
header provides a large number of
algorithms that work with iterators. A good reference is available at https://en.cppreference.com/w/cpp/algorithm. For
example, we could write a basic cpp11 version of
findInterval()
that takes two arguments, a vector of values
and a vector of breaks, and locates the bin that each x falls into. This
shows off a few more advanced iterator features. Read the code below and
see if you can figure out how it works.
#include <algorithm>
#include "cpp11.hpp"
using namespace cpp11;
[[cpp11::register]] integers findInterval2(doubles x, doubles breaks) {
::integers out(x.size());
writableauto out_it = out.begin();
for (auto&& val : x) {
auto pos = std::upper_bound(breaks.begin(), breaks.end(), val);
*out_it = std::distance(breaks.begin(), pos);
++out_it;
}
return out;
}
The key points are:
We step through two iterators (input and output) simultaneously.
We can assign into an dereferenced iterator (
out_it
) to change the values inout
.upper_bound()
returns an iterator. If we wanted the value of theupper_bound()
we could dereference it; to figure out its location, we use thedistance()
function.
When in doubt, it is generally better to use algorithms from the STL than hand rolled loops. In Effective STL, Scott Meyers gives three reasons: efficiency, correctness, and maintainability. Algorithms from the STL are written by C++ experts to be extremely efficient, and they have been around for a long time so they are well tested. Using standard algorithms also makes the intent of your code more clear, helping to make it more readable and more maintainable.
Data structures
The STL provides a large set of data structures: array
,
bitset
, list
, forward_list
,
map
, multimap
, multiset
,
priority_queue
, queue
, deque
,
set
, stack
, unordered_map
,
unordered_set
, unordered_multimap
,
unordered_multiset
, and vector
. The most
important of these data structures are the vector
, the
unordered_set
, and the unordered_map
. We’ll
focus on these three in this section, but using the others is similar:
they just have different performance tradeoffs. For example, the
deque
(pronounced “deck”) has a very similar interface to
vectors but a different underlying implementation that has different
performance tradeoffs. You may want to try it for your problem. A good
reference for STL data structures is https://en.cppreference.com/w/cpp/container — I
recommend you keep it open while working with the STL.
cpp11 knows how to convert from many STL data structures to their R equivalents, so you can return them from your functions without explicitly converting to R data structures.
Vectors
An STL vector is very similar to an R vector, except that it grows
efficiently. This makes STL vectors appropriate to use when you don’t
know in advance how big the output will be. Vectors are templated, which
means that you need to specify the type of object the vector will
contain when you create it: vector<int>
,
vector<bool>
, vector<double>
,
vector<string>
. You can access individual elements of
a vector using the standard []
notation, and you can add a
new element to the end of the vector using .push_back()
. If
you have some idea in advance how big the vector will be, you can use
.reserve()
to allocate sufficient storage.
The following code implements run length encoding
(rle()
). It produces two vectors of output: a vector of
values, and a vector lengths
giving how many times each
element is repeated. It works by looping through the input vector
x
comparing each value to the previous: if it’s the same,
then it increments the last value in lengths
; if it’s
different, it adds the value to the end of values
, and sets
the corresponding length to 1.
#include "cpp11.hpp"
#include <vector>
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]]
(doubles x) {
list rle_cppstd::vector<int> lengths;
std::vector<double> values;
// Initialise first value
int i = 0;
double prev = x[0];
.push_back(prev);
values.push_back(1);
lengths
for(auto it = x.begin() + 1; it != x.end(); ++it) {
if (prev == *it) {
[i]++;
lengths} else {
.push_back(*it);
values.push_back(1);
lengths++;
i= *it;
prev }
}
return writable::list({
"lengths"_nm = lengths,
"values"_nm = values
});
}
(An alternative implementation would be to replace i
with the iterator lengths.rbegin()
which always points to
the last element of the vector. You might want to try implementing
that.)
Other methods of a vector are described at https://en.cppreference.com/w/cpp/container/vector.
Sets
Sets maintain a unique set of values, and can efficiently tell if
you’ve seen a value before. They are useful for problems that involve
duplicates or unique values (like unique
,
duplicated
, or in
). C++ provides both ordered
(std::set
) and unordered sets
(std::unordered_set
), depending on whether or not order
matters for you. Unordered sets can somtimes be much faster (because
they use a hash table internally rather than a tree). Often even if you
need an ordered set, you could consider using an unordered set and then
sorting the output. Benchmarking with your expected dataset is the best
way to determine which is fastest for your data. Like vectors, sets are
templated, so you need to request the appropriate type of set for your
purpose: unordered_set<int>
,
unordered_set<bool>
, etc. More details are available
at https://en.cppreference.com/w/cpp/container/set and https://en.cppreference.com/w/cpp/container/unordered_set.
The following function uses an unordered set to implement an
equivalent to duplicated()
for integer vectors. Note the
use of seen.insert(x[i]).second
. insert()
returns a pair, the .first
value is an iterator that points
to element and the .second
value is a Boolean that’s true
if the value was a new addition to the set.
#include <unordered_set>
#include "cpp11.hpp"
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]]
(integers x) {
logicals duplicated_cppstd::unordered_set<int> seen;
int n = x.size();
::logicals out(n);
writablefor (int i = 0; i < n; ++i) {
[i] = !seen.insert(x[i]).second;
out}
return out;
}
Exercises
To practice using the STL algorithms and data structures, implement the following using R functions in C++, using the hints provided:
median.default()
usingpartial_sort
.%in%
usingunordered_set
and thefind()
orcount()
methods.unique()
using anunordered_set
(challenge: do it in one line!).which.min()
usingmin_element
, orwhich.max()
usingmax_element
.setdiff()
,union()
, andintersect()
for integers using sorted ranges andset_union
,set_intersection
andset_difference
.
Case studies
The following case studies illustrate some real life uses of C++ to replace slow R code.
Gibbs sampler
The following case study updates an example blogged about by Dirk Eddelbuettel, illustrating the conversion of a Gibbs sampler in R to C++. The R and C++ code shown below is very similar (it only took a few minutes to convert the R version to the C++ version), but runs about 30 times faster on my computer. Dirk’s blog post also shows another way to make it even faster: using the faster random number generator functions in GSL (easily accessible from R through the RcppGSL package) can make it another two to three times faster.
The R code is as follows:
gibbs_r < function(N, thin) {
mat < matrix(nrow = N, ncol = 2)
x < y < 0
for (i in 1:N) {
for (j in 1:thin) {
x < rgamma(1, 3, y * y + 4)
y < rnorm(1, 1 / (x + 1), 1 / sqrt(2 * (x + 1)))
}
mat[i, ] < c(x, y)
}
mat
}
This is relatively straightforward to convert to C++. We:
Add type declarations to all variables.
Use
(
instead of[
to index into the matrix.Include “Rmath.h” and call the functions with
Rf_
.
#include "cpp11/matrix.hpp"
#include "cpp11/doubles.hpp"
#include "Rmath.h"
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]] cpp11::doubles_matrix<> gibbs_cpp(int N, int thin) {
::doubles_matrix<> mat(N, 2);
writabledouble x = 0, y = 0;
for (int i = 0; i < N; i++) {
for (int j = 0; j < thin; j++) {
= Rf_rgamma(3., 1. / double(y * y + 4));
x = Rf_rnorm(1. / (x + 1.), 1. / sqrt(2. * (x + 1.)));
y }
(i, 0) = x;
mat(i, 1) = y;
mat}
return mat;
}
Benchmarking the two implementations yields a significant speedup for running the loops in C++:
R vectorisation versus C++ vectorisation
This example is adapted from “Rcpp is smoking fast for agentbased models in data frames”. The challenge is to predict a model response from three inputs. The basic R version of the predictor looks like:
vacc1a < function(age, female, ily) {
p < 0.25 + 0.3 * 1 / (1  exp(0.04 * age)) + 0.1 * ily
p < p * if (female) 1.25 else 0.75
p < max(0, p)
p < min(1, p)
p
}
We want to be able to apply this function to many inputs, so we might write a vectorinput version using a for loop.
vacc1 < function(age, female, ily) {
n < length(age)
out < numeric(n)
for (i in seq_len(n)) {
out[i] < vacc1a(age[i], female[i], ily[i])
}
out
}
If you’re familiar with R, you’ll have a gut feeling that this will
be slow, and indeed it is. There are two ways we could attack this
problem. If you have a good R vocabulary, you might immediately see how
to vectorise the function (using ifelse()
,
pmin()
, and pmax()
). Alternatively, we could
rewrite vacc1a()
and vacc1()
in C++, using our
knowledge that loops and function calls have much lower overhead in
C++.
Either approach is fairly straightforward. In R:
vacc2 < function(age, female, ily) {
p < 0.25 + 0.3 * 1 / (1  exp(0.04 * age)) + 0.1 * ily
p < p * ifelse(female, 1.25, 0.75)
p < pmax(0, p)
p < pmin(1, p)
p
}
(If you’ve worked R a lot you might recognise some potential
bottlenecks in this code: ifelse
, pmin
, and
pmax
are known to be slow, and could be replaced with
p * 0.75 + p * 0.5 * female
,
p[p < 0] < 0
, p[p > 1] < 1
. You
might want to try timing those variations.)
Or in C++:
#include "cpp11.hpp"
using namespace cpp11;
namespace writable = cpp11::writable;
[[cpp11::register]]
double vacc3a(double age, bool female, bool ily){
double p = 0.25 + 0.3 * 1 / (1  exp(0.04 * age)) + 0.1 * ily;
= p * (female ? 1.25 : 0.75);
p = std::max(p, 0.0);
p = std::min(p, 1.0);
p return p;
}
[[cpp11::register]]
(doubles age, logicals female,
doubles vacc3) {
logicals ilyint n = age.size();
::doubles out(n);
writablefor(int i = 0; i < n; ++i) {
[i] = vacc3a(age[i], female[i], ily[i]);
out}
return out;
}
We next generate some sample data, and check that all three versions return the same values:
n < 1000
age < rnorm(n, mean = 50, sd = 10)
female < sample(c(T, F), n, rep = TRUE)
ily < sample(c(T, F), n, prob = c(0.8, 0.2), rep = TRUE)
stopifnot(
all.equal(vacc1(age, female, ily), vacc2(age, female, ily)),
all.equal(vacc1(age, female, ily), vacc3(age, female, ily))
)
The original blog post forgot to do this, and introduced a bug in the
C++ version: it used 0.004
instead of 0.04
.
Finally, we can benchmark our three approaches:
bench::mark(
vacc1 = vacc1(age, female, ily),
vacc2 = vacc2(age, female, ily),
vacc3 = vacc3(age, female, ily)
)
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 vacc1 1.77ms 2.26ms 439. 7.86KB 39.5
#> 2 vacc2 60µs 74.6µs 11894. 148.84KB 34.7
#> 3 vacc3 15.9µs 18.4µs 48346. 14.03KB 4.84
Not surprisingly, our original approach with loops is very slow. Vectorising in R gives a huge speedup, and we can eke out even more performance (about ten times) with the C++ loop. I was a little surprised that the C++ was so much faster, but it is because the R version has to create 11 vectors to store intermediate results, where the C++ code only needs to create 1.
Using cpp11 in a package
The same C++ code that is used with cpp_source()
can
also be bundled into a package. There are several benefits of moving
code from a standalone C++ source file to a package:
Your code can be made available to users without C++ development tools.
Multiple source files and their dependencies are handled automatically by the R package build system.
Packages provide additional infrastructure for testing, documentation, and consistency.
To add cpp11
to an existing package first put your C++
files in the src/
directory of your package. Then add the
following to your DESCRIPTION
file:
```
LinkingTo: cpp11
```
and add the following roxygen directive somewhere in
your package’s R files. (A common location is
R/pkgnamepackage.R
)
```
#' @useDynLib pkgname, .registration = TRUE
```
You’ll then need to run devtools::document()
to update your NAMESPACE
file to include the
useDynLib
statement.
The easiest way to set this up is to call
usethis::use_cpp11()
, which will do the above steps for
your automatically.
Before building the package, you’ll need to run
cpp11::cpp_register()
. This function scans the C++ files
for [[cpp11::register]]
attributes and generates the
binding code required to make the functions available in R. Rerun
cpp11::cpp_register()
whenever functions are added,
removed, or have their signatures changed. If you are using
devtools
to develop your package this is done automatically
by the pkgbuild package when your package has
LinkingTo: cpp11
in its DESCRIPTION file.
Learning more
C++ is a large, complex language that takes years to master. If you would like to dive deeper or write more complex functions other resources I’ve found helpful in learning C++ are:
C++ Annotations, aimed at knowledgeable users of C (or any other language using a Clike grammar, like Perl or Java) who would like to know more about, or make the transition to, C++.
Algorithm Libraries, which provides a more technical, but still concise, description of important STL concepts. (Follow the links under notes.)
Writing performant code may also require you to rethink your basic approach: a solid understanding of basic data structures and algorithms is very helpful here. That’s beyond the scope of this vignette, but I’d suggest the Algorithm Design Manual MIT’s Introduction to Algorithms, Algorithms by Robert Sedgewick and Kevin Wayne which has a free online textbook and a matching Coursera course.