Getting started with cppally

Below we explore some of the capabilities of cppally, from its custom C++ scalar and vectors, to using templates and concepts.

Note: The classes r_vec and r_vector are aliases of one another and thus can be used interchangeably.

Setup

Let’s start by loading cppally

library(cppally)

Registering C++ functions (to R)

To make a C++ function available to R we use the [[cppally::register]] tag.

#include <cppally.hpp>
using namespace cppally;

[[cppally::register]]
void hello_world(){
  print("Hello World!");
}

After tagging our functions we want to make them available to R. To do that we have a few routes.

Registering C++ functions outside of a package context

After writing our hello world program in foo.cpp we can use cpp_source() to compile and register the function to R.

cpp_source(file = "src/foo.cpp")

Now the function is available in R

hello_world()
#> Hello World!

Similarly we can use the helper cpp_eval to run simple expressions and return the result without needing to include cppally.hpp and register the function.

cpp_eval('print("Hello World Again!")')
#> Hello World Again!

Note - For the rest of the examples it is assumed that the following code is always included beforehand.

#include <cppally.hpp>
using namespace cppally;

Registering C++ functions inside a cppally-linked package

Since cppally is header-only, we can include the headers directly into our own package.

General steps to using cppally in a package

Create package (if you haven’t already done so) using usethis::create_tidy_package()
Run cppally::use_cppally()
Run cppally::document()

This will automatically add the necessary package content needed to start working with cppally. For continuous development, use cppally::load_all() to compile and register cppally tagged functions, including our hello world function.

Note: We aim to integrate cppally registration into the devtools framework for ease-of-use.

C++ types

cppally offers a rich set of R types in C++ that are NA-aware. This means that common arithmetic and logical operations will account for NA in a similar fashion to R.

Scalars

logical scalar - `r_lgl`

cppally’s scalar version of logical, r_lgl can represent true, false or NA.

r_true
r_false
r_na

#> [1] TRUE
#> [1] FALSE
#> [1] NA

Logical operators work just like in R


[[cppally::register]]
r_vec<r_lgl> lgl_ops(){
  return make_vec<r_lgl>(
    r_true || r_false, // true
    r_true && r_false, // false
    r_na || r_true,    // true
    r_na && r_true,    // NA
    r_na && r_false,   // false
    r_na || r_na,      // NA
    r_na && r_na      // NA
  );
}

lgl_ops()
#> [1]  TRUE FALSE  TRUE    NA FALSE    NA    NA

Using r_lgl in if-statements

For type-safety reasons r_lgl cannot be implicitly converted to bool except in if-statements where an error is thrown if the value is NA.

DON’T do this:


[[cppally::register]]
void bad_lgl_print(r_lgl condition){
  if (condition){
    print("true");
  } else {
    print("false");
  }
}

bad_lgl_print(TRUE)
#> true
bad_lgl_print(FALSE)
#> false
bad_lgl_print(NA) # Can't implicitly convert NA to bool
#> Error:
#> ! Cannot implicitly convert r_lgl NA to bool, please check

DO this:


[[cppally::register]]
void good_lgl_print(r_lgl condition){
  if (is_na(condition)){
    print("NA");
  } else if (condition){
    print("true");
  } else {
    print("false");
  }
}

good_lgl_print(TRUE)
#> true
good_lgl_print(FALSE)
#> false
good_lgl_print(NA) # NA is handled explicitly so no issues
#> NA

We can also use r_lgl members is_true() and is_false() which return bool and are equivalent to R’s isTRUE() and isFALSE()


[[cppally::register]]
void also_good_lgl_print(r_lgl condition){
  if (condition.is_true()){
    print("true");
  } else {
    print("not true");
  }
}

also_good_lgl_print(TRUE)
#> true
also_good_lgl_print(FALSE)
#> not true
also_good_lgl_print(NA) # Falls into 'not true' branch here as expected
#> not true

Important: The && and || operators for r_lgl do NOT short-circuit like they do for bool. Both sides of the expression are always evaluated. If you specifically require short-circuiting behaviour, use is_true() and is_false() before using && and ||.

All cppally scalar types are implemented as structs that contain the underlying C/C++ types as well as other member functions.

cppally type	Description	Built on
`r_lgl`	Scalar logical	`int`
`r_int`	Scalar integer	`int`
`r_int64`	Scalar 64-bit integer	`int64_t`
`r_dbl`	Scalar double	`double`
`r_str`	Scalar string	`r_sexp`
`r_str_view`	Scalar string (view)	`SEXP`
`r_cplx`	Scalar double complex	`std::complex<double>`
`r_raw`	Scalar raw	`unsigned char`
`r_sym`	Symbol	`SEXP`
`r_date` ¹	Scalar date	`r_dbl`
`r_psxct`	Scalar date-time	`r_dbl`
`r_sexp`	Generic R object (SEXP)²	`SEXP`

NA values

Use is_na() to check that a value is NA and na<T>() to generate NA values.

is_na(na<r_int>())

#> [1] TRUE

C++ NA values and their R C API equivalents

Type	Value	R C API Value
`r_lgl`	`na<r_lgl>()`/`r_na`	`NA_LOGICAL`
`r_int`	`na<r_int>()`	`NA_INTEGER`
`r_int64`	`na<r_int64>()`	Not applicable
`r_dbl`	`na<r_dbl>()`	`NA_REAL`
`r_str`	`na<r_str>()`	`NA_STRING`
`r_cplx`	`na<r_cplx>()`	Not applicable
`r_sym`	Not applicable	Not applicable
`r_sexp`³	`na<r_sexp>()`/`r_null`	`R_NilValue`

Checking equality

There are two ways to check for exact equality of cppally scalars - with the == operator or with identical().

The cppally == operator always returns r_lgl and identical() always returns bool, which is a particularly important distinction when dealing with NA values as the former can represent NA while the latter cannot.


[[cppally::register]]
void cppally_equality(){

  r_int x = na<r_int>();
  r_int y = na<r_int>();
  
  r_lgl x_equal_to_y = x == y;
  bool x_identical_to_y = identical(x, y);
  
  // NA so not printed
  if ( x_equal_to_y.is_true() ){
    print("x is equal to y\n");
  }
  
  // NA so not printed
  if ( x_equal_to_y.is_false() ){
    print("x is not equal to y\n");
  }
  
  // NA so printed
  if (is_na(x_equal_to_y)){
    print("`x == y` produces `NA`\n");
  }
  
  // Both na<r_int>() therefore they are identical to each other
  if (x_identical_to_y){
    print("x is identical to y\n");
  }
}

cppally_equality()
#> `x == y` produces `NA`
#> x is identical to y

identical() can not only compare scalars, but also vectors, lists, factors, and data frames.


template <typename T, typename U>
[[cppally::register]]
bool cpp_identical(T x, U y){
  return identical(x, y);
}

cpp_identical(3L, 3L)
#> [1] TRUE
cpp_identical(NA, NA)
#> [1] TRUE
cpp_identical(3L, 3) # int != double
#> [1] FALSE
cpp_identical(1:10, 1:10)
#> [1] TRUE
cpp_identical(list(1, 2, 3), list(3, 2, 1))
#> [1] FALSE
cpp_identical(iris, iris)
#> [1] TRUE

Scalar operators

cppally also defines arithmetic and relational comparison operators for its scalar types. Like the logical and equality operators seen earlier, they are all NA-aware.

Scalar arithmetic operators

Addition

r_int(0) + r_dbl(2.5)

#> [1] 2.5

Subtraction

r_int(0) - r_int(1)

#> [1] -1

Multiplication

r_int(2) * r_int(3)

#> [1] 6

Division

r_dbl(9) / 3

#> [1] 3

Addition with NA

na<r_int>() + r_dbl(2.5)

#> [1] NA

Subtraction with NA

na<r_int>() - r_int(1)

#> [1] NA

Multiplication with NA

na<r_int>() * r_int(3)

#> [1] NA

Division with NA

na<r_dbl>() / 3

#> [1] NA

Scalar relational operators

Less than

r_int(1) < r_int(2)

#> [1] TRUE

Less than or equal to

r_dbl(2) <= r_dbl(2)

#> [1] TRUE

Greater than

r_int(3) > r_int(2)

#> [1] TRUE

Greater than or equal to

r_dbl(2) >= r_dbl(3)

#> [1] FALSE

Less than with NA

na<r_int>() < r_int(2)

#> [1] NA

Less than or equal to with NA

na<r_dbl>() <= r_dbl(2)

#> [1] NA

Greater than with NA

na<r_int>() > r_int(2)

#> [1] NA

Greater than or equal to with NA

na<r_dbl>() >= r_dbl(3)

#> [1] NA

Other defined operators not showcased: ++, --, +=, -=, *=, /=, %=, -, %, |, &, !

Vectors

cppally vectors are templated and can be thought of as containers of scalar elements like r_int, r_dbl, etc.

We can create vectors like so


// Integer vector of size n
[[cppally::register]]
r_vec<r_int> new_integer_vector(int n){
  r_vec<r_int> int_vctr(n, /*fill = */ r_int(0));
  return int_vctr;
}

new_integer_vector(3)
#> [1] 0 0 0

inline vectors

To create inline vectors, use make_vec<>

make_vec<r_dbl>(1, 1.5, 2, na<r_dbl>())

#> [1] 1.0 1.5 2.0  NA

We can add names on the fly with arg()


make_vec<r_dbl>(
    arg("first") = 1,
    arg("second") = 1.5,
    arg("third") = 2,
    arg("last") = na<r_dbl>()
  )

#>  first second  third   last 
#>    1.0    1.5    2.0     NA

In R a list is a generic vector, so cppally defines lists as r_vec<r_sexp>, a vector of the generic type r_sexp.

make_vec<r_sexp>(1, 2, 3)

#> [[1]]
#> [1] 1
#> 
#> [[2]]
#> [1] 2
#> 
#> [[3]]
#> [1] 3

A list of all cppally vectors of length 0


[[cppally::register]]
r_vec<r_sexp> all_vectors(){
  return make_vec<r_sexp>(
    arg("logical") = r_vec<r_lgl>(),
    arg("integer") = r_vec<r_int>(),
    arg("integer64") = r_vec<r_int64>(), // Requires bit64
    arg("double") = r_vec<r_dbl>(),
    arg("character") = r_vec<r_str>(),
    arg("character") = r_vec<r_str_view>(),
    arg("raw") = r_vec<r_raw>(),
    arg("date") = r_vec<r_date>(),
    arg("date-time") = r_vec<r_psxct>(),
    arg("list") = r_vec<r_sexp>()
  );
}

all_vectors()
#> $logical
#> logical(0)
#> 
#> $integer
#> integer(0)
#> 
#> $integer64
#> integer64(0)
#> 
#> $double
#> numeric(0)
#> 
#> $character
#> character(0)
#> 
#> $character
#> character(0)
#> 
#> $raw
#> raw(0)
#> 
#> $date
#> Date of length 0
#> 
#> $`date-time`
#> POSIXct of length 0
#> 
#> $list
#> list()

Scalar math

There is a rich suite of math functions that accept cppally types.


[[cppally::register]]
r_vector<r_dbl> cppally_math(r_dbl x){
    return make_vec<r_dbl>(
        arg("abs")          = abs(x),
        arg("floor")        = floor(x),
        arg("ceiling")      = ceiling(x),
        arg("trunc")        = trunc(x),
        arg("round")        = round(x),
        arg("signif")       = signif(x, 3),
        arg("sign")         = sign(x),
        arg("min")          = min(0, x),
        arg("max")          = max(0, x),
        arg("sqrt")         = sqrt(x),
        arg("pow")          = pow(x, 2),
        arg("exp")          = exp(x),
        arg("log")          = log(x),
        arg("log_base")     = log(x, 2),
        arg("log10")        = log10(x)
    );
}

cppally_math(2.5)
#>        abs      floor    ceiling      trunc      round     signif       sign 
#>  2.5000000  2.0000000  3.0000000  2.0000000  2.0000000  2.5000000  1.0000000 
#>        min        max       sqrt        pow        exp        log   log_base 
#>  0.0000000  2.5000000  1.5811388  6.2500000 12.1824940  0.9162907  1.3219281 
#>      log10 
#>  0.3979400
cppally_math(NA)
#>      abs    floor  ceiling    trunc    round   signif     sign      min 
#>       NA       NA       NA       NA       NA       NA       NA       NA 
#>      max     sqrt      pow      exp      log log_base    log10 
#>       NA       NA       NA       NA       NA       NA       NA

Coercion

To coerce from one scalar to another we can use as<T>


[[cppally::register]]
r_int double_to_int(r_dbl x){
  return as<r_int>(x);
}

double_to_int(pi)
#> [1] 3
double_to_int(NA_real_)
#> [1] NA

We can also coerce from one vector type to another


[[cppally::register]]
r_vec<r_int> to_int_vec(r_vec<r_dbl> x){
  return as<r_vec<r_int>>(x);
}

to_int_vec(c(0, 1.5, NA))
#> [1]  0  1 NA

Since as<T> is extremely flexible, we can also coerce from a scalar to a vector or vice versa


[[cppally::register]]
r_vec<r_sexp> coercions(){
    r_dbl a(4.2);
    r_vec<r_dbl> b = make_vec<r_dbl>(2.5);
    return make_vec<r_sexp>(
        as<r_vec<r_int>>(a),
        as<r_int>(a),
        as<r_int>(b),
        as<r_dbl>(b)
    );
}

coercions()
#> [[1]]
#> [1] 4
#> 
#> [[2]]
#> [1] 4
#> 
#> [[3]]
#> [1] 2
#> 
#> [[4]]
#> [1] 2.5

We can even coerce to and from C++ vectors


[[cppally::register]]
r_vector<r_int> cpp_vectors_example(r_vector<r_int> x){
  std::vector x_cpp = as<std::vector<r_int>>(x);
  x_cpp.push_back(r_int(42));
  return as<r_vector<r_int>>(x_cpp);
}

cpp_vectors_example(41L)
#> [1] 41 42

While coercing to a std::vector just to push back an element before coercing back might not be the most efficient, it does showcase how easy it is to work with cppally vectors and C++ vectors.

Strings

cppally provides the useful string type r_str

We can create R strings easily

r_str("hello")

#> [1] "hello"

To get a C or C++ string, use the members c_str() and cpp_str() respectively

C string via c_str()

r_str("hello").c_str()

#> [1] "hello"

C++ string_view via cpp_str()

This can be converted into a std::string via its constructor


[[cppally::register]]
r_str str_concatenate(r_str x, r_str y, r_str sep){
  std::string left = std::string(x.cpp_str());
  std::string right = std::string(y.cpp_str());
  std::string middle = std::string(sep.cpp_str());
  std::string combined = left + middle + right;
  return r_str(combined.c_str());
}

str_concatenate("hello", "how are you?", sep = ", ")
#> [1] "hello, how are you?"

Symbols

Symbols have class r_sym and can be created directly from a string literal

r_sym("new_symbol")

#> new_symbol

Or from a cppally string

r_sym(r_str("symbol_from_string"))

#> symbol_from_string

Cached strings & symbols

cppally provides an efficient caching strategy for constructing cppally strings/symbols from string literals

cached_str<>

cached_str<"cached_string">()

#> [1] "cached_string"

This initialises the string once, caches it (to R’s CHARSXP pool), and efficiently re-uses the cached string for each subsequent call.

We can cache symbols in a similar way

cached_sym<"cached_symbol">()

#> cached_symbol

Lists

r_sexp is generally interpreted as an “element of a list” since lists are defined as r_vec<r_sexp>, a vector that holds generic r_sexp elements.


using list = r_vec<r_sexp>;

[[cppally::register]]
list new_list(int n){
  return list(n);
}

new_list(0)
#> list()
new_list(3)
#> [[1]]
#> NULL
#> 
#> [[2]]
#> NULL
#> 
#> [[3]]
#> NULL

The problem with a class like r_sexp is that it is by design generic and therefore difficult to work with in C++. To disambiguate the actual type we can use r_sexp_visit() via a C++ lambda.

Example: using r_sexp_visit() to resize every vector to length n in-place


[[cppally::register]]
r_vec<r_sexp> resize_all(r_vec<r_sexp> x, r_size_t n){
    r_size_t list_length = x.length();
    for (r_size_t i = 0; i < list_length; ++i){
        r_sexp_visit(x.view(i), [&]<RVector T>(T vec) {
            x.set(i, vec.resize(n));
        });
    }
    return x;
}

Factors

We can create a factor via r_factors()


[[cppally::register]]
r_factors new_factor(r_vec<r_str> x){
    return r_factors(x);
}

new_factor(letters)
#>  [1] a b c d e f g h i j k l m n o p q r s t u v w x y z
#> Levels: a b c d e f g h i j k l m n o p q r s t u v w x y z

In cppally, like R, factors are not vectors and therefore do not satisfy the RVector concept. To access the underlying integer codes vector, use the public codes() member function


static_assert(!RVector<r_factors>);

[[cppally::register]]
r_vec<r_int> factor_codes(r_factors x){
    return x.codes();
}

letter_fct <- new_factor(letters)

letter_fct |>
    factor_codes()
#>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26

Value matching

Use r_vec member find() to find the 0-indexed locations of a scalar value.


[[cppally::register]]
r_vector<r_int> find_empty_string(r_vector<r_str> x){
    return x.find(r_str(""));
}

x <- c("zero", "one", "two", "three", "four")

find_empty_string(x)
#> integer(0)

# Add empty strings
x[c(1, 3)] <- ""

find_empty_string(x)
#> [1] 0 2

To find the locations of multiple values (first match), use match(). It works like R’s base::match() but is 0-indexed.


[[cppally::register]]
r_vector<r_int> match_strs(r_vector<r_str> x, r_vector<r_str> table){
    return match(x, table);
}

letters
#>  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
#> [20] "t" "u" "v" "w" "x" "y" "z"

vowels <- c("a", "e", "i", "o", "u")

# cppally::match is 0-indexed
match_strs(vowels, letters)
#> [1]  0  4  8 14 20

cppally provides the IS_IN infix operator, identical to R’s %in%


[[cppally::register]]
r_vector<r_lgl> cpp_in(r_vector<r_str> x, r_vector<r_str> table){
    return x IS_IN table;
}

cpp_in(c("a", "A", NA), letters)
#> [1]  TRUE FALSE FALSE

To mimic R’s new %notin% operator, simply use the logical negation operator.


[[cppally::register]]
r_vector<r_lgl> cpp_not_in(r_vector<r_str> x, r_vector<r_str> table){
    return !(x IS_IN table);
}

Technical note: cppally internally negates the intermediate result of x IS_IN table in-place in this particular case because it satisfies specific properties of exclusivity which while not covered here, may be covered in a later vignette. This in-place negation is naturally efficient as it avoids allocating a new vector.

cpp_not_in(c("a", "A", NA), letters)
#> [1] FALSE  TRUE  TRUE

Subsetting

Subsetting is 0-indexed in cppally, as is all other indexing.


template <RVector T, typename subscript_t>
requires any<subscript_t, r_lgl, r_int, r_str>
[[cppally::register]]
T cpp_subset(T x, r_vector<subscript_t> y){
    return subset(x, y);
}

Subsetting with integer indices

x <- 1:10
cpp_subset(x, 0L) # index 0 is 1st value
#> [1] 1
cpp_subset(x, 9L) # index 9 is last value here
#> [1] 10
cpp_subset(x, 10L) # index 10 is out-of-bounds so NA is returned
#> [1] NA
cpp_subset(x, NA_integer_) # NA is returned with integer NA index
#> [1] NA

Subsetting with logical indices

cpp_subset(x, x > 5)
#> [1]  6  7  8  9 10
cpp_subset(x, x > 100)
#> integer(0)

# It differs to base subsetting (via `[`)
cpp_subset(x, rep(NA, 10)) # cppally only returns values associated with TRUE
#> integer(0)
x[rep(NA, 10)] # base R returns NA values when subsetting with NA logicals
#>  [1] NA NA NA NA NA NA NA NA NA NA

base R performs negative subsetting by using negative numbers. This is not possible with our 0-indexed subset as we would have to also represent negative zero to subset everything except the first element at index 0. Instead cppally has an invert argument.


template <RVector T, typename subscript_t>
requires any<subscript_t, r_lgl, r_int, r_str>
[[cppally::register]]
T cpp_negative_subset(T x, r_vector<subscript_t> y){
    return subset(x, y, /*invert=*/ true);
}

cpp_negative_subset(x, 0L) # Everything but 1st
#> [1]  2  3  4  5  6  7  8  9 10
cpp_negative_subset(x, x > 5) # Everything except where x > 5
#> [1] 1 2 3 4 5
cpp_negative_subset(x, integer()) # Everything
#>  [1]  1  2  3  4  5  6  7  8  9 10

Named subsetting is also supported. cppally internally hashes the vector names and performs hash lookups. For more info, see the ‘Vector Names Hashing’ vignette.

names(x) <- LETTERS[seq_along(x)]

cpp_subset(x, c("A", "J", "Z"))
#>    A    J <NA> 
#>    1   10   NA
cpp_negative_subset(x, "A")
#>  B  C  D  E  F  G  H  I  J 
#>  2  3  4  5  6  7  8  9 10

Concepts and Templates

One of the most powerful features of C++20 are concepts. These allow users to write human-readable templates and constraints.

When writing your own templates, it is necessary to place them in headers for cppally registration to work correctly.

Let’s practice by creating the abs() function in C++ using templates and the RMathType concept.


template <RMathType T>
[[cppally::register]]
T cpp_abs(T x){
  if (is_na(x)){
    return na<T>();
  } else if (x < 0){
    return -x;
  } else {
    return x;
  }
}

What’s nice is that it works correctly for integers and doubles while simultaneously preserving their type

cpp_abs(-4.2)
#> [1] 4.2
cpp_abs(-3L)
#> [1] 3

class(cpp_abs(-4.2)) # Double preserved
#> [1] "numeric"
class(cpp_abs(-3L)) # Integer preserved
#> [1] "integer"

This type of programming is historically tricky within the R C API and typically necessitates a switch statement that switches on the object’s type, handling each type separately. With our abs() template, the logic is correctly handled with one set of operations.

How it works

The top-line template <RMathType T> declares a template that encapsulates T, an RMathType - a concept that contains r_lgl, r_int, r_int64 and r_dbl

If x is NA then we immediately also return NA via na<T>() which is a templated function that returns NA of the input type T.

To correctly register templates, the ‘[[cppally::register]]’ tag must always go above the function name.

template <typename T>
[[cppally::register]] // <--- Here
T foo(T x){
  return x;
}

Templates without function arguments

Explicit instantiation (from R) is unfortunately not possible and template types must be deduced from supplied arguments.

template <typename T>
[[cppally::register]]
T foo(){
    return T();
}

Here foo() will not be compiled because the function has no arguments that let the compiler automatically deduce what T is. In C++ you would always call this function like so: foo<T>(). Unfortunately we can’t do that from R directly.

You may get a cryptic compiler error like this

error: no matching function for call to 'foo()'
[]<typename T>() -> decltype(cpp_to_r(::foo())) {

along with an equally cryptic note

note:   couldn't deduce template parameter 'T'
[]<typename T>() -> decltype(cpp_to_r(::foo())) {

Even though these kinds of templates can be written with cppally in C++, they cannot be exported to R.

An obvious and somewhat ugly workaround is to include a prototype argument that allows the template parameter to be deduced from.


// Return the default constructor result of RScalar types

template <RScalar T>
[[cppally::register]]
T scalar_default(T ptype){
    return T();
}

scalar_default(integer(1)) # Default is 0L
#> [1] 0
scalar_default(numeric(1)) # Default is 0.0
#> [1] 0
scalar_default(character(1)) # Default is ""
#> [1] ""

Exporting variadic templates are also not supported. The best alternative is to use lists (r_vec<r_sexp>).

In the above example we used the RScalar concept which includes all cppally scalar types (excluding r_sexp). For a list of all cppally concepts, please see the Annex

Attributes

Attributes can be manipulated via functions defined in the attr namespace.

Example: Adding names to a list


[[cppally::register]]
r_vec<r_sexp> set_list_names(r_vec<r_sexp> x, r_vec<r_str> names){
  x.set_names(names);
  return x;
}

set.seed(42)
norm_samples <- lapply(1:5, \(x) rnorm(10, mean = x))
set_list_names(norm_samples, paste0("sample_", 1:5))
#> $sample_1
#>  [1] 2.3709584 0.4353018 1.3631284 1.6328626 1.4042683 0.8938755 2.5115220
#>  [8] 0.9053410 3.0184237 0.9372859
#> 
#> $sample_2
#>  [1]  3.3048697  4.2866454  0.6111393  1.7212112  1.8666787  2.6359504
#>  [7]  1.7157471 -0.6564554 -0.4404669  3.3201133
#> 
#> $sample_3
#>  [1] 2.693361 1.218692 2.828083 4.214675 4.895193 2.569531 2.742731 1.236837
#>  [9] 3.460097 2.360005
#> 
#> $sample_4
#>  [1] 4.455450 4.704837 5.035104 3.391074 4.504955 2.282991 3.215541 3.149092
#>  [9] 1.585792 4.036123
#> 
#> $sample_5
#>  [1] 5.205999 4.638943 5.758163 4.273295 3.631719 5.432818 4.188607 6.444101
#>  [9] 4.568554 5.655648

More useful attribute helpers

get_attrs() - Returns a list of attributes (possibly r_vec<r_sexp>(r_null))
set_attrs() - Sets attributes to ones specified. Note: replaces any current attributes
clear_attrs() - Removes all attributes
set_attr() - Set a single attribute
get_attr() - Get a single attribute
inherits1() - Does object inherit class?
inherits_any() - Does object inherit at least one of the specified classes?
inherits_all() - Does object inherit all of the specified classes?
modify_attrs() - Modifies current attributes but doesn’t remove any existing ones

Regular sequences

There are two core functions for generating regular sequences - sequence() and seq(). seq() behaves exactly like the R equivalent base::seq(), and sequence() behaves like the R equivalent base::sequence(), with the exception that it accepts scalar arguments instead of vector ones.

seq(r_dbl(1), r_dbl(5), r_dbl(0.5))

#> [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

sequence(5, /*from = */ r_int(0), /*by = */ r_int(-1))

#> [1]  0 -1 -2 -3 -4

We can also use sequence() to easily replicate base::seq_len()


[[cppally::register]]
r_vec<r_int> cpp_seq_len(r_size_t n){
  return sequence(n, /* from = */ r_int(1), /* by = */ r_int(1));
}

cpp_seq_len(5)
#> [1] 1 2 3 4 5

It is also straightforward to replicate base::sequence() with pmap()


template <typename T>
requires (any<T, r_int, r_int64, r_dbl>)
[[cppally::register]]
r_vec<r_sexp> cpp_sequences(r_vec<r_int> size, r_vec<T> from, r_vec<T> by){
    return pmap([](auto a, auto b, auto c){
        return as<r_sexp>(sequence(a, b, c));
    }, size, from, by);
}

cpp_sequences(1:3, from = 0L, by = 1L) |> 
  unlist()
#> [1] 0 0 1 0 1 2

# Same as base R
sequence(1:3, from = 0L, by = 1L)
#> [1] 0 0 1 0 1 2

Sugar functions

cppally also offers many useful and high-performance common functions in cppally/sugar

Example: n_unique() - fast calculation of number of unique values.


template <RVector T>
[[cppally::register]]
r_int cpp_n_unique(T x){
  return as<r_int>(n_unique(x));
}

library(bench)
x <- sample(1:100, 10^5, replace = TRUE)
mark(
  base_n_unique = length(unique(x)),
  cppally_n_unique = cpp_n_unique(x)
)
#> # A tibble: 2 × 6
#>   expression            min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>       <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 base_n_unique       961µs   2.26ms      441.    1.38MB     11.6
#> 2 cppally_n_unique    260µs  750.1µs     1386.        0B      0

More useful sugar functions

unique() - Like R’s unique() but with a sort argument to return sorted unique values
order() - Like base R’s order but it internally uses a hybrid approach of ska sort, count sorting, quick sort, etc.
make_groups() - An advanced function that returns a struct containing group IDs and number of groups (i.e number of unique group IDs). The groups struct contains the following members:
- r_vec<r_int> ids - The cached group IDs
- int n_groups - Number of unique groups
- bool ordered - Do the group IDs specify a sorting order, or are they by order-of-first-appearance?
- bool sorted - Are the group IDs sorted? (This can also be true for order-of-first-appearance IDs)
- r_vec<r_int> start() - Returns an r_vec(n_groups) vector of start locations of each unique group, signifying the location in the data at which each group initially appeared
- r_vec<r_int> counts() - Returns an r_vec(n_groups) vector of frequency counts of each unique group
- r_vec<r_int> order() - Returns an r_vec(ids.length()) order vector. This is a 0-indexed permutation vector that can be used to return sorted group IDs
recycle() - Recycles supplied vectors to common length
r_vec::subset() - Fast subsetting of vectors

Stats sugar functions

Some statistical summary functions that are all very highly optimised for speed

sum() - Sum of values
mean() - Average of values
range() - Min and max range of values
var() - Variance

Annex

Symbols in R-registered templates

r_sym is unsupported in templates when it’s part of a template argument but is supported when the argument is explicitly an r_sym.

[[cppally::register]]
r_str symbol_to_string(r_sym x){
    return as<r_str>(x);
}

hello_world_symbol <- as.symbol("hello world!")
hello_world_symbol
`hello world!`
symbol_to_string(hello_world_symbol)
[1] "hello world!"

All core cppally concepts

RIntegerType - Includes r_lgl, r_int, r_int64
RMathType - Includes r_lgl, r_int, r_int64 and r_dbl
RStringType - Includes r_str and r_str_view
RScalar - Includes all cppally specific scalar types
RVal - Includes anything a cppally vector (r_vec<>) can contain: RScalar +r_sexp
RVector - Includes r_vec<T> where T is an RVal
RFactor - Factors
RDataFrame - Data frames
RComposite - Includes vectors, factors and data frames
RTimeType - Includes r_date and r_psxct
RNumericType - Numeric types, including RMathType and RTimeType
RSortableType - Includes RNumericType and RStringType (strings can also be sorted)
RAtomicVector - A vector that contains RScalar elements
CppallyType - Any R type defined by R, including RVal, RVector, RFactor, RDataFrame, RSymbol
CppType - Anything that is not an CppallyType
CastableToRScalar - Anything that can be constructed or cast into an RScalar (which also includes RScalar)

Other useful type traits

unwrap_t - Returns the underlying unwrapped type
as_r_scalar_t - Returns the equivalent RScalar type
as_r_composite_t - Returns the equivalent RComposite type
common_r_t - Returns the common cppally type between 2 types

Accessing the underlying types and values

While it is generally recommended not to access the underlying objects, you can do so with unwrap() which returns the underlying C/C++ value. For example, unwrap(r_int(5)) will return an int of value 5.

To access the underlying type, use unwrap_t<> which always aligns with unwrap()

The main reason for wanting to access underlying values would likely be optimisation and so unwrap() and unwrap_t allow this to be done consistently.

Example: Summing a double vector using r_vec<T>::data() member


[[cppally::register]]
double primitive_sum(const r_vec<r_dbl>& x){

  // r_vec<T>::data_type always returns typename T
  using data_t = typename std::remove_cvref_t<decltype(x)>::data_type;

  using primitive_t = unwrap_t<data_t>;
  primitive_t *p_x = x.data();

  r_size_t n = x.length();
  double sum = 0;

  OMP_SIMD_REDUCTION1(+:sum)
  for (r_size_t i = 0; i < n; ++i){
    sum += p_x[i];
  }
  return sum;
}

x <- rnorm(10^5)
primitive_sum(x)
#> [1] -467.8787

Unlike r_str which is composite and holds an r_sexp member, r_date and r_psxct instead inherit directly from r_dbl. This means that they can implicitly convert to r_dbl↩︎
r_sexp represents a generic R object which can include cppally vectors. We will explain how to disambiguate r_sexp later which is most useful when working with lists and data frames↩︎
Having an NA sentinel for r_sexp is very useful when writing templates involving vectors. For this reason the NA sentinel is r_null. This doesn’t mean is_na(r_null) is true, and is intentionally not true because it is not a scalar and therefore cannot be NA. As r_null represents the absence of a tangible R object, it can be thought of as a zero-length object and since all NA values are represented as length-1 vectors (in R), is_na(r_null) should not return true.↩︎