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---
title: "Unofficial Rcpp API Documentation"
output:
pdf_document: default
html_document: default
date: '`r format(Sys.time(), "%F %T %z")`'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
```
**Warning: This post is a work in progress. It will periodically be updated
as time permits.**
# Introduction
The following **unofficial** API documentation for [Rcpp](http://rcpp.org) is based off some
personal notes and teaching materials that I have prepared over the years of
working with Rcpp. I've attempted to reformat the notes in the form of [Armadillo's API](http://arma.sourceforge.net/docs.html),
which I think are some of the best documentation out there for a C++ matrix library.
At some point, when the documentation becomes a bit more stable or if there is
larger contributor interest, I will likely attempt to merge this into the Rcpp
project so that a docs subdomain could hopefully be added to <http://rcpp.org>.
Please note: The post is written using [RMarkdown](http://rmarkdown.rstudio.com)
for maximum flexibility.
# API Documentation for Rcpp 0.12.11
## Preamble
- The goal of the API documentations are to provide a public facing concise
view of Rcpp features. As a result, the documentation will be somewhat long.
To help navigate the documentation, it has been split into different sections.
Furthermore, one should use the built in search functionality to search text
for keywords using either `CTRL` + `F` on Windows and Linux or `CMD` + `F` on
macOS.
- Presently, any contribution to this document can be made by a [pull request
(PRs)](/~https://github.com/coatless/rcpp-api/pulls) on GitHub.
- Please report any bugs to the [Rcpp Core Team](/~https://github.com/rcppcore/rcpp/issues).
## Overview
- [Vector, Matrix, List, DataFrame](#vmld)
- [Member functions](#member-functions)
- [Operators](#ops)
- [Dimensional Information](#dim-info)
- [Element Access](#elem-access)
- [Position Access](#position-access)
- [Categorical Access](#categorical-access)
- [Logical Access](#logical-access)
- [Subset Views](#subview)
- [Iterators](#iterators)
- [STL-style container functions](#stl-functions)
- [Static Member Functions](#static-members)
- [Sugar](#sugar)
- [Statistical Distributions](#stat-dist)
- [Exception Handling](#exception-handling)
- [External Exception Classes](#external-exception-clasess)
- [Internal Exception Classes](#internal-exception-clasess)
- [Simple Exceptions](#simple-exception)
- [Exceptions](#exception)
- [Advanced exceptions](#advanced-exception)
- Environment, Function, Language
- XPtr
- S4 classes / modules
------
## Vector, Matrix, List, and DataFrame Classes
| | |
|:----------------------------------------------------------------|:-------------------|
| [Vector<*RTYPE*>, NumericVector, IntegerVector, ...](#vector) | Vector class |
| [Matrix<*RTYPE*>, NumericMatrix, IntegerMatrix, ...](#matrix) | Matrix class |
| [List](#list) | List typedef |
| [DataFrame](#dataframe) | Data frame class |
| [RObject](#robject) | RObject class |
## Member functions
| | |
|:-------------------------------------------------|:-------------------------------------------------------------------|
| [Operators](#ops) | Mathematical (add, subtract, etc) and logical (inequalities) |
| [Dimensional Information](#dim-info) | Size attribute information |
| [Element Access](#elem-access) | Retrieving element values with and without bounds check |
| [Subset Views](#subview) | Subset data structures |
| [Iterators](#iterators) | Random access iterators |
| [STL-style Container Functions](#stl-functions) | Standard Library styled functions |
| [Static Member Functions](#static-members) | Set of member functions persistant across instances |
## Exception Handling
**[External Exception Classes](#external-exception-clasess)**
| | |
|------------------------------------|------------------------------|
| [`stop`](#stop) | Stop execution |
| [`warning`](#warning) | Send Warning to console |
**[Internal Exception Classes](#internal-exception-clasess)**
_[Simple Exceptions](#simple-exception)_
| | |
|-----------------------------------------------|--------------------------------|
| [`not_a_matrix`](#simple-exception) | Object is not a matrix |
| [`parse_error`](#simple-exception) | Unable to parse values |
| [`not_s4`](#simple-exception) | Not a valid S4 class |
| [`not_reference`](#simple-exception) | Object not a reference |
| [`not_initialized`](#simple-exception) | Object not initialized |
| [`no_such_slot`](#simple-exception) | S4 Object lacks slot |
| [`no_such_field`](#simple-exception) | Exception not used |
| [`not_a_closure`](#simple-exception) | Object not a closure |
| [`no_such_function`](#simple-exception) | No such function |
| [`unevaluated_promise`](#simple-exception) | Promise not yet evaluated |
_[Exceptions](#exception)_
| | |
|-----------------------------------------------|-----------------------------------|
| [`S4_creation_error`](#exception) | Error creating object of S4 class |
| [`reference_creation_error`](#exception) | Exception not used |
| [`no_such_binding`](#exception) | No such binding |
| [`binding_not_found`](#exception) | Binding not found |
| [`binding_is_locked`](#exception) | Binding is locked |
| [`no_such_namespace`](#exception) | No such namespace |
| [`function_not_exported`](#exception) | Function not exported |
| [`eval_error`](#exception) | Evaluation error |
_[Advanced exceptions](#advanced-exception)_
| | |
|-----------------------------------------------|---------------------------------|
| [`not_compatible`](#adv-exception) | Not a compatible transformation |
| [`index_out_of_bounds`](#adv-exception) | Request index is out of bounds |
## Sugar
**[Logical Operations](#sugar-logic-ops)**
| | |
|:-----------------------------|:---------------------------|
| [`ifelse`](#ifelse) | Vectorized If-else |
| [`is_false`](#isbool) | Is Value False? |
| [`is_true`](#isbool) | Is Value True? |
| [`any`](#any) | At Least One Value is True |
| [`all`](#all) | All Values Must be True |
**[Complex Operators](#sugar-complex-ops)**
| | |
|:------------------------------|:-----------------------------------|
| [`Re`](#complex-number) | Real Values of Complex Number |
| [`Im`](#complex-number) | Imaginary Values of Complex Number |
| [`Mod`](#complex-modulus) | Modulus (r) |
| [`Arg`](#complex-arg) | Arg (theta) |
| [`Conj`](#complex-conjugate) | Complex Conjugate |
**[Data Operations](#sugar-data-ops)**
| | |
|:----------------------------|:------------------------------|
| [`head`](#first-last-elems) | View the First _n_ Values |
| [`tail`](#first-last-elems) | View the Last _n_ Values |
| [`abs`](#abs) | Absolute Value |
| [`sqrt`](#sqrt) | Square Root |
| [`pow`](#pow) | Raise to the _n_^th^ Power |
| [`sum`](#sum) | Summation |
| [`sign`](#sign) | Extract the Sign of Values |
| [`diff`](#diff) | Lagged Difference |
| [`cumsum`](#carth) | Cumulative Sum |
| [`cumprod`](#carth) | Cumulative Product |
| [`cummin`](#cext) | Cumulative Minimum |
| [`cummax`](#cext) | Cumulative Maximum |
| [`sin`](#trig) | Sine |
| [`cos`](#trig) | Cosine |
| [`tan`](#trig) | Tangent |
| [`asin`](#trig) | Arc Sine |
| [`acos`](#trig) | Arc Cosine |
| [`atan`](#trig) | Arc Tangent |
| [`sinh`](#trig) | Hyperbolic Sine |
| [`cosh`](#trig) | Hyperbolic Cosine |
| [`tanh`](#trig) | Hyperbolic Tangent |
| [`log`](#logexp) | Natural Logarithms |
| [`exp`](#logexp) | Expoential |
| [`log10`](#logexp) | Base 10 Logarithm |
| [`log1p`](#logexp) | Natural Logarithm $log(1+x)$ |
| [`expm1`](#logexp) | $\exp(x) - 1$ |
| [`sample`](#sample) | Randomly sample values |
**[Rounding of Numbers](#sugar-rounding)**
| | |
|:-----------------------------------|:----------------------------------------------|
| [`ceiling`](#ceil),[`ceil`](#ceil) | Smallest integer greater than or equal to x |
| [`trunc`](#trunc) | Truncates the values in x toward 0 |
| [`floor`](#floor) | Largest integer less than or equal to x |
| [`round`](#round) | Round values to specified decimal place |
| [`signif`](#signif) | Rounds values to number of significant digits |
**[Finite, Infinite and NaN Detection](#sugar-nan)**
| | |
|:-----------------------------------|:-------------------------------------------|
| [`pre-defined`](#nanconstants) | Pre-defined NA/NaN/Inf Constants |
| [`is_na`](#missingness) | Detects if values are missing |
| [`is_nan`](#missingness) | Detects if values are not a number (`NaN`) |
| [`is_finite`](#finite) | Detects if value is finite |
| [`is_infinite`](#finite) | Detects if value is infinite |
| [`na_omit`](#na-omit) | Remove `NA` and `NaN` values |
| [`noNA`](#nona) | Assert that the object is `NA` free |
**[The Apply Family](#sugar-apply)**
| | |
|:----------------------|:----------------------------------------------------------------|
| [`sapply`](#sapply) | Apply a function to one input and store results in vector |
| [`lapply`](#lapply) | Apply a function to one input and store results in list |
| [`mapply`](#mapply) | Apply a function to multiple inputs and store results in vector |
**[Special Functions of Mathematics](#sugar-special-math)**
| | |
|:-----------------------------|:---------------------------|
| [`factorial`](#factorials) | Factorial |
| [`lfactorial`](#factorials) | Factorial Logarithm |
| [`choose`](#combinatorics) | Combination |
| [`lchoose`](#combinatorics) | Combination Logarithm |
| [`beta`](#beta) | Beta Function |
| [`lbeta`](#beta) | Natural Log Beta Function |
| [`gamma`](#gamma) | Gamma Function |
| [`lgamma`](#gamma) | Natural Log Gamma Function |
| [`psigamma`](#gamma-deriv) | General Gamma Derivative |
| [`digamma`](#gamma-deriv) | Second Gamma Derivative |
| [`trigamma`](#gamma-deriv) | Third Gamma Derivative |
| [`tetragamma`](#gamma-deriv) | Fourth Gamma Derivative |
| [`pentagamma`](#gamma-deriv) | Fifth Gamma Derivative |
**[Statistical Summaries](#sugar-stats)**
| | |
|:---------------------|:---------------------|
| [`min`](#minmax) | Minimum Value |
| [`max`](#minmax) | Maximum Value |
| [`range`](#range) | Range |
| [`mean`](#mean) | Mean Value |
| [`median`](#median) | Median Value |
| [`var`](#var) | Variance |
| [`sd`](#var) | Standard Deviation |
**[Special Operators](#sugar-special-ops)**
| | |
|:-----------------------|:-------------------------------------|
| [`rev`](#rev) | Reverse ordering of a vector |
| [`pmax`](#pext) | Parallel maximum value |
| [`pmin`](#pext) | Parallel minimum value |
| [`clamp`](#clamp) | Values between a minimum and maximum |
| [`which_max`](#which) | Index of the maximum value |
| [`which_min`](#which) | Index of the minimum value |
**[Uniqueness Operators](#sugar-unique-ops)**
| | |
|:------------------------------|:---------------------------------------------------------------|
| [`match`](#match) | Find indices of the first value in a separate vector |
| [`self_match`](#self-match) | Find indices of the first occurrence of each value in a vector |
| [`in`](#in) | Determine if a match was located for each element of _A_ in _B_|
| [`unique`](#unique) | Obtain the unique values |
| [`duplicated`](#unique) | Obtain a logical vector indicating the duplicate values |
| [`sort_unique`](#sort-unique) | Obtain the unique values and sort them |
| [`table`](#table) | Create a frequency table of occurrences |
**[Set Operations](#sugar-set-ops)**
| | |
|:--------------------------|:-----------------------------------------|
| [`setequal`](#setequal) | Equality of Set Values |
| [`intersect`](#intersect) | Intersection of Set Values |
| [`union_`](#union) | Union of Set Values |
| [`setdiff`](#setdiff) | Asymmetric Difference of Set Values |
**[Matrix Operations](#sugar-matrix-ops)**
| | |
|:---------------------------|:-----------------------------------------------|
| [`colSums`](#matrix-sum) | Column Sums of a Matrix |
| [`rowSums`](#matrix-sum) | Row Sums of a Matrix |
| [`colMeans`](#matrix-mean) | Column Means of a Matrix |
| [`rowMeans`](#matrix-mean) | Row Means of a Matrix |
| [`outer`](#outer) | Outer Product of Arrays on a Function |
| [`lower_tri`](#tri-mat) | Extract the _Lower_ Triangle Part of a Matrix |
| [`upper_tri`](#tri-mat) | Extract the _Upper_ Triangle Part of a Matrix |
| [`diag`](#diag) | Extract the Diagonal Portion of a Matrix |
| [`row`](#idx-mat) | Create a matrix of Row Indexes |
| [`col`](#idx-mat) | Create a matrix of Column Indexes |
**[Object Creation](#sugar-object-creation)**
| | |
|:--------------------|:-----------------------------------------------|
| [`cbind`](#cbind) | Create matrix by combing column vectors |
| [`seq_along`](#seq) | Generate an R index sequence given a vector |
| [`seq_len`](#seq) | Generate an R index sequence given an integer |
| [`rep`](#rep) | Replicate vector $N$ times |
| [`rep_each`](#rep) | Replicate each element in line $N$ times |
| [`rep_len`](#rep) | Replicate values until vector is of length $N$ |
**[String Operations](#sugar-string-ops)**
| | |
|:------------------------|:-----------------------------------------------------|
| [`collapse`](#collapse) | Collapse multiple strings into one string |
| [`trimws`](#trimws) | Trim leading and/or trailing whitespace from strings |
**[Statistical Distributions](#stat-dist)**
**[Discrete Distributions](#discrete-dist)**
| | |
|-----------------------------------|------------------------|
| [`p/d/q/rbinom`](#bin-dist) | Binomial |
| [`p/d/q/rgeom`](#geo-dist) | Geometric |
| [`p/d/q/rhyper`](#hypergeo-dist) | Hypergeometric |
| [`p/d/q/rnbinom`](#negbin-dist) | Negative Binomial |
| [`p/d/q/rpois`](#pois-dist) | Poisson |
| [`p/d/q/rwilcox`](#wilcox-dist) | Wilcoxon |
| [`p/d/q/rsignrank`](#signed-dist)| Wilcoxon Signed Rank |
**[Continuous Distributions](#continuous-dist)**
| | |
|------------------------------------|------------------------------|
| [`p/d/q/rbeta`](#beta-dist) | Beta |
| [`p/d/q/rcauchy`](#cauchy-dist) | Cauchy |
| [`p/d/q/rchisq`](#chisquare-dist) | Chi-square |
| [`p/d/qnchisq`](#nchisquare-dist) | Non-central Chi-square |
| [`p/d/q/rexp`](#exp-dist) | Exponential |
| [`p/d/q/rf`](#f-dist) | F |
| [`p/d/qnf`](#nf-dist) | Non-central F |
| [`p/d/q/rgamma`](#gamma-dist) | Gamma |
| [`p/d/q/rnorm`](#normal-dist) | Normal |
| [`p/d/q/rlnorm`](#lognormal-dist) | Log Normal |
| [`p/d/q/rlogis`](#logistic-dist) | Logistic |
| [`p/d/q/rt`](#t-dist) | Student's T |
| [`p/d/q/runif`](#unif-dist) | Uniform |
| [`p/d/q/rweibull`](#weibull-dist) | Weibull |
## Vector, Matrix, List, and DataFrame Classes {#vmld}
### Vector {#vector}
- The templated `Vector` class is a one dimensional array-like structure
providing storage for homogenous data types, with an
interface similar to that of `std::vector`. Being an implementation of
[policy-based design](https://en.wikipedia.org/wiki/Policy-based_design),
much of the behavior of `Vector` is determined by the policy classes it
inherits from
- `RObjectMethods`
- `StoragePolicy`
- `SlotProxyPolicy`
- `AttributeProxyPolicy`
- `NamesProxyPolicy`
as well as the CRTP base class `VectorBase`. This type is instantiated as
`Vector<RTYPE>`, where `RTYPE` is one of the following valid `SEXPTYPE`s:
- `REALSXP`
- `INTSXP`
- `CPLXSXP`
- `LGLSXP`
- `STRSXP`
- `VECSXP`
- `RAWSXP`
- `EXPRSXP`
- For convenience, the following [`typedefs` have been defined](/~https://github.com/RcppCore/Rcpp/blob/6f81b4684481dbd9bb554dd95e66725fc3b63a8c/inst/include/Rcpp/vector/instantiation.h#L27-L38) in the `Rcpp` namespace:
- `NumericVector` = `Vector<REALSXP>`
- `DoubleVector` = `Vector<REALSXP>`
- `RawVector` = `Vector<RAWSXP>`
- `IntegerVector` = `Vector<INTSXP>`
- `ComplexVector` = `Vector<CPLXSXP>`
- `LogicalVector` = `Vector<LGLSXP>`
- `CharacterVector` = `Vector<STRSXP>`
- `StringVector` = `Vector<STRSXP>`
- `GenericVector` = `Vector<VECSXP>`
- `List` = `Vector<VECSXP>`
- `ExpressionVector` = `Vector<EXPRSXP>`
- Within this documentation, the default type used will be `NumericVector` unless
another data type is required to show a specific feature.
- Constructors:
- `Vector()`
- `Vector(SEXP x)`
- `Vector(const int &size, const stored_type &u)`
- `Vector(const std::string &st)`
- `Vector(const char *st)`
- `Vector(const Vector &other)`
- `Vector(const int &size)`
- `Vector(const Dimension &dims)`
- `Vector(const Dimension &dims, const U &u)`
- `Vector(const Vector &other)`
- By default, the vectors constructed from dimensions will always be
initialized with all entries being zero (`0`) or an empty string (`""`).
- For the majority of cases, the interface being used is that of the R to C++
interface that relies upon the `Vector(SEXP x)` constructor, which establishes
a **pointer** to the underlying data. That is, the `Vector` object _points_ to
the memory location of the `SEXP` R object in order to avoid copying the data
into _C++_. The only exception to this rule is if the data passed is of a
different type in which case a deep copy is performed _before_ a pointer is
established. For example, if `numeric()` data is passed to `NumericVector`
the correct handoff occurs. However, if `integer()` data were to be passed to
a `NumericVector` a `clone()` would be made to type `numeric()` which has its
pointer then assigned to the `NumericMatrix`.
- Examples:
```{Rcpp rcpp_vector_ctor}
SEXP A;
NumericVector B(A); // from a SEXP
// create a vector of length 5 filled with 0
NumericVector C(5);
// Output: 0 0 0 0 0
// construct a filled vector of size 3 with 2.0
NumericVector D = NumericVector(3, 2.0);
// Output: 2 2 2
// initialize empty numeric vector of size 5
NumericVector D2 = no_init(5);
// fill vector with 3.0
D2.fill(3.0);
// Output: 3 3 3 3 3
// cloning (deep copy)
NumericVector E = clone(D);
// Output: 2 2 2
```
- See also:
- [Vector Class doxygen documentation](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1Vector.html)
- [`typedef` at cppreference.com](http://en.cppreference.com/w/cpp/language/typedef)
------
### Matrix {#matrix}
- The main class for matrices is the templated `Matrix` class, which derives from
a combination of both the `Vector` and `MatrixBase` types. Like the `Vector`
class, `Matrix` uses the policy-based design pattern to manage the lifetime of
its undelying `SEXP` via the template parameter `StoragePolicy`, which
uses the `PreserveStorage` policy class by default. Matrices are
instantiated as `Matrix<RTYPE>`, where the value `RTYPE` is one of the
following `SEXPTYPE`s:
- `REALSXP`
- `INTSXP`
- `CPLXSXP`
- `LGLSXP`
- `STRSXP`
- `VECSXP`
- `RAWSXP`
- `EXPRSXP`
- For convenience, the following [`typedefs` have been defined](/~https://github.com/RcppCore/Rcpp/blob/6f81b4684481dbd9bb554dd95e66725fc3b63a8c/inst/include/Rcpp/vector/instantiation.h#L40-L50) in the `Rcpp`
namespace:
- `NumericMatrix` = `Matrix<REALSXP>`
- `RawMatrix` = `Matrix<RAWSXP>`
- `IntegerMatrix` = `Matrix<INTSXP>`
- `ComplexMatrix` = `Matrix<CPLXSXP>`
- `LogicalMatrix` = `Matrix<LGLSXP>`
- `CharacterMatrix` = `Matrix<STRSXP>`
- `StringMatrix` = `Matrix<STRSXP>`
- `GenericMatrix` = `Matrix<VECSXP>`
- `ListMatrix` = `Matrix<VECSXP>`
- `ExpressionMatrix` = `Matrix<EXPRSXP>`
- Within this documentation, the default type used will be `NumericMatrix` unless
another data type is required to show a specific feature.
- Constructors:
- `Matrix()`
- `Matrix(SEXP x)`
- `Matrix(const int& nrows_, const int& ncols)`
- `Matrix(const int& nrows_, const int& ncols, Iterator start)`
- `Matrix(const int& n)`
- `Matrix(const Matrix& other)`
- By default, the matrices constructed from dimensions will always be
initialized with all entries being zero (`0`) or empty strings (`""`)
- For the majority of cases, the interface being used is that of the R to C++
interface that relies upon the `Matrix(SEXP x)` constructor, which establishes
a **pointer** to the underlying data. That is, the `Matrix` object _points_ to
the memory location of the `SEXP` R object in order to avoid copying the data
into _C++_. The only exception to this rule is if the data passed is of a
different type in which case a deep copy is performed _before_ a pointer is
established. For example, if `numeric()` data is passed to `NumericMatrix`
the correct handoff occurs. However, if `integer()` data were to be passed to
a `NumericMatrix` a `clone()` would be made to type `numeric()` which has its
pointer then assigned to the `NumericMatrix`.
- Examples:
```{Rcpp rcpp_matrix_ctor}
SEXP A;
NumericMatrix B(A); // from a SEXP
// create a square matrix (all elements set to 0.0)
NumericMatrix C(2);
// Output:
// 0 0
// 0 0
// of a given size (all elements set to 0.0)
NumericMatrix D(2, 3);
// Output:
// 0 0 0
// 0 0 0
// of a given size with dimensions (all elements set to 0.0)
NumericMatrix D2(Dimension(3, 2));
// Output:
// 0 0
// 0 0
// 0 0
// initialize empty numeric matrix
NumericMatrix D3 = no_init(2, 1);
// fill matrix with 3.0
D3.fill(3.0);
// Output:
// 3.0
// 3.0
// fill matrix using a vector
NumericVector E = NumericVector(15, 2.0);
NumericMatrix F = NumericMatrix(3, 5, E.begin());
// Output:
// 2 2 2 2 2
// 2 2 2 2 2
// 2 2 2 2 2
// cloning (explicit deep copy)
NumericMatrix G = clone(F);
```
- See also:
- [`Matrix` Class doxygen documentation](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1Matrix.html)
- [`typedef` at cppreference.com](http://en.cppreference.com/w/cpp/language/typedef)
### List {#list}
- The `List` data structure is a `typedef` of templated `Vector` class based
on the `RTYPE` of `VECSXP` that provides heterogenous storage class. As
a result, the `List` class acts as a generic storage object that can
simultaneously hold multiple different `RTYPE` structures.
- Constructors:
- `List()`
- `List(SEXP x)`
- `List(const int &size, const stored_type &u)`
- `List(const std::string &st)`
- `List(const char *st)`
- `List(const Vector &other)`
- `List(const int &size)`
- `List(const Dimension &dims)`
- `List(const Dimension &dims, const U &u)`
- `List(const Vector &other)`
- Unlike the `Vector` class, the `List` constructed from dimensions will have a
`NULL` value set for each element unless a value is otherwise assigned.
- Examples:
```{Rcpp rcpp_list_ctor}
SEXP A;
List B(A); // from a SEXP
// create an empty List of size 2
List C(2);
// Output:
// [[1]]
// NULL
// [[2]]
// NULL
// construct List of size 3 with one element containing 2.0
List D = List(3, 2.0);
// Output:
// [[1]]
// [1] 2
// [[2]]
// [1] 2
// [[3]]
// [1] 2
// initialize empty list of size 3
List D2 = no_init(3);
// fill list one element equal to 3.0
D2.fill(3.0);
// Output:
// [[1]]
// [1] 3
// [[2]]
// [1] 3
// [[3]]
// [1] 3
// cloning (deep copy)
List E = clone(D);
// Output:
// [[1]]
// [1] 2
// [[2]]
// [1] 2
// [[3]]
// [1] 2
// Create a named list
NumericVector F = NumericVector::create(1.2, 3.5);
CharacterVector G = CharacterVector::create("a", "b", "c");
LogicalVector H = LogicalVector::create(true, false, false, true);
// Create named list
List F = List::create(Named("v1") = F,
Named("v2") = G,
_["v3"] = H); // Shorthand for Named("V3")
// Output:
// $v1
// [1] 1.2 3.5
// $v2
// [1] "a" "b" "c"
// $v3
// [1] TRUE FALSE FALSE TRUE
```
- See also:
- [`Vector`](#vector)
- [Vector Class doxygen documentation](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1Vector.html)
- [`typedef` at cppreference.com](http://en.cppreference.com/w/cpp/language/typedef)
### DataFrame {#dataframe}
- The `DataFrame` data structure is a typedef of the `DataFrame_Impl` class,
which is a special extension of the templated `Vector` class
that allows for a collection of heterogenous `Vector`'s of the same length.
As the crux of the implementation of is
[policy-based design](https://en.wikipedia.org/wiki/Policy-based_design) focused,
much of the behavior of `DataFrame` is determined by the policy classes it
inherits from
- `RObjectMethods`
- `StoragePolicy`
- `SlotProxyPolicy`
- `AttributeProxyPolicy`
- `NamesProxyPolicy`
as well as the CRTP base class `VectorBase`.
- Constructors:
- `DataFrame()`
- `DataFrame(SEXP x)`
- `DataFrame(const DataFrame &other)`
- `DataFrame(const T &obj)`
- **Caveat:** All `DataFrame` columns _must_ be named. Failure to name the columns
will result in the run time error of:
> not compatible with STRSXP
since Rcpp uses an internal call to _R_ to create the `DataFrame`.
- Examples:
```{Rcpp rcpp_dataframe_ctor}
SEXP A;
DataFrame B(A); // from a SEXP
// Create Data
NumericVector C = NumericVector::create(5.8, 9.1, 3.2);
CharacterVector D = CharacterVector::create("a", "b", "c");
LogicalVector E = LogicalVector::create(true, false, false);
// Create a new dataframe
DataFrame G = DataFrame::create(Named("C") = C,
_["D"] = D, // shorthand for Named("D")
Named("E") = E);
```
- See also:
- [`Vector`](#vector)
- [DataFrame `typedef`](http://dirk.eddelbuettel.com/code/rcpp/html/DataFrame_8h.html)
- [`DataFrame_Impl` class](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1DataFrame__Impl.html)
- [`typedef` at cppreference.com](http://en.cppreference.com/w/cpp/language/typedef)
### RObject {#robject}
- The `RObject` data structure is a typedef of the `RObject_Impl` class. Principally,
the class can be viewed as the glue of Rcpp due to [policy-based design](https://en.wikipedia.org/wiki/Policy-based_design)
principles. In turn, the `RObject` class really acts as a "shell" that stores
properties of the following policies:
- `PreserveStorage`: Member functions that provide the `SEXP` _R_ object
alongside ways to modify and update the object.
- `SlotProxyPolicy`: Member functions related to _only_ manipulating S4
objects.
- `AttributeProxyPolicy`: Member functions that modify attribute information
of the _R_ object.
- `RObjectMethods`: Member functions that provide descriptors of the _R_
object such as type, object oriented programming (S3/S4) system, and
`NULL` status.
- Constructors:
- `RObject()`
- `RObject(const RObject &other)`
- `RObject(const GenericProxy<Proxy> &proxy)`
- Examples:
```{Rcpp robject_example}
// Extract attribute information via AttributeProxyPolicy
RObject A;
RObject B = A.attr("dim");
```
- See also:
- [Modern C++ Design](http://en.wikipedia.org/wiki/Modern_C++_Design)
- [`RObject_Impl` class](http://dirk.eddelbuettel.com/code/rcpp/html/RObject_8h.html)
- [`RObjectMethods` class](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1RObjectMethods.html)
- [`StoragePolicy` class](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1StoragePolicy.html)
- [`SlotProxyPolicy` class](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1SlotProxyPolicy.html)
- [`AttributeProxyPolicy` class](http://dirk.eddelbuettel.com/code/rcpp/html/classRcpp_1_1AttributeProxyPolicy.html)
- [`typedef` at cppreference.com](http://en.cppreference.com/w/cpp/language/typedef)
## Member Functions {#member-functions}
### Operators {#ops}
- Operators allow for operations to take place between two different `Vector` or `Matrix` objects.
The operations are defined to works in an element-wise fashion where applicable.
- Viable mathematical operations that are able to be performed.
| Operation | Definition | Vector-Vector | Vector-Scalar | Vector-Matrix | Matrix - Matrix | Matrix - Scalar |
|:---------:|:------------------:|:-------------:|:--------------:|:-------------:|:---------------:|:---------------:|
| + | Addition | Yes | Yes | No | No | Yes |
| - | Subtraction | Yes | Yes | No | No | Yes |
| / | Division | Yes | Yes | No | No | Yes |
| * | Multiplication | Yes | Yes | No | No | Yes |
- Logical Operations
| Operation | Definition | Vector-Vector | Vector-Scalar | Vector-Matrix | Matrix - Matrix | Matrix - Scalar |
|:---------:|:-------------------------:|:-------------:|:--------------:|:---------------:|:---------------:|:---------------:|
| == | Equality | Yes | Yes | No | No | Yes |
| != | Non-equality | Yes | Yes | No | No | Yes |
| >= | Greater than or equal to | Yes | Yes | No | No | Yes |
| <= | Less than or equal to | Yes | Yes | No | No | Yes |
| < | Less than | Yes | Yes | No | No | Yes |
| > | Greater than | Yes | Yes | No | No | Yes |
| ! | Negate | Yes | Yes | No | No | Yes |
- Examples:
```{Rcpp sugar_math_ops}
// Sample data
NumericVector A = NumericVector::create(1, 2, 3, 4);
NumericVector B = NumericVector::create(2, 3, 4, 5);
// --- Addition
// Add a vector and scalar
NumericVector H = A + 2.0;
// Output: 3 4 5 6
// Add a vector and another vector
NumericVector I = A + B;
// Output: 3 5 7 9
// --- Subtraction
// Subtract a scalar from a vector
NumericVector J = 2.0 - A;
// Output: 1 0 -1 -2
// Subtract vectors
NumericVector K = A - B;
// Output: -1 -1 -1 -1
// --- Multiplication
// Multiple by scalar
NumericVector L = 3 * A;
// Output: 3 6 9 12
// Multiple Vectors
NumericVector M = A * B;
// Output: 2 6 12 20
// --- Division
// Divide by scalar
NumericVector L = 1 / A;
// Output: 1.0000000 0.5000000 0.3333333 0.2500000
// Divide Vectors
NumericVector M = A / B;
// Output: 0.5000000 0.6666667 0.7500000 0.8000000
// --- All together
NumericVector res = 3.0 * A - 1.0 / B + A + B + 5.0;
// Output: 10.50000 15.66667 20.75000 25.80000
```
### Dimensional Information {#diminfo}
| | |
|:----------------------|:---------------------------------------------|
| `.nrow()`,`.rows()` | number of rows in a `Matrix`, `DataFrame` |
| `.ncol()`,`.cols()` | number of columns in a `Matrix`, `DataFrame` |
| `.size()`,`.length()` | number of items in a `Matrix`, `Vector` |
- Return type is that of an `int`, `unsigned int`, or `R_xlen_t`
- Note: As of Rcpp 0.13.0, new size attribute accessors were added to the
`DataFrame` class that mimick those available in `Matrix`. Previously, to
obtain the number of columns, one would have to use the `.size()` or `.length()`
member function. In addition, the number of observations previously had to
be obtained by `.nrows()`.
- Examples:
```{Rcpp dimensional_info}
// --- Vector Example
NumericVector X(3);
int nelem = X.size(); // Output: 3
int nlens = X.length(); // Output: 3
Rcout << "Vector X has " << nelem << " elements." << std::endl;
// --- Matrix Example
NumericMatrix X(4,5);
int rows = X.nrow(); // Output: 4
int cols = X.ncols(); // Output: 5
int elems = X.size(); // Output: 20
Rcout << "Matrix Y has " << rows << " rows and " << cols << " columns." << std::endl;
```
### Element Access {#elem-access}
- Described within this section is the ability to access elements using
the position, categorical, and logical indexing systems.
- The access system provides two retrieval methods for all classes
that differ in computational time to obtain values from objects.
- The preferred method to access elements is `()`, which that takes slightly
longer due to a bounds check being performed that verifies whether the
requested element is within the access scope. Furthermore, if the
requested element is out of bounds, an exception is raised and the program
stops.
- The other method uses `[]`, which does _not_ perform a bounds check and
assumes that the access scope is valid. If an element is out of bounds,
the behavior exhibited will be undefined and may cause havoc with later
parts of a procedure. Only use this form of accessor if the procedure has
been thoroughly tested and debugged.
- Note: Using accessors without a bounds check is **not** recommended unless
the code has been thoroughly tested as undefined behavior (UB) may emerge. UB
is very problematic.
#### Position Access {#position-access}
- Access a single element or object using a positional index.
- `(i)` provides the *i*th element or object in addition to performing a
bounds check that ensures the requested index is a valid location.
- `[i]` similar to the previous case, but does so *without* a bounds check.
- `at(i,j)` provides the *i,j*th element of a `Matrix` with a bounds check.
- `(i,j)` provides the *i,j*th element of a `Matrix` *without* a bounds check.
- **Caveat:** Using either `[i]` or `(i)` on `List` and `DataFrame`,
provides the object (e.g. `Vector`) at position *i* whereas the use on
`Vector` or `Matrix` will provide a scalar element (e.g. `double`).
- Note: Unlike _R_, there is no `[]` subset operator for matrices with _C++_.
The reason for the lack of `operator[]` relates to a fundamental design choice
made by the creators of C++ related to the presence of the
[`operator,`](http://en.cppreference.com/w/cpp/language/operator_other#Built-in_comma_operator).
In essence, after the complete evaluation of the first coordinate `x` and disposal
of the results, only then is the second coordinate `y` able to be evaluated.
Unfortunately, this yields the following `-Wall` issue:
> left operand of comma operator has no effect.
Therefore, the only viable matrix subset operators within C++ are `operator()` and `operator at()` provide subset operations.
- Examples:
```{Rcpp position_access}
// Create data
NumericVector A = NumericVector::create(1, 2, 3, 4);
CharacterVector C = CharacterVector::create("B", "D", "E", "F");
// --- Vector
// Retrieve the first value from A. (C++ indices start at 0 not 1!)
double a = A(0);
// Output: 1
// Modify the last value using unbound accessor
// Warning: Make sure the point is valid!
A[3] = 5;
// --- Matrix
// Create matrix with elements in A
NumericMatrix B(2, 2, A.begin());
// Output:
// 1 3
// 2 5
// Extract Value at 2, 1
double val_r1c0 = B(1, 0);
// Output: 2
// Modify value at 1, 2
B(0, 1) = 4;
// Output:
// 1 4
// 2 5
// The following shows a bounds throw error
// B.at(1, 2) = 4;
// --- List
// Create a List
List D = List::create(Named("A") = A,
_["C"] = C); // shorthand for Named("C")
// Extract A from List
NumericVector E = D[0];
double val2 = E[1];
// Output: 2
// Extract B from List
CharacterVector F = D[1];
// --- Data Frame
// Create a DataFrame
DataFrame G = DataFrame::create(Named("A") = A,
_["C"] = C); // shorthand for Named("C")
// Extract A from DataFrame
NumericVector E = DF[0];
```
#### Categorical Access {#categorical-access}
- Access element by name within a `Vector`, `List`, or `DataFrame`.