# Writing data into R matrix objects

Aaron Lun

#### 19 May 2021

#### Package

beachmat 2.8.0

# 1 Overview of the output API

Note: this document refers to version 2 of the beachmat API, which is still supported but no longer under active development. Developers writing new code are encouraged to use version 3, which is much more streamlined.

This document describes the use of the beachmat API for storing data in R matrices. We will demonstrate the API on numeric matrices, though same semantics are used for matrices of other types (e.g., logical, integer, character). First, we include the relevant header file:

    #include "beachmat/numeric_matrix.h"

Three types of output matrices are supported - simple `matrix`, `*gCMatrix` and `HDF5Matrix` objects. For example, a simple numeric output matrix with `nrow` rows and `ncol` columns is created by:

    // returns a std::unique_ptr<numeric_output> object
    auto odptr=beachmat::create_numeric_output(
        nrow, /* size_t */
        ncol, /* size_t */
        oparam /* beachmat::output_param */
    );

The `beachmat::output_param` class specifies the output matrix representation. It is simply constructed by passing the class and package names:

    beachmat::output_param simple_param;
    beachmat::output_param simple_param2("matrix", "base"); /* default */
    beachmat::output_param sparse_param("dgCMatrix", "Matrix");

Other class/package combinations can be handled by external linkage if available. Otherwise, the `output_param` constructor will default to an ordinary matrix.

# 2 Dynamic choice of output type

Another option is to allow the function to dynamically choose the output type to match that of an existing matrix. This is useful for automatically choosing an output format that reflects the choice of input format. For example, if data are supplied to a function in a simple matrix, it would be reasonable to expect that the output is similarly small enough to be stored as a simple matrix. On the other hand, if the input is file-backed, it suggests that the output may also be very large and thus require file-backed storage.

Dynamic choice of output type is performed by using the `Rcpp::Robject` object containing the input matrix to initialize the `output_param` object. If we have a matrix object `dmat`, the output type can be matched to the input type with:

    beachmat::output_param oparam(dmat /* Rcpp::RObject */);
    auto odptr=beachmat::create_numeric_output(nrow, ncol, oparam);

A similar process can be used for a pointer `dptr` to an existing `*_matrix` instance:

    beachmat::output_param oparam(dptr->get_class(), dptr->get_package());

# 3 Methods to store data

## 3.1 Storing data in columns

The `set_col()` method fills column `c` with elements pointed to by an iterator `out` to a Rcpp vector. `c` should be a zero-indexed integer in `[0, ncol)`, and there should be at least `nrow` accessible elements, i.e., `*out` and `*(out+nrow-1)` should be valid entries.

    odptr->set_col(
        c, /* size_t */
        out /* Rcpp::Vector::iterator */
    );

`out` can be an iterator to a `Rcpp::NumericVector`, `Rcpp::LogicalVector` or `Rcpp::IntegerVector`; type conversions will occur as expected to the type of the output matrix. No value is returned by this method.

`set_col()` can also be used with `first` and `last` arguments. This will fill column `c` from rows `first` to `last-1` with the entries from `*out` to `*(out+last-first-1)`, respectively. Both `first` and `last` should be in `[0, nrow]` and zero-indexed, with the additional requirement that `last >= first`.

    odptr->set_col(
        c, /* size_t */
        out, /* Rcpp::Vector::iterator */
        first, /* size_t */
        last /* size_t */
    );

The `set_col_indexed()` method fills column `c` with the vector of elements starting at iterator `val` at a vector of row indices starting at `idx`. Row indices can be unordered and duplicated11 1 But obviously they should be zero-indexed.; later entries will override earlier ones. Note that no check is performed for the sanity of the row indices.

    odptr->set_col_indexed(
        c, /* size_t */
        N, /* size_t */
        idx, /* Rcpp::IntegerVector::iterator */
        valm /* Rcpp::Vector::iterator */
    );

## 3.2 Storing data in rows

The `set_row()` method fills row `r` with elements pointed to by an iterator `out` to a Rcpp vector. `r` should be a zero-indexed integer in `[0, nrow)`, and there should be at least `nrow` accessible elements, i.e., `*out` and `*(out+nrow-1)` should be valid entries. No value is returned.

    odptr->set_row(
        r, /* size_t */
        out /* Rcpp::Vector::iterator */
    );

Filling of a range of the row can be achieved with the `first` and `last` arguments. This will fill row `r` from columns `first` to `last-1` with entries from `*out` to `*(out+last-first-1)`, respectively. Both `first` and `last` should be in `[0, ncol]` and zero-indexed, with the additional requirement that `last >= first`.

    odptr->set_row(
        r, /* size_t */
        out, /* Rcpp::Vector::iterator */
        first, /* size_t */
        last /* size_t */
    );

The `set_row_indexed()` method fills row `r` with the vector of elements starting at iterator `val` at a vector of column indices starting at `idx`. Column indices can be unordered and duplicated22 2 And again, zero-indexed.; later entries will override earlier ones. Note that no check is performed for the sanity of the column indices.

    odptr->set_row_indexed(
        r, /* size_t */
        N,  /* size_t */
        idx, /* Rcpp::IntegerVector::iterator */
        val, /* Rcpp::Vector::iterator */
    );

## 3.3 Storing data in individual cells

The `set()` method fills the matrix entry at row `r` and column `c` with the double-precision value `Y`. Both `r` and `c` should be zero-indexed integers in `[0, nrow)` and `[0, ncol)` respectively. No value is returned by this method.

    odptr->set(
        r, /* size_t */
        c, /* size_t */
        Y /* double */
    )

# 4 Returning a matrix object to R

The `yield()` method returns a `Rcpp::RObject` object containing a matrix to pass to R.

    Rcpp::RObject out = odptr->yield();

This is commonly used at the end of the function to return a matrix to R:

    return dptr->yield();

Note that this operation may involve an R-level memory allocation, which may subsequently trigger garbage collection. This is usually not a concern as Rcpp is excellent at protecting against unintended collection of objects. However, one exception is that of random number generation, where the destruction of the `Rcpp::RNGScope` may trigger a collection of unprotected `SEXP`s. This will almost always be the case when using `yield()` naively, as the construction of the matrix `SEXP` is done at the end of the function:

    // Possible segfault:
    extern "C" SEXP dummy1 () {
        auto odptr=beachmat::create_numeric_output(nrow, ncol,
            beachmat::output_param());
        Rcpp::RNGScope rng;
        // Do something with random numbers and store in odptr.
        return odptr->yield();
    }

One solution is to restrict the scope of the `Rcpp::RNGScope`. This ensures that there are no unprotected `SEXP` objects upon destruction of the `RNGScope`, as `yield()` has not yet been called.

    extern "C" SEXP dummy2 () {
        auto odptr=beachmat::create_numeric_output(nrow, ncol,
            beachmat::output_param());
        {
            Rcpp::RNGScope rng;
            // Do something with random numbers and store in odptr.
        }
        return odptr->yield();
    }

# 5 Methods to read data

A subset of the access methods are also implemented for `*_output` objects:

- `get_nrow()` and `get_ncol()`
- `get_row()`, `get_col()` and `get()`
- `get_class()`, `get_package()` and `clone()`.

These methods behave as described previously for `*_matrix` objects. They may be useful in situations where data are stored in an intermediate matrix and need to be queried before the matrix is fully filled.

In most applications, though, it is possible to fully fill the output matrix, call `yield()` and then create a `numeric_matrix` from the resulting `Rcpp::RObject`. This is often faster because certain optimizations become possible when beachmat knows that the supplied matrix is read-only (for example, `get_const_col()` and `get_const_col_indexed()`).

# 6 Other matrix types

Logical, integer and character output matrices are supported by changing the types in the creator function (and its variants):

    // returns a std::unique_ptr<integer_output>
    auto oimat=beachmat::create_integer_output(nrow, ncol, beachmat::output_param());

    // returns a std::unique_ptr<logical_output>
    auto olmat=beachmat::create_logical_output(nrow, ncol, beachmat::output_param());

    // returns a std::unique_ptr<character_output>
    auto ocmat=beachmat::create_character_output(nrow, ncol, beachmat::output_param());

For integer, logical and numeric matrices, `out` can be an iterator for any `Rcpp::NumericVector`, `Rcpp::IntegerVector` or `Rcpp::LogicalVector` objects. For integer and logical matrices, `Y` should be an integer. For character matrices, `out` should be of type `Rcpp::StringVector::iterator` and `Y` should be a `Rcpp::String` object.

Additional notes

- Similar to the issue discussed previously, it is probably unwise to use anything but a `Rcpp::LogicalVector::iterator` as `out` when storing data in a `logical_output`. This is because type conversion at the C++ level will not give the same results as conversion at the R level.

# 7 Handling parallelization

The API is not thread-safe, due to (i) the use of cached class members and (ii) the potential for race conditions when writing to the same location on disk/memory. The first issue can be solved by using `clone()` to create `*_output` copies for use in each thread33 3 Excepting HDF5 matrices, see comments here.. However, each copy may still read from and write to the same disk/memory location. Furthermore, even if each copy writes to different rows or columns, they are not guaranteed to affect different parts of memory. (Storage of rows of a sparse matrix, for example, is dependent on the nature of previous rows.) It is thus the responsibility of the calling function to ensure that access is locked and unlocked appropriately across multiple threads, e.g., via `#pragma omp critical`.