Rcpp Data Frame Return with a List Column: A Solution for Handling AsIs Class Flag

Understanding Rcpp Data Frame Return with a List Column (Where is the AsIs?)

In this article, we will delve into the intricacies of working with data frames in Rcpp and specifically address how to create a list column within these structures. The question arises from attempting to achieve the following output using the I() function in regular R code:

what_i_wanted = data.frame(
  another_regular_column = c(42, 24, 4242),  
  thelistcol = I(list(as.raw(c(0,1,2)), as.raw(c(3, 4)), as.raw(c(5, 6, 7, 8, 9, 10))))
)

str(what_i_wanted)
# 'data.frame': 3 obs. of  2 variables:
# $ another_regular_column: num  42 24 4242
# $ thelistcol            :List of 3
#  ..$ : raw  00 01 02
#  ..$ : raw  03 04
#  ..$ : raw  05 06 07 08 ...
#  ..- attr(*, "class")= chr "AsIs"

This structure is identical to what we obtain when using the Rcpp makeListColumn() function. The primary difference between this code and our attempted Rcpp solution lies in the use of the I() function.

Introduction to Rcpp

Rcpp allows us to create C++ code that can be seamlessly integrated into an R environment, creating a powerful synergy for both speed and functionality. This is especially useful when dealing with tasks requiring computational intensity or data processing efficiency.

Understanding Data Frames in Rcpp

A DataFrame in Rcpp represents a two-dimensional table of values where each row corresponds to an observation, and each column corresponds to a variable. We can think of it as a list of rows.

Rcpp provides several classes to work with data frames, including Rcpp::DataFrame, which serves as the base class for most data frame-related operations in Rcpp.

Creating a List Column within a Data Frame

When working with C++ in Rcpp, we need to be aware that lists behave differently than they do in regular C++. In particular, when dealing with std::vectors and their push_back function, it’s crucial to remember that these changes occur at runtime.

The Problem with AsIs in Rcpp

The question centers on the implementation of the AsIs class flag within a list column. We’re looking for an Rcpp way to handle this similar to how we do it in regular R using I() function, which indicates whether we should treat data as ‘as-is’ or not.

A New Approach

We’ll explore creating a list structure inside of our C++ code and then modify its attributes at runtime. This will enable us to include the AsIs class flag within our data frame’s the_listcol column.

The Rcpp Code Solution

Here is the revised Rcpp code that solves this problem:

#include &lt;Rcpp.h&gt;

// [[Rcpp::export]]
SEXP makeListColumn() {
  // Store inside of an Rcpp List
  Rcpp::List the_future_list(3);
  the_future_list[0] = Rcpp::RawVector::create(0, 1, 2);
  the_future_list[1] = Rcpp::RawVector::create(3, 4);
  the_future_list[2] = Rcpp::RawVector::create(5, 6, 7, 8, 9, 10);

  // Mark with AsIs
  the_future_list.attr("class") = "AsIs";

  // Store inside of a regular vector
  std::vector&lt;int&gt; another_regular_column;
  another_regular_column.push_back(42);
  another_regular_column.push_back(24);
  another_regular_column.push_back(4242);

  // Construct a list 
  Rcpp::List ret = Rcpp::List::create(
      Rcpp::Named("another_regular_column") = another_regular_column,
      Rcpp::Named("thelistcol") = the_future_list);

  // Coerce to a data.frame
  ret.attr("class") = "data.frame";
  ret.attr("row.names") = Rcpp::seq(1, another_regular_column.size());

  // Return the data.frame
  return ret;
}

Conclusion

In this article, we explored how to work with list columns in a C++-based DataFrame within an R environment using Rcpp. We touched upon the importance of understanding classes and their modifications at runtime.

We also looked into the differences between regular C++ lists and those created by Rcpp::List. Understanding these differences is crucial when creating data structures that will be used with R’s data.frame.

The code we presented above demonstrates how to create a list column within an Rcpp data frame. It shows how to include attributes like AsIs to differentiate the treatment of the data.

Finally, by using the correct classes and their methods in our Rcpp code, we’re able to leverage both speed and functionality when working with complex tasks that require computational intensity or data processing efficiency.

Last modified on 2024-02-22