Seamless R and C++ Integration with Rcpp

Eddelbuettel, Dirk

doi:10.1007/978-1-4614-6868-4

Cited by 835 publications

(788 citation statements)

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“…The Rcpp package (Eddelbuettel and François 2011;Eddelbuettel 2013) is used to facilitate this integration of the R and C++ code for these objects. Each method is wrapped individually which allows us to add user-friendly custom error handling, type coercion, and performance improvements at the cost of a more verbose implementation.…”

Section: Under the Hood: S4 Classes And Methodsmentioning

confidence: 99%

RProtoBuf: Efficient Cross-Language Data Serialization inR

Eddelbuettel¹,

Stokely²,

Ooms³

2016

J. Stat. Soft.

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Modern data collection and analysis pipelines often involve a sophisticated mix of applications written in general purpose and specialized programming languages. Many formats commonly used to import and export data between different programs or systems, such as CSV or JSON, are verbose, inefficient, not type-safe, or tied to a specific programming language. Protocol Buffers are a popular method of serializing structured data between applications -while remaining independent of programming languages or operating systems. They offer a unique combination of features, performance, and maturity that seems particularly well suited for data-driven applications and numerical computing. The RProtoBuf package provides a complete interface to Protocol Buffers from the R environment for statistical computing. This paper outlines the general class of data serialization requirements for statistical computing, describes the implementation of the RProtoBuf package, and illustrates its use with example applications in large-scale data collection pipelines and web services.

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Section: Under the Hood: S4 Classes And Methodsmentioning

confidence: 99%

RProtoBuf: Efficient Cross-Language Data Serialization inR

Eddelbuettel¹,

Stokely²,

Ooms³

2016

J. Stat. Soft.

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“…The calculations of nearest neighbour analysis and second-order characteristics were developed based on R scripts and C++ files from Pommerening's Forest Biometrics Lab [45]. The Spatstat package [46] and Rcpp package [47] were used in our calculations.…”

Section: Mark Differentiation Functionmentioning

confidence: 99%

Prevalence of Inter-Tree Competition and Its Role in Shaping the Community Structure of a Natural Mongolian Scots Pine (Pinus sylvestris var. mongolica) Forest

Wang

Park

QianXue

et al. 2017

Forests

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Abstract:Inter-tree competition is considered one of the most important ecological processes of forest development. However, its importance in structuring the spatial patterns of plant communities remains controversial. We collected observational data from two plots in a natural Mongolian Scots pine forest to study the contribution of competition to tree growth, mortality, and size inequality. We used the nearest neighbour method to determine the presence of competition, and unmarked and marked spatial point pattern analyses to test the density-dependent mortality effects and the spatial autocorrelation of tree size. We identified significant positive correlations between tree canopy diameter and nearest neighbour distance in both plots, which were more evident in the denser plot. The pair correlation functions of both plots indicated regular distribution patterns of living trees, and trees living in more crowded environments were more likely to die. However, the mark differentiation characteristics showed weak evidence of a negative spatial autocorrelation in tree size, particularly in the high-density plot. The high mortality rate of suppressed trees and weak asymmetric competition may have accounted for the lack of dissimilarity in tree size. This study showed that inter-tree competition is an important determinant of the development of Mongolian Scots pine forests.

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“…Particularly noteworthy here is the C++ language, developed originally as a 'better C ', which is by its design very interoperable with C. And with the introduction of the Rcpp package (Eddelbuettel and François, 2011;Eddelbuettel, 2013;, and its later refinements, this process of extending R has become considerably easier yet also more robust. To date, Rcpp has become the most popular extension system for R. This article introduces Rcpp, and illustrates with several examples how the Rcpp Attributes mechanism in particular eases the transition of objects between R and C++ code.…”

Section: Introductionmentioning

confidence: 99%

Extending R with C++: A Brief Introduction to Rcpp

Eddelbuettel¹,

Balamuta

2017

Preprint

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R has always provided an application programming interface (API) for extensions. Based on the C language, it uses a number of macros and other low-level constructs to exchange data structures between the R process and any dynamically-loaded component modules authors added to it. With the introduction of the Rcpp package, and its later refinements, this process has become considerably easier yet also more robust. By now, Rcpp has become the most popular extension mechanism for R. This article introduces Rcpp, and illustrates with several examples how the Rcpp Attributes mechanism in particular eases the transition of objects between R and C++ code.

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Seamless R and C++ Integration with Rcpp

Cited by 835 publications

References 0 publications

RProtoBuf: Efficient Cross-Language Data Serialization inR

RProtoBuf: Efficient Cross-Language Data Serialization inR

Prevalence of Inter-Tree Competition and Its Role in Shaping the Community Structure of a Natural Mongolian Scots Pine (Pinus sylvestris var. mongolica) Forest

Extending R with C++: A Brief Introduction to Rcpp

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