Conference paper Open Access

Designing Types for R, Empirically

Alexi; Aviral; Filip; Jan

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  <identifier identifierType="DOI">10.5281/zenodo.4037278</identifier>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-0814-5015</nameIdentifier>
    <title>Designing Types for R, Empirically</title>
    <subject>Empirical Evaluation</subject>
    <subject>Dynamic Program Analysis</subject>
    <subject>Type System</subject>
    <date dateType="Issued">2020-09-18</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4037277</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;The R programming language is widely used in a variety of domains. It was designed to favor an interactive style of programming with minimal syntactic and conceptual overhead. This design is well suited to interactive data analysis, but a bad fit for tools such as compilers or program analyzers which must generate native code or catch programming errors. In particular, R has no type annotations, and all operations are dynamically checked at run-time. The starting point for our work are the twin questions: what expressive power is needed to accurately type R code? and which type system is the R community willing to adopt? Both questions are difficult to answer without actually experimenting with a type system. The goal of this paper is to provide data that can feed into that design process. To this end, we perform a large corpus analysis to gain insights in the degree of polymorphism exhibited by idiomatic R code and explore potential benefits that the R community could accrue from a simple type system. As a starting point, we infer type signatures for 25,215 functions from 412 packages among the most widely used open source R libraries. We then conduct an evaluation on 8,694 clients of these packages, as well as on end-user code found on the Kaggle competition website.&lt;/p&gt;</description>
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