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3 results for “R programming language”
Do Current Language Models Support Code Intelligence for R Programming Language?
<p>This is the dataset used in the paper: Do Current Language Models Support Code Intelligence for Programming Language?</p> <p> </p> <p>This dataset contains code snippets from R programming language repositories on GitHub, paired with their corresponding natural language (NL) descriptions. It was created for research in software engineering tasks like code summarization and code search. The data was collected using the GitHub REST API and includes over 1,500 public R repositories. To ensure quality, only active, well-structured R packages with proper documentation were included. Roxygen2, a popular documentation framework, was used to extract both the code and its matching NL descriptions.</p> <p>The dataset is organized into three parts: base R functions (Base), functions from the tidyverse (Tidy), and a combined set (RCombine). The dataset follows the CodeSearchNet format, with a split for training, validation, and testing data, ensuring no duplicate functions.</p>
Diversification and change in the R programming language
<p>Languages change over time, driven by creation of new words and cultural pressure to optimise communication. Programming languages resemble written language but communicate primarily with computer hardware rather than a human audience. I tested for changes over time in use of R, a mature, open-source programming language used for scientific computing. Across 393,142 GitHub repositories published between 2014 and 2021, I extracted 143,409,288 R functions, programming "verbs", and paired linguistic and ecological approaches to estimate change in the diversity and composition of function use over time. I found that the number of R functions in use increased and underwent substantial change, driven primarily by the popularity of the "tidyverse" collection of community-written extensions. I provide evidence that users can directly change the nature of programming languages, with patterns that match known processes from natural languages and genetic evolution. In the case of R, patterns suggested there are selective pressures for increased analytic complexity and R functions in decline but not extinct ("extinction debts"). R's evolution towards the tidyverse may also represent the start of a division into two distinct dialects, which may impact the readability and continuity of analytic and scientific inquiries codified in R, as well as the language's future.</p>
Diversification and change in the R programming language
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