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dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Comments can be used to summarize code or to explain the programmer's intent. According to this school of thought, restating the code in plain English is considered superfluous; the need to re-explain code may be a sign that it is too complex and should be rewritten, or that the naming is bad. "Don't document bad code – rewrite it." [9]
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
The NFL announced the rosters for the 2025 Pro Bowl Games on Thursday morning. Players from 28 of 32 teams were selected with the Baltimore Ravens leading the way with nine selections, followed by ...
It’s more accurately described as “command center memory,” orchestrated by the central nervous system, adds Rothstein. But that’s only half the story. Beyond neural pathways, muscle memory ...
By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data . [ 145 ]