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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]
{} – For an indiscriminate list of "famous" people associated in some way with a topic. {} – For local-interest trivia that is unverifiable or otherwise unencyclopedic. List cleanup {} – Suggests converting into prose a section that consists of a list. {{Cleanup list}} – For indiscriminate lists that need reduction.
The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham [1] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data. [2] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3 ...
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration [1] and data management tasks such as data wrangling, data warehousing, data integration and application integration.
The table above (even if some more columns are added) maintains one line per country for narrower browser and screen widths. So it is therefore more readable and scannable in long country tables. The table format below can greatly increase in number of lines, and require more vertical scrolling, especially if more columns are added.
R packages are extensions to the R statistical programming language. R packages contain code, data, and documentation in a standardised collection format that can be installed by users of R, typically via a centralised software repository such as CRAN (the Comprehensive R Archive Network).
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.
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