Packages-packages-packages ... How efficient are you using R?

The current culture of "competencies" and "practices" assumes that a person is taught some approaches and recipes for solving a set of problems. At the same time, the time of the relevance of these "recipes" is hidden outside the framework, and they, in fact, are cast into a monolith, replicated by a person for years. Sometimes we hear sayings about "best practices" that are already 30 years old and during this time several paradigm changes have passed. And with this "best practice" you seem to be in a time capsule.



Yes, it is mentally convenient and saves the energy of the "specialist". Yes, it creates a sense of stability. But for high-quality and efficient work, it is necessary to constantly correct and sharpen the tool.



The 2020 R is very different from the 2018 R. In the very basic code, significant changes have been made to improve efficiency and stability (speed and memory consumption). But the more dynamic part of the ecosystem is the packages. It is useful to review their collection periodically in order to move to more convenient and efficient implementations. Since the last publication of the "Gentleman's set of R packages for automating business tasks" , the packages themselves have undergone major upgrades and their range has expanded quite a lot and the leaders have changed places many times.



It is no secret that mainstream does not mean maximum efficiency and versatility. In keeping with the mainstream, it is very easy to miss packages that are gems. It is especially convenient to open them at R conferences UseR !, Rconf, eRum, etc.



Below is a list of general-purpose packages that prove to be very useful for everyday tasks (x packages from> 10K on CRAN). It often turns out that many new items are unknown to the interlocutors. For a summary review of the cut for July 2020, I publish it as a compilation. Links, in most cases, lead to a feature collection page. I am sure that everyone will find something useful for themselves.



R: EDA





R: data_pkg





R: algo_pkg





R: vis_pkg





R: sys_pkg





R: shiny+Rmarkdown





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