Overview
- Package Manager is RStudio’s latest tool that reduces (or eliminates) security risks attached with downloading packages from CRAN
- You only need a single server system to connect to a CRAN mirror, all other server systems can connect to it
- It could lead to a massive surge in terms of RStudio’s adoption by more enterprises
Introduction
RStudio has just laid to rest one of the biggest hurdles R has previously faced in terms of enterprise adoption with the release of the ‘Package Manager’ tool.
Anyone who has used R is well aware of CRAN and the multiple mirrors available to download packages. More often than not desktop systems in the organization will have full access to download packages from any available mirror, but server systems usually are not given the required permissions (due to security concerns).
Given the widespread usage of R, there have been previous attempts to circumvent this issue – creating an internal CRAN-like system, building entirely new or attempting to modify existing packages, etc. But none of these were truly satisfactory enough answers.
Package Manager is a game changer, there’s no other way to put it. It is a repository management server to organize and centralize R packages in your organization. In other words, it is a single CRAN-esque interface that eliminates the security risk on server systems.
Source: R-Bloggers
The above image shows how the Package manager works and reduces the risk of a security breach. You only need a single server system for external access to a CRAN mirror built for this purpose. All the other server systems in the organization can then connect to this individual system instead of multiple unmonitored mirrors. Of course individual desktops/laptops can also make use of Package Manager.
Check out the below resources to understand and get started with using Package Manager:
Our take on this
Having been a R user before I learned Python, I always keenly follow any R updates. And this is as big as they come. If your organization or business had been dithering on using R, this should tip the scales in R’s favor significantly.
If you have any questions around this and how to enable your organization to use it, check out the links I have mentioned above. They are comprehensive in nature and will take care of your queries. You can also use the comments section below and I will try to help out as well.
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Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.
