The R packages are documented well with description, vignette, sample examples, version controls, shareable code, integration with IDE (R Studio), license, and many more. On the contrary, the R packages are the complete environment for computational codes, which ensures all the covering over it, so that it can be used without any hassle. Besides, the version controls are the other serious issues. But, in alternative cases, if such codes are lacking with instruction manuals, documentation, and prerequisite sources or libraries, it is challenging to troubleshoot and reproduce the results. If such codes are documented and demonstrated well, the world can utilize it with boundless possibilities. Generally, GitHub is the preferred repository for many researchers to store and share their codes. Several researchers working in computational domain work on various interesting problem statements and end up with some meaningful code and analysis. The combination of such R packages makes the task of data analysis as easy as playing with some board games. There are several packages for data wrangling, data visualization, data predictions, datasets, optimizations, test benches, performance evaluation tools, and many more.

The R packages are generally combinations of functions that are written in R and targeted for some data analysis functionalities. Nowadays, R packages are gaining huge popularity because of many reasons. The R packages are the open-source tools, generally used in analyzing or visualizing datasets.

Build a package in r