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Easy steps to develop and publish your first R package
A step by step guide to building your own R package
The R packages are the open-source tools, generally used in analyzing or visualizing datasets. Nowadays, R packages are gaining huge popularity because of many reasons. Some of them are documented here.
The R packages are generally combinations of functions that are written in R and targeted for some data analysis functionalities. There are several packages for data wrangling, data visualization, data predictions, datasets, optimizations, test benches, performance evaluation tools, and many more. The combination of such R packages makes the task of data analysis as easy as playing with some board games.
Several researchers working in computational domain work on various interesting problem statements and end up with some meaningful code and analysis. Generally, GitHub is the preferred repository for many researchers to store and share their codes. If such codes are documented and demonstrated well, the world can utilize it with boundless possibilities. 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. Besides, the version controls are the other serious issues.
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. The R packages are documented well with description, vignette, sample examples, version controls, shareable code, integration with IDE (R Studio), license, and many more. Also, the R packages available in CRAN repository are well checked and can be uploaded to repository only after all warnings and errors are addressed by the package contributors, which ensures the error-free codes in the form of R package.
Many researchers avoid developing the R package due to a lack of knowledge of possibilities and the tedious procedure to develop the R packages.
This post is to demonstrate the easiest steps to develop an R package with minimum efforts. Also, it discusses the possible warnings and corresponding solutions.
