Using R language to analyze and programming vital data by applying it to a human diseases

R is an open-source software platform for statistical data analysis. The R project started in 1993 as a project launched by two New Zealand statisticians, Ross Ilhaka and Robert Gentleman, and their goal was to create a new research platform in statistical computing. Since then, this pilot project has grown to include more than twenty statisticians and computer scientists from all over the world. Because it is an open-source platform, R has been rapidly accredited by statistical departments from universities around the world, and its expansion nature has attracted them as a platform for academic research, and the free platform has also played an important role. And not long ago, statisticians, data scientists, and machine learning began publishing research papers containing R code to implement new work assignments, among most academic journals. Platform R made this process very easy: anyone can post a working package within the platform in the "R Archive Full Network" "CRAN", and it is available to everyone. As of this writing, thousands of R platform users have contributed over 6,100 work packages, extending the capabilities of the platform to fields as diverse as economics, clinical trial analysis, social science, and web data. Anyone can search for applications in MRAN for the topic they want. Many companies and other organizations are working on expanding the R project while maintaining the original essence through the non-profit R Foundation (based in Vienna, Austria). The Bio Conductor has created more than 900 additional work packages, making this project a pioneering programmatic in genetic and genetic data analysis. R Studio has created a great interactive development environment in the R language, boosting user productivity all over the world. Revolution Analytics supported the R project with an open revolution that made it easy to embed it in any other application. In this article, we will try to take a practical example through which we give an overview of data analysis using R.