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R programming is an interpreted programming language widely used to analyze statistical information and a graphical representation.

R programming is popular in the field of data science among data analysts, researchers, statisticians, etc. You can use R to retrieve data from datasets, clean them, analyze and visualize them, and present them in the most suitable way.

Our R tutorials will guide you to learn R programming one step at a time.

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About R Programming

  • Open-source Language - R is freely available to the public. You can make improvements on the language and access hundreds of useful packages created by others for free.
  • Domain-specific Language - R is a domain-specific language and its specialty lies in statistical and data analysis.
  • Strong Graphical Capabilities - R can also be used for data visualization. It provides extended libraries that will help you produce high quality interactive graphics.
  • Interpreted Language - R is an interpreted language like Python.
  • Integration with Other Technologies - You can integrate your R code or project with programming procedures written in C, C++, Python, .Net, etc.

Why Learn R Programming?

  • R is one of the most popular statistical programming languages for data scientists. It is heavily used in the field of machine learning, scientific computing, and statistical analysis.
  • Since R is an interpreted programming language, you can run your code without any compiler. This makes development easier.
  • R can be used to perform vector calculations. It is a vector language and can be used to add functions to a single vector.

How to learn R programming?

  • R tutorial from codemy - You can start learning R programming from our step-by-step tutorials with intuitive examples. Check out Getting Started with R.
  • Official R Documentation - The official R documentation provides a good insight on the R concepts.
  • Write a lot of Code in R - The best way to learn ay programming language is by practicing to write a lot of code.
  • Read R code - Join Github's open-source projects and read other people's code.