What Is R Programming?

Written by Indicative Team


R Programming Defined

R programming is a computer language and an integrated suite of software facilities, primarily used for statistical inference, data analysis, linear regression, time series, and machine learning.

R programming can provide users;

  • an effective data handling and storage facility
  • a suite of operators for calculations on arrays, in particular matrices
  • a large, coherent, integrated collection of intermediate tools for data analysis
  • graphical facilities for data analysis and display either on-screen or on hard-copy
  • a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

To conduct data analysis with R programming, there are a series of steps:

  • Program: R is a clear and accessible programming tool
  • Transform: R is made up of a collection of libraries designed specifically for data science
  • Discover: Investigate the data, refine your hypothesis and analyze them
  • Model: R provides a wide array of tools to capture the right model for your data
  • Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world

Some of the benefits of using R programming include:

  • Open Source – R is an open-source programming language. 
  • Exemplary Support for Data Wrangling – R provides exemplary support for data wrangling. 
  • Quality Plotting and Graphing – R facilitates quality plotting and graphing.
  • Highly Compatible- R is highly compatible and can be paired with many other programming languages like C, C++, Java, and Python.
  • Platform Independent – R is a platform-independent, cross-platform language. 
  • Machine Learning Operations – R provides various facilities for carrying out machine learning operations like classification and regression.
  • Continuously Growing – R is a constantly evolving programming language.

In Data Defined, we help make the complex world of data more accessible by explaining some of the most complex aspects of the field.

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