R is an open-source programming language created explicitly for statistical computing and graphics. Ross and Robert Gentleman started R in 1993, and it is widely used for data analysis and statistical modeling.
It is particularly well-suited for working with data, with a wide range of packages that support data import, cleaning, manipulation and visualization. R also has a strong community, contributing to developing a wide range of packages, tutorials, and other resources.
R has an extensive library of packages that provide a wide range of statistical and graphical capabilities. R is also widely used in academia and research, as well as in industry, for data analysis, data visualization, and statistical modeling.
R Studio is built on top of the R programming language that provides various reproducible research tools. The multiple tools, such as R Markdown, allow users to create documents that integrate text, code, and outputs. It also has Shiny, which enables users to create interactive web applications using R. It has built-in support for version control systems like Git that track changes to your code and collaborate with others.
With the increasing amount of data in various fields, R became a powerful tool for data science and machine learning, particularly in making beautiful graphs and visualizations.
R packages include:
Dplyr: It is a data manipulation library for R.
Tidyr is a great package that will help you clean and tidy your data.
Ggplot2: the perfect library for visualizing data.
Shiny: It is the ideal tool for creating interactive web apps directly from R.
Caret: one of the most important libraries for machine learning in R.
This article was written by Mercy Chebet of the HURU School Data Science Class