R is an open source programming language and software environment for statistical computing and graphics. It is one of the primary languages used by data scientists and statisticians. It is supported by the R Foundation for Statistical Computing and a large community of open source developers. Since R utilizes a command line interface, there can be a steep learning curve for some individuals who are used to using GUI focused programs such as SPSS and SAS so extensions to R such as RStudio can be helpful. Since R is an open source program and available for free, there can a large attraction for academics whose access to statistical programs are regulated through their association to various colleges or universities.
R has multiple packages (which are similar to libraries used in languages like python) on repositories like CRAN and bioconductor, which can be utilized for various purposes.
* [RStudio](https://www.rstudio.com/products/rstudio/) is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.
* [The Comprehensive R Archive Network (CRAN)](https://cran.r-project.org/) is a leading source for R tools and resources.
* [Tidyverse](https://www.tidyverse.org/) is an opinionated collection of R packages designed for data science like ggplot2, dplyr, readr, tidyr, purr, tibble.
* [data.table](https://github.com/Rdatatable/data.table/wiki) is an implementation of base `data.frame` focused on improved performance and terse, flexible syntax.
* [Shiny](https://shiny.rstudio.com/) framework for building dashboard style web apps in R.
