What Is the R Programming Language?

R is an interpreted programming language and runtime environment designed for statistical computing, graphics and data visualization.

What Is R in Programming?

R is one of the most comprehensive statistical programming languages available, capable of handling everything from data manipulation and visualization to statistical analysis.

A part of the GNU Project, R is considered a different implementation or open-source variation of the S programming language, as much of the code written for S can also run under R.

The R distribution supports a large number of statistical procedures, such as linear and nonlinear modeling, time series analysis, clustering and more. R also has various functions for creating publication-quality plots and data visualizations, which can include mathematical symbols and formulae.

Additionally, R features an active community that regularly produces packages to facilitate processes like specialized statistical techniques, graphical devices, import/export capabilities and reporting tools.

Key Features of R

R is widely used in data science, research and academia industries for its flexibility and range of data tools. 

Below are a few key features of R:

1. Statistical and Mathematical Tools

R offers built-in support for a wide range of statistical techniques, including:

• Regression
• Linear and nonlinear modeling
• Time-series analysis 
• Hypothesis testing 
• Clustering and classification 
• Multivariate statistics

2. Data Visualization Capabilities

R has the ability to produce high-quality data visualizations. Using R packages like ggplot2, lattice and plotly, users can create:

• Static, animated and interactive graphics
• Custom plots with detailed annotations
• Publication-ready visualizations

3. Data Handling and Transformation Capabilities

R’s core data structures — including vectors, matrices, lists and data frames — greatly support data handling, wrangling and transformation tasks. In particular, with packages like dplyr, tidyr and data.table, R enables efficient:

• Data manipulation
• Data reshaping and cleaning 
• Data aggregation and summarization

4. Extensive Package Ecosystem

R has a large ecosystem of packages available through CRAN (The Comprehensive R Archive Network) and has new packages constantly being developed by the community. This extensibility allows R users to:

• Implement specialized statistical methods
• Work with spatial, genomic and financial data
• Integrate with machine learning libraries 

The R Ecosystem

Beyond the core language, R thrives on a broad ecosystem of packages, platforms and contributors that extend its functionality and support a wide range of disciplines. 

Here’s some of key components that make up the R ecosystem:

CRAN (Comprehensive R Archive Network)

CRAN (The Comprehensive R Archive Network) is a network of web servers that serve as the main repository for R packages and the R language itself. It hosts over 20,000 R packages covering topics like statistics, machine learning, finance, epidemiology and more. CRAN requires packages to pass quality checks before inclusion, making it a trusted source for R tools.

Core R Package Collection

R offers curated package collections to streamline workflows, with some of the most common ones being:

• tidyverse: A set of data science packages with unified syntax, with key packages including ggplot2 (for data visualization), dplyr (data manipulation) and tidyr (data reshaping).
• Bioconductor: A package ecosystem for bioinformatics and computational biology, with packages for genomic data analysis, sequence alignment and sequence annotation.
• Shiny: An R package and web application framework for R that allows users to build interactive dashboards and tools directly from R code.

R Community and Open-Sourcing

R largely operates on community contributions and open-sourced tools from thousands of contributors worldwide. The R community develops packages, writes documentation and publishes tutorials to support the R language and user base.

R vs. Python for Data Science

When to Use R for Data Science

R is widely used in data science by statisticians and data miners for data analysis and the development of statistical software.

R libraries are capable of complex statistical work, including implementing regression models, spatial and time series analysis, classification and classical statistical tests, among others.

When to Use Python for Data Science

Python’s ease of use and simple syntax allows entry-level data scientists and developers to build solutions more quickly than they could with R.

Python is often preferred over R for data science when projects involve deep learning, web integration or deployment into production systems. Specifically, its libraries like TensorFlow, PyTorch and scikit-learn make it a strong choice for machine learning and AI tasks.

Is Programming in R Hard?

R may be difficult for beginners to grasp as a first programming language, but should pose no issue for experienced developers.

For many researchers and statisticians who don’t possess a programming background, however, learning the language can present a challenge. Beginners may find the syntax difficult to read and basic operations confusing. Functions like selecting, naming and renaming variables are known to be more difficult to accomplish in R than in other languages. Experienced developers should have enough context from working within more common languages to clear this hurdle without much difficulty.

How to Get Started With R

Here’s how to install R, choose an integrated development environment (IDE) and try some basic R code for yourself.

How to Install R

To start, download and install R from the Comprehensive R Archive Network (CRAN). Make sure to choose the version for your operating system (Windows, macOS or Linux) and follow the installation instructions.

Choosing an R IDE

While R can be run from a terminal, many users prefer working in an IDE for efficiency and additional features.

Two popular IDE options for R include:

• RStudio: A widely-used IDE for R, RStudio provides a user-friendly interface, built-in support for plots and version control.
• Jupyter Notebook: If you already use Python, Jupyter also lets you run R code using the IRKernel. This allows users to combine R with markdown and visual outputs into one notebook.

How to Code in R: The Basics

Once R is set up, here’s how to run basic code like performing vector mathematics and creating a plot:

Vector Math in R

x <- c(1, 2, 3, 4, 5)
y <- c(10, 20, 30, 40, 50)
z <- x + y
print(z) 

# Output: 11 22 33 44 55

Creating a Scatter Plot in R

x <- c(1, 2, 3, 4, 5)
y <- c(10, 20, 30, 40, 50)

# Create scatter plot
plot(x, y, main = "Simple Scatter Plot",
     xlab = "X Values", ylab = "Y Values",
     pch = 19, col = "blue")

History of R

R originated in the early 1990s as an open-source implementation of the S programming language, which was developed at Bell Laboratories in the 1970s. S was designed for statistical computing and graphics, introducing concepts that would later shape R’s functionality.

R was created by statisticians Ross Ihaka and Robert Gentleman at the University of Auckland in 1991, and was first publicly announced in 1993. Ihaka and Gentleman developed R with the intention to make a more accessible and extensible environment for data analysis. Due to its graphics capabilities and growing package ecosystem, the R project gained traction among academics and researchers, especially in statistics and bioinformatics fields. 

In 1997, the R Core team was formed to oversee R development, and the Comprehensive R Archive Network (CRAN) was established to host R and its expanding library of packages.

Over time, R has evolved into a major open-source and community-driven programming language for data science and related disciplines.

Frequently Asked Questions