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, which was developed by John Chambers and associates at Bell Laboratories, as much of the code written for S can 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.
When Should You Use R?
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.
Is R Used Anymore?
R still remains a popular language in data science, though Python has overtaken it as a favorite in the field. The reason for the shift is mostly due to Python’s low barrier to entry, simple syntax and relaxed code, allowing entry-level data scientists and developers to build solutions more quickly than they could with R.
According to the TIOBE Index’s ratings as of November 2024, Python ranks as the most popular programming language, while R is ranked 18th with a popularity score of 1 percent.
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.
R was first implemented in the early 1990s and built specifically for supporting mathematical calculations and data analysis. It gained popularity in data science because it could run calculations without the use of a compiler, making code more efficient.
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.
Frequently Asked Questions
What is R programming used for?
The R programming language is largely used in data science for statistical computing tasks and creating statistical graphics/data visualizations. R can be used for linear and nonlinear modeling, time series analysis, parametric and nonparametric tests, clustering and more statistical analyses.
Is R similar to Python?
Both R and Python are open-source and used for data science applications, though they are different in purpose and functionality. R is mainly built for statistical analysis, while Python is designed as a general-purpose programming language.
Is R difficult to learn?
R is considered difficult to learn due to its syntax being different from other popular programming languages, having a large number of commands and having a complex variable naming/selecting system.
