Lozen Liu | Jan 06, 2023

R is an interpreted programming language designed for use in statistical computing and graphics. It is considered a different implementation of the S programming language developed by John Chambers and associates at Bell Laboratories. The language provides access to a variety of statistical and graphical techniques, and easily produces well-designed plots that include mathematical symbols and formulae.


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

R libraries are capable of complex statistical work, such as implementing linear and nonlinear modeling, spatial and time-series analysis, classification, classical statistical tests and more. 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. The volume of R packages and their ease of use is largely responsible for the widespread use of R in data science.

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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 largely 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. 

Even Python has begun facing competition in recent years, causing R to be phased out at an even greater rate. According to the TIOBE Index’s September 2021 ratings, Python ranks as the second most popular language with an 11.67 percent popularity ranking, while R is ranked 18th with a popularity score of just 0.98 percent.

R Programming Tutorial | Video:


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. R gained popularity in data science because it could run calculations without the use of a compiler, therefore making code more efficient. For many researchers and statisticians who don’t often 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.

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