R programming is a language and environment for statistical computing and graphics. This system is comprised of two parts: the R language itself and the run-time environment. R is an interpreted language, meaning users access its function through a command-line interpreter. Unlike other programming languages such as Python and Java, R is not a general-purpose programming language. It’s considered a domain-specific language(DSL), which means its function and use are designed for a specific area of use or domain. R is equipped with a large set of tasks that enable data visualizations, so users can analyze data, model required, and then create graphics. In addition to the language’s in-built graphical functions, numerous add-ons or modules facilitate.
R Programming for Data Science:
Here are several reasons why R remains one of the most popular programming languages is important in data science.
- Need of R Programming for Statisticians: Statisticians originally used R for doing statistical analysis, and it remains the programming choice of most today. R programming syntax makes it easy to create complex statistical models with just a few lines of code. Since many statisticians can use and contribute to R packages. If you aspire to work in academia- or if you’d just like to read academic papers and then be able to dig into the code behind them- having R programming skills can be a must.
- Top Tech Firms Demand for R Programming: Most companies hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. R is in at analysis and consulting firms, banks and other financial institutions, academic institutions, and research labs, and everywhere else data needs analyzing and visualizing.
- Data Science gets easier with R: Python may be one of the most beginner-friendly programming languages. Still, once you get past the syntax, R has a big advantage as it was designed specifically for data manipulation and analysis. Learning the core skills of data science- data manipulation, data visualization, and machine learning- can be easier in R once you’ve gotten through the fundamentals.
- Salary offered to R programmers: R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. These packages are part of the tidyverse, a growing collection of packages maintained by Rstudio, a certified B-corp that creates a free-touse R environment of the same name perfect for data work.
- Growth of Data Scientists and Statisticians using R: As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world. That means it’s easy to find answers to questions and community guidance as you work through projects in R.
- Extra skills to your skillset: Even if you are already a Python expert, only one language will be used for some jobs. Adding R to your toolbox will make some projects easier and it make the work more flexible and marketable for employees when you are looking for jobs in data science.
Conclusion:
According to recent surveys, the R programming language has gained significant popularity and is currently the 6th most popular language in 2024. If you want to learn this trending language, EIMR Business School offers an excellent opportunity. Our BCA degree program, specializing in Data science and Entrepreneurship, provides over 10 free certifications, including R programming language certifications. Additionally, you will acquire valuable entrepreneurship skills throughout the program. EIMR Business School is your gateway to mastering R and excelling in business.
