The statistical programming language R is specially designed to handle large amounts of data. With R, managing large volumes of data and creating visuals and visualizations suitable for publication becomes simple. Also, it simplifies all types of data mining, analysis, and modeling jobs.
R programming is remarkably well-suited to data science, an essential topic in today’s society because statisticians first built it for statistical purposes. Although statistics and graphics are R’s primary uses, it is also used for artificial intelligence (AI), machine learning, financial analysis, and other things. R remains active since the start of the 1990s and remains strong.
It is consistently recognized as one of the most commonly used coding languages in the world, in addition to being one that fetches high incomes. Keep reading to find out more about the R programming language, its statistical and programming features, its importance, and more.
R Programming Language: What is it exactly?
With almost everyone talking about R in the developer’s community, you must be asking, ‘What is R programming exactly?’ Well, R is a free coding language that developers use for data analysis and statistical software analysis. The syntax ‘>’ represents this programming language, and it comes with a prompt in its command-line interface.
Whether it’s macOS, Linux, or Windows, R programming is flexible enough to work with all of them. Whenever it comes to any graphical and statistical procedure, like linear regression or machine learning, programmers resort to the R programming language.
Key features of R Programming Language
Statistical features:
1. Basic Statistics: The mean, mode, and median are the three most frequently used words in basic statistics. They are all referred to collectively as “Measures of Central Tendency.” Therefore, measuring central tendency is relatively simple when using the R language.
2. Static Graphics: R has a wealth of tools for designing engaging static graphics. The list of plot types supported by R includes graphic maps, mosaic plots, biplots, and many more.
3. Probability Distributions: Probability distributions are crucial to statistics, and with R, we can easily handle a wide range of probability distribution types, including the binomial distribution, the normal distribution, the chi-squared distribution, and many others.
4. Data Analysis: It offers a sizable, well-organized, and comprehensive array of tools.
Programming features:
1. R Packages: One of the key characteristics of R is the abundance of available libraries. CRAN (Comprehensive R Archive Network), a repository with over 10,000 packages, is available for R.
2. Distributed Computing is a model where parts of a software system are distributed over several computers to increase productivity and performance. In November 2015, two new R packages, ddR and multiplier, which are used for distributed programming, were made available.
R Programming Language: why learn it?
Here are some benefits of R programming and reasons why you might want to consider studying it:
1. Programming in R is independent of operating systems. Your R application will run without a hitch on any operating system.
2. It’s pretty simple to learn and comprehend R. R programming will simplify your duties if you are knowledgeable about statistics.
3. R libraries offer one of the most influential and intelligent data visualizations.
4. Programmers always prefer using the R programming language for their machine learning and data science projects.
5. The need for R is expanding quickly and is now a trend in the sector.
6. You can get plenty of community support and a sizable user base in R to aid in your learning.
Nowadays, many programmers prefer R to Python. This is why:
1. R programming was created expressly with statisticians in mind, allowing even beginners to perform data analysis immediately.
2. In terms of statistical learning, R is more appropriate than Python.
3. Excellent libraries are available for data exploration and experimentation in R programming.
4. R has incredible visuals, making it ideal for data visualization.
R Programming Language Availability
The Comprehensive R Archive Network, or CRAN project, offers R as a command-line interface environment. However, using an IDE—of which there are many for R—will help you learn more quickly as a beginner.
1. IDE R Programming: The most well-known IDE to learn R programming is RStudio. Both desktop and business versions are offered.
2. StatET: An IDE for R programming and package creation built on the Eclipse platform. Supports a number of statistical software programs in addition to R, including S-Plus, SAS, Stata, and OpenBUGS/JAGS.
Is it difficult to learn R Programming Language?
R could be challenging for beginners to understand as a first programming language, but it shouldn’t be a problem for seasoned coders. Early in the 1990s, R was first used, and it was created primarily to enable arithmetic calculations and data analysis. Since R can perform computations without the aid of a compiler, it has grown in favor among data scientists. This makes code more effective.
However, understanding the language might be difficult for many statisticians and researchers who frequently lack programming experience. Beginners could find the syntax tough to read and the fundamental procedures perplexing. R is known to be more challenging to use than other languages for operations like choosing, naming, and renaming variables.
Experienced developers must be able to overcome this obstacle without too much difficulty because they have adequate context from working with more popular languages.
Conclusion
Conclusively, R is a powerful, robust, flexible, and extensible programming language that programmers use for a range of industries, including finance, data science, etc. Given that R programming is a free and fully open-source programming language, users can benefit from its vast selection of packages to accomplish a range of jobs.
Additionally, there is a worldwide community of R enthusiasts that is supportive. R skills can help you advance in your data career and find a well-paying position in data science. So, choose the most complete R courses available online and start today.
